This rule follows from Rule 1: Reality is always more complicated. Put another way, there are always edge cases. Often edge cases between principles (personal or otherwise) get resolved via a principle priority stack, an implicit or explicit hierarchy of principles. An example is Isaac Asimov’s Three Laws of Robotics:

First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Asimov spent much of his robot fiction dissecting situations where these “Laws” explicitly conflict or otherwise get subverted by edge cases. While these are make-believe situations, the priority stack mental model explains a lot of otherwise seemingly irrational behavior in the real world.

For example, why do politicians and political parties seem to flip-flop on supposedly strongly held principles? I do not think in all cases it is because they actually don’t have strongly held principles, but that there is something higher up in their principle priority stack, usually winning elections.

The same is true with corporations, especially public ones. Most have profits (or a similar financial metric) at the top of their principle priority stack, above other customer-focused principles like sound privacy practices, good service, etc. Some corporations have it the other way around, like DuckDuckGo where we’ve forgone a lot of profits because privacy is higher on our principle priority stack, or some B corps with other elevated non-financial priorities.

The priority stack model itself has edge cases! A given priority stack holds under normal conditions, but extreme conditions can reshuffle the ranking. For example, politicians do occasionally reach a breaking point where a usually lower principle (to winning elections) is about to be violated badly enough that they temporarily elevate it to the top, accepting re-election risk as a result. It’s rare, but it does happen.

What can you practically do with this Rule? I think at least two things. First, when thinking about principles, either your own or others’, you can ask how they arrange in a priority stack, especially relative to a given situation you are facing or are concerned with facing in the future. This arrangement can help clarify what you or others would actually do.

Second, if you’d like to convey that you care about a particular principle, I think it helps to publicly signal it in a priority stack context, at least against one other perceived high priority. For example, I'd love to vote for politicians who name a few principles they hold higher than re-election, such that they publicly commit to voting for those principles regardless of future electoral consequences. You can't fully predict or trust the future, but a stated priority stack is more trustworthy than silence, and more trustworthy still when there's a track record behind it.

The Dark Knight (2008).

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

There are many proposals that would pair a so-called “token tax” on AI usage with support for displaced workers. Every one I've seen, though, specifies disbursement mechanisms up front, such as direct payments based on specific triggers. But what shape that support should optimally take remains genuinely uncertain: the peak of displacement hasn't been reached, and we don't yet know which industries will be hit hardest, or on what timelines. Instead of trying to work any of this out up front, we can decouple the collection of the tax from how funds would be disbursed.

We should start collecting an AI token tax now, and figure out exactly what to do with the funds later, holding them in a true lockbox outside general appropriations, with statutory protection limiting use of funds to supporting displaced workers in the future. We’d (at DuckDuckGo) be willing to support bills to this effect and ultimately pay a token tax, presumably collected by the leading AI companies on a usage basis, for example a 10% surcharge on token charges. That amount would roughly match the 10% employers pay in payroll taxes, which also further reduces the incentive to replace human workers with AI workers.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

This rule, that you can make a compelling narrative for almost anything untrue, has profound negative consequences for democratic societies. I believe that’s because:

* What makes a narrative compelling isn’t its truth, but that it sounds true. For example, simpler things often sound truer (making them more compelling), while actual truth is often nuanced, and increasingly so in our ever-more-complicated world.

* People are generally either already committed to a political tribe, or are deciding on a more case-by-case basis based on narrative. If they’re in the first camp, the tribe can concoct a compelling narrative to keep them satisfied no matter the issue. If they’re in the second camp, what they end up believing won’t necessarily be correlated to the best policy outcomes (truth) per the first proposition, as they are more likely to just choose the narrative that sounds most compelling to them.

* Our current state of social media amplification seems to have only made this dynamic worse. More news and political argument consumption has moved from longer-form content with robust journalistic standards to shorter-form video clips with lesser standards. There were always narratives in either form, but the former tries harder to correlate with the truth, and so, on net, as a society, we have less exposure to truth-correlated narratives.

Building on this, we primarily communicate with each other, and certainly in the political sphere, via narrative. Politicians are elected on sound bites and video clips, and are therefore incentivized to sell the public crisp policy stories that sound compelling, regardless of whether those policies are our best probabilistic shot at achieving the intended policy outcomes. The politicians may even believe their stories, and that perhaps makes them even more persuasive, but that doesn’t make the stories any more true. It’s not that the stories need to be actually against the policy outcomes; they may just not be pushing the best possible policies. In aggregate, this means we’re always enacting sub-optimal policies, and over the long-run are less prosperous than we would otherwise be.

The antidote for this Rule, and more generally the best way we know how to align with desired real world outcomes, is through the scientific method, namely tight, iterative loops of hypothesis → experiment → analysis (and back to hypothesis). Narratives are of course central to this process as well! People usually create stories to come up with hypotheses, and that’s fine as long as eventually the experimental results catch up with the stories and to the extent the stories don’t fit the results, they get replaced by new ones that do. The tighter the loops, the faster this happens.

The same is of course true in policy land on a long enough time horizon. Ultimately it becomes so obvious a policy isn’t working that it gets thrown out (or the government collapses). So what’s the difference? I think it is just a matter of how tight the feedback loops really are, of how quickly these loops are happening and how much analysis is really informing the next set of hypotheses. In government, at least in the U.S., it can take literally decades to revise a policy, whether that is throwing out a bad one or just tweaking a mediocre one based on real-world outcomes to make it better. That’s obviously too slow to achieve optimal results. The two-party system makes this worse, but it isn’t even good within one party, so it seems more like a structural problem: compelling narratives insulate sub-optimal policies from needed scrutiny, which, among other things, slows the feedback loops down too much. That takes us all the way back to Rule 4: You can make a compelling narrative for almost anything untrue.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Thank You For Smoking (2005)

For stuck issues like these, we should have a citizen-led path to amend the Constitution as a solution to this governance problem. Now I fully agree that it should be super hard to amend the Constitution, and so we could make this path just as hard as the Congress-led one.

Thanks for reading Gabriel Weinberg! Subscribe for free to receive new posts and support my work.

The current Congress-led path is two-thirds of both houses need to propose an amendment and then three-fourths of state legislatures need to ratify it. A citizen-led path could similarly require two-thirds of states to propose an amendment (via something like signature campaigns within those states) and then a nationwide three-fourths vote to ratify it. In other words the thresholds could be similar, just put directly in the hands of the people. You would still need most red and blue states working together to pass an amendment, such that nothing could pass without super-majority bipartisan support. That is, a super high bar.

There are a lot of interesting structural reform proposals out there, but most require new Constitutional amendments to have lasting staying power. To really unlock the possibilities for those, we need a citizen-led amendment path in place first. Of course, we would need an amendment using the old way to get this new way in place.

Congress won’t reform itself, but a citizen-led amendment path could.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

* Start with the official World Cup tournament schedule (including yet-to-be played playoff matches)

* Blend Elo rankings with FIFA rankings (50/50)

* Use the Elo formulas to probabilistically predict winners (assuming no draws, even in group stage)

* Run one million individual simulations of the full tournament (it reaches diminishing returns around 50K, but hey, why not!)

* Run again with a home field advantage boost (+180 Elo) for the U.S., Canada, and Mexico based on prior World Cup outcomes

* Count up who participated in each match

Some interesting findings (at least to me as a U.S. fan) are below, followed by a rundown for every match (in reverse order).

Big Disclaimer 1: The above is of course a gross simplification of the actual tournament. For example, it doesn’t take into account team matchup histories, game models, etc. etc. I do think, however, it is useful enough for the designed purpose of generally predicting likely match participants.

Big Disclaimer 2: I did a lot of output validation so I think the results are largely accurate (to the extent they can be given Big Disclaimer 1). However, I didn’t write or review every line of code, so it is likely there are still some bugs in there. If you think you see anything that seems off, let me know and I’ll try to track it down (and update anything if necessary).

Aside on Claude code: Like many others, I found this process both productive and frustrating. It was definitely faster than I could have done it alone, but Claude kept forgetting basic context, and was way overconfident in the accuracy of the results. That is, many rounds of validation at every stage of output was absolutely necessary despite Claude saying things were good. I couldn’t trust its word at all.

HA+ = with home field advantage (anytime this comes into play there is a + next to the team name)

HA- = without home field advantage

Most Likely Finals

# HA+ HA- Matchup

1 12.4% 13.3% Argentina v Spain

2 5.4% 5.6% Argentina v France

3 4.8% 6.4% England v Spain

4 3.3% 3.4% Brazil v Spain

5 2.8% 3.2% Portugal v Spain

6 2.5% 2.7% France v Spain

7 2.4% 0.2% Argentina v United States

8 2.3% 3.0% England v France

9 1.9% 0.7% Mexico v Spain

10 1.8% 1.9% Argentina v Netherlands

11 1.7% 1.7% Brazil v France

12 1.6% 0.2% Spain v United States

13 1.4% 1.7% Colombia v Spain

14 1.3% 1.5% France v Portugal

15 1.2% 1.4% Argentina v England

16 1.1% 1.2% Netherlands v Spain

17 1.1% 1.2% Spain v Uruguay

18 1.0% 0.1% England v United States

19 1.0% 0.4% France v Mexico

20 0.9% 1.0% Argentina v Germany

Championship Probabilities

# HA+ HA- FIFA ELO Blend Team

1 29.2% 30.6% 1 1 1 Spain

2 19.0% 19.2% 2 2 2 Argentina

3 11.9% 12.2% 3 3 3 France

4 7.4% 9.1% 4 4 4 England

5 4.7% 4.6% 5 6 5 Brazil

6 4.7% 0.3% 15 31 23 United States+

7 3.7% 4.1% 6 7 6 Portugal

8 3.4% 3.4% 7 8 7 Netherlands

9 2.2% 2.5% 13 5 8 Colombia

10 1.7% 1.8% 10 12 10 Germany

11 1.5% 1.8% 11 10 9 Croatia

12 1.5% 0.7% 16 20 19 Mexico

13 1.1% 1.3% 9 21 11 Belgium

14 0.9% 0.9% 8 24 13 Morocco

15 0.9% 1.0% 18 13 14 Switzerland

16 0.9% 0.9% 14 17 16 Senegal

17 0.8% 0.9% 17 14 15 Uruguay

18 0.8% 0.8% 23 9 17 Ecuador

19 0.7% 0.7% 12 19 12 Italy

20 0.7% 0.7% 19 16 18 Japan

Here’s a visualization of the above made by a reader (thanks!)

United States: Tournament Progression

HA+ HA- Round

97.9% 88.8% Round of 32

69.1% 39.5% Round of 16

42.7% 14.4% Quarter-Finals

18.7% 3.8% Semi-Finals

10.2% 1.1% Final

4.7% 0.3% Champion

United States: Most Likely Opponents by Round

Round of 32:

# HA+ HA- Opponent

1 12.7% 16.1% Iran

2 12.6% 5.0% Switzerland

3 11.8% 13.7% Canada

4 9.3% 3.8% Italy

5 5.6% 3.3% Ukraine

6 5.5% 7.0% Egypt

7 5.4% 15.6% France

8 5.3% 6.8% Belgium

9 5.3% 4.3% Norway

10 5.1% 3.1% Japan

11 4.8% 2.7% Wales

12 4.7% 2.8% Poland

13 3.3% 4.2% Senegal

14 2.3% 1.4% Netherlands

15 1.4% 4.5% Portugal

Round of 16:

# HA+ HA- Opponent

1 28.7% 20.4% Belgium

2 17.8% 24.6% Argentina

3 9.4% 6.9% Algeria

4 8.3% 6.3% Iran

5 6.8% 4.9% Norway

6 6.7% 5.2% Austria

7 6.0% 7.8% Uruguay

8 3.9% 2.8% Senegal

9 2.1% 1.8% Egypt

10 2.1% 2.9% Spain

11 1.8% 4.4% Germany

12 1.3% 1.8% Korea Republic

13 1.1% 0.8% France

14 0.9% 2.2% Ecuador

15 0.6% 0.9% Canada

Quarter-Finals:

# HA+ HA- Opponent

1 45.4% 35.5% Spain

2 9.5% 10.2% Portugal

3 9.4% 8.8% Colombia

4 7.4% 6.1% Croatia

5 5.4% 4.3% England

6 3.5% 5.8% Argentina

7 3.1% 1.2% Canada

8 2.5% 2.6% Uruguay

9 2.5% 2.1% Austria

10 1.8% 3.9% Switzerland

11 1.7% 2.9% Italy

12 1.4% 3.8% Netherlands

13 1.1% 1.2% Panama

14 0.9% 0.9% Algeria

15 0.6% 1.7% Morocco

Semi-Finals:

# HA+ HA- Opponent

1 24.9% 18.4% France

2 11.2% 8.2% Netherlands

3 8.4% 8.8% Brazil

4 7.2% 5.8% Germany

5 5.8% 10.5% England

6 5.1% 3.3% Mexico

7 4.9% 4.0% Morocco

8 3.6% 3.5% Ecuador

9 3.4% 2.1% Switzerland

10 3.3% 9.4% Spain

11 3.1% 3.6% Senegal

12 2.9% 2.1% Denmark

13 2.8% 2.9% Japan

14 2.6% 1.8% Korea Republic

15 1.7% 1.0% Italy

Final:

# HA+ HA- Opponent

1 23.1% 18.8% Argentina

2 15.4% 20.4% Spain

3 9.7% 10.7% England

4 7.9% 9.9% France

5 6.9% 5.8% Brazil

6 5.7% 5.3% Portugal

7 3.6% 1.4% Mexico

8 3.1% 2.7% Colombia

9 3.0% 3.5% Netherlands

10 2.1% 2.2% Croatia

11 2.0% 2.0% Germany

12 1.8% 1.6% Uruguay

13 1.7% 1.5% Senegal

14 1.6% 2.1% Belgium

15 1.5% 1.6% Ecuador

M104 - Final (Winners of M101 & M102)

Jul 19, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 12.4% 13.3% Argentina v Spain

2 5.4% 5.6% Argentina v France

3 4.8% 6.4% England v Spain

4 3.3% 3.4% Brazil v Spain

5 2.8% 3.2% Portugal v Spain

6 2.5% 2.7% France v Spain

7 2.4% 0.2% Argentina v United States+

8 2.3% 3.0% England v France

9 1.9% 0.7% Mexico v Spain

10 1.8% 1.9% Argentina v Netherlands

11 1.7% 1.7% Brazil v France

12 1.6% 0.2% Spain v United States+

13 1.4% 1.7% Colombia v Spain

14 1.3% 1.5% France v Portugal

15 1.2% 1.4% Argentina v England

16 1.1% 1.2% Netherlands v Spain

17 1.1% 1.2% Spain v Uruguay

18 1.0% 0.1% England v United States+

19 1.0% 0.4% France v Mexico

20 0.9% 1.0% Argentina v Germany

# HA+ HA- Team

1 41.0% 43.2% Spain

2 32.4% 32.9% Argentina

3 21.1% 21.8% France

4 14.8% 18.3% England

5 10.8% 10.6% Brazil

6 10.2% 1.1% United States+

7 9.0% 9.8% Portugal

8 7.9% 8.2% Netherlands

9 5.8% 6.5% Colombia

10 5.5% 2.6% Mexico

11 4.7% 4.9% Germany

12 4.2% 5.1% Croatia

13 3.3% 3.9% Belgium

14 3.0% 3.2% Senegal

15 2.9% 3.1% Uruguay

16 2.9% 3.3% Switzerland

17 2.9% 3.0% Morocco

18 2.7% 2.8% Ecuador

19 2.3% 2.4% Japan

20 2.2% 2.4% Italy

M103 - Third Place (Losers of M101 & M102)

Jul 18, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 2.0% 2.2% Argentina v France

2 1.9% 0.7% France v Mexico

3 1.8% 1.8% Brazil v France

4 1.7% 2.1% England v France

5 1.5% 1.7% Argentina v Spain

6 1.2% 0.4% Mexico v Spain

7 1.2% 1.6% England v Spain

8 1.2% 1.3% Argentina v Netherlands

9 1.1% 1.2% Brazil v Spain

10 1.1% 1.0% Brazil v Netherlands

11 1.1% 1.2% France v Portugal

12 1.0% 0.4% Mexico v Netherlands

13 1.0% 1.0% France v Senegal

14 0.9% 1.2% England v Netherlands

15 0.8% 0.2% Argentina v United States+

16 0.8% 0.8% Argentina v Germany

17 0.8% 0.9% Portugal v Spain

18 0.7% 0.7% France v Germany

19 0.7% 0.7% France v Netherlands

20 0.7% 0.7% Ecuador v France

# HA+ HA- Team

1 19.4% 19.4% France

2 13.2% 14.0% Argentina

3 12.8% 12.6% Brazil

4 12.7% 13.4% Spain

5 11.7% 5.8% Mexico

6 11.7% 14.5% England

7 11.6% 11.5% Netherlands

8 8.7% 8.8% Germany

9 8.5% 2.7% United States+

10 7.9% 9.1% Portugal

11 6.4% 6.6% Morocco

12 6.1% 6.3% Senegal

13 6.0% 7.1% Colombia

14 6.0% 6.1% Ecuador

15 5.7% 6.1% Switzerland

16 5.4% 6.8% Croatia

17 5.0% 5.2% Japan

18 4.6% 5.6% Belgium

19 4.5% 2.7% Canada

20 4.4% 4.8% Uruguay

M102 - Semi-Final (W99 v W100)

Jul 15, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 8.9% 11.7% Argentina v England

2 7.7% 7.7% Argentina v Brazil

3 6.7% 2.2% Argentina v Mexico

4 2.7% 3.8% England v Portugal

5 2.7% 2.7% Argentina v France

6 2.6% 2.7% Argentina v Senegal

7 2.3% 2.4% Argentina v Ecuador

8 2.3% 2.4% Brazil v Portugal

9 1.9% 0.7% Mexico v Portugal

10 1.8% 1.9% Argentina v Japan

11 1.8% 2.0% Argentina v Germany

12 1.6% 1.7% Argentina v Netherlands

13 1.5% 2.1% Argentina v Croatia

14 1.4% 1.6% Argentina v Morocco

15 1.2% 1.8% Colombia v England

16 1.2% 1.5% England v Spain

17 1.1% 1.5% England v Uruguay

18 1.1% 1.2% Argentina v Norway

19 1.1% 1.2% Brazil v Colombia

20 1.0% 0.4% England v United States+

# HA+ HA- Team

1 42.6% 43.7% Argentina

2 22.1% 27.9% England

3 18.3% 17.8% Brazil

4 15.6% 5.0% Mexico

5 13.3% 14.9% Portugal

6 6.8% 7.9% Colombia

7 6.4% 6.4% France

8 6.1% 6.3% Senegal

9 5.7% 6.1% Uruguay

10 5.7% 5.9% Spain

11 5.4% 5.5% Ecuador

12 4.9% 6.5% Croatia

13 4.9% 1.4% United States+

14 4.6% 4.7% Japan

15 4.3% 4.4% Germany

16 4.1% 4.2% Netherlands

17 4.1% 0.6% Canada

18 3.3% 3.6% Morocco

19 2.8% 3.7% Italy

20 2.7% 3.0% Norway

M101 - Semi-Final (W97 v W98)

Jul 14, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 16.1% 17.3% France v Spain

2 7.1% 7.6% Netherlands v Spain

3 4.4% 4.7% Germany v Spain

4 4.3% 0.6% France v United States+

5 2.8% 3.0% Morocco v Spain

6 2.6% 2.7% Brazil v Spain

7 2.4% 2.2% Spain v Switzerland

8 2.0% 1.7% Denmark v Spain

9 1.9% 0.3% Netherlands v United States+

10 1.8% 2.3% Belgium v France

11 1.7% 1.7% Korea Republic v Spain

12 1.7% 1.9% Colombia v France

13 1.6% 1.9% Croatia v France

14 1.5% 1.7% Ecuador v Spain

15 1.5% 1.7% England v France

16 1.2% 1.4% France v Portugal

17 1.2% 1.1% Italy v Spain

18 1.1% 0.2% Germany v United States+

19 1.1% 1.2% Japan v Spain

20 1.0% 1.1% Argentina v France

# HA+ HA- Team

1 48.0% 50.7% Spain

2 34.1% 34.7% France

3 15.4% 15.5% Netherlands

4 13.8% 2.4% United States+

5 9.1% 9.3% Germany

6 6.0% 5.4% Switzerland

7 6.0% 6.0% Morocco

8 5.4% 5.4% Brazil

9 5.3% 6.8% Belgium

10 5.0% 5.7% Colombia

11 4.7% 5.4% Croatia

12 4.4% 4.9% England

13 4.2% 3.5% Denmark

14 3.7% 3.3% Korea Republic

15 3.6% 4.0% Portugal

16 3.3% 2.9% Italy

17 3.3% 3.3% Ecuador

18 3.0% 3.2% Argentina

19 3.0% 3.2% Senegal

20 2.8% 2.9% Japan

M100 - Quarter-Final (W95 v W96)

Jul 11, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 15.6% 17.7% Argentina v Portugal

2 9.5% 1.5% Argentina v Canada

3 8.0% 9.3% Argentina v Colombia

4 4.6% 6.4% Argentina v Italy

5 4.4% 6.9% Argentina v Switzerland

6 3.2% 3.7% Portugal v Uruguay

7 2.1% 1.0% Portugal v United States+

8 2.1% 2.4% Argentina v Croatia

9 2.0% 0.3% Canada v Uruguay

10 1.9% 2.2% Portugal v Spain

11 1.8% 2.1% Argentina v Panama

12 1.6% 1.9% Colombia v Uruguay

13 1.5% 1.8% Iran v Portugal

14 1.3% 1.6% Argentina v Iran

15 1.3% 0.1% Canada v United States+

16 1.2% 0.2% Canada v Spain

17 1.1% 1.3% Argentina v Belgium

18 1.1% 1.3% Argentina v England

19 1.1% 1.3% Belgium v Portugal

20 1.1% 0.5% Colombia v United States+

# HA+ HA- Team

1 55.3% 57.7% Argentina

2 28.3% 30.9% Portugal

3 17.3% 2.6% Canada

4 14.6% 16.1% Colombia

5 11.2% 12.1% Uruguay

6 8.4% 11.2% Italy

7 8.0% 12.1% Switzerland

8 7.8% 4.0% United States+

9 7.5% 8.7% Iran

10 6.8% 7.1% Spain

11 5.8% 6.5% Belgium

12 4.0% 3.7% Australia

13 3.8% 4.2% Croatia

14 3.3% 3.7% Panama

15 3.0% 2.2% Turkey

16 2.9% 3.4% Egypt

17 2.7% 2.9% Austria

18 2.4% 2.7% Paraguay

19 2.1% 2.2% England

20 1.8% 2.1% Algeria

M99 - Quarter-Final (W91 v W92)

Jul 11, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 11.6% 3.7% Brazil v Mexico

2 9.8% 12.4% Brazil v England

3 4.6% 1.5% Mexico v Senegal

4 4.3% 1.4% Ecuador v Mexico

5 4.1% 5.2% England v Senegal

6 3.6% 1.2% Japan v Mexico

7 3.6% 4.6% Ecuador v England

8 3.2% 1.0% France v Mexico

9 3.1% 4.0% England v Japan

10 2.8% 0.9% Germany v Mexico

11 2.7% 3.5% England v France

12 2.4% 3.1% England v Germany

13 2.4% 0.8% Mexico v Netherlands

14 2.3% 0.7% Mexico v Morocco

15 2.1% 3.0% Brazil v Croatia

16 2.0% 2.6% England v Netherlands

17 2.0% 2.5% England v Morocco

18 1.7% 0.5% Mexico v Norway

19 1.4% 1.9% England v Norway

20 0.9% 1.2% Croatia v Senegal

# HA+ HA- Team

1 37.8% 12.2% Mexico

2 32.2% 41.0% England

3 31.3% 31.7% Brazil

4 14.1% 15.0% Senegal

5 12.6% 13.4% Ecuador

6 10.4% 10.9% Japan

7 9.4% 9.7% France

8 8.8% 9.4% Germany

9 7.7% 8.6% Morocco

10 7.1% 8.3% Norway

11 7.0% 9.8% Croatia

12 7.0% 7.3% Netherlands

13 2.4% 3.8% Scotland

14 1.9% 4.0% Denmark

15 1.9% 2.6% Colombia

16 1.7% 2.3% Portugal

17 1.4% 3.0% Korea Republic

18 1.4% 1.8% Côte d’Ivoire

19 1.3% 1.6% Ukraine

20 0.8% 1.2% Uruguay

M98 - Quarter-Final (W93 v W94)

Jul 10, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 19.3% 5.1% Spain v United States+

2 11.6% 14.7% Belgium v Spain

3 3.0% 4.1% Norway v Spain

4 3.0% 4.8% Spain v Turkey

5 2.9% 0.8% Colombia v United States+

6 2.9% 0.7% Croatia v United States+

7 2.6% 2.3% Spain v Switzerland

8 2.5% 3.3% Iran v Spain

9 2.3% 3.1% Algeria v Spain

10 2.1% 0.6% England v United States+

11 2.0% 2.6% Senegal v Spain

12 2.0% 0.5% Portugal v United States+

13 1.9% 1.7% Italy v Spain

14 1.9% 2.4% Austria v Spain

15 1.8% 2.2% Belgium v Colombia

16 1.7% 2.2% Belgium v Croatia

17 1.4% 3.7% Canada v Spain

18 1.3% 1.6% Belgium v England

19 1.2% 1.5% Belgium v Portugal

20 1.2% 0.3% Argentina v United States+

# HA+ HA- Team

1 58.8% 58.8% Spain

2 33.0% 8.6% United States+

3 19.7% 25.1% Belgium

4 8.8% 8.8% Colombia

5 8.8% 8.8% Croatia

6 6.5% 6.5% England

7 6.0% 7.0% Austria

8 5.9% 5.9% Portugal

9 5.2% 8.2% Turkey

10 5.1% 6.8% Norway

11 4.9% 6.3% Algeria

12 4.4% 3.9% Switzerland

13 4.3% 5.7% Iran

14 4.2% 4.2% Argentina

15 3.7% 3.8% Uruguay

16 3.4% 4.3% Senegal

17 3.2% 3.0% Italy

18 2.3% 6.4% Canada

19 1.7% 2.3% Japan

20 1.6% 3.6% Australia

M97 - Quarter-Final (W89 v W90)

Jul 9, Boston (Gillette Stadium)

# HA+ HA- Teams

1 13.8% 14.0% France v Netherlands

2 6.4% 6.5% France v Morocco

3 5.1% 4.8% France v Switzerland

4 5.1% 5.1% Germany v Netherlands

5 4.3% 4.4% Brazil v France

6 3.7% 3.3% Denmark v France

7 2.9% 2.9% France v Japan

8 2.9% 2.7% France v Korea Republic

9 2.3% 2.3% Germany v Morocco

10 2.3% 2.1% France v Italy

11 2.2% 2.3% Canada v France

12 2.0% 2.0% Ecuador v Netherlands

13 1.8% 1.8% Germany v Switzerland

14 1.6% 2.6% France v Mexico

15 1.6% 1.6% Brazil v Germany

16 1.4% 1.2% Denmark v Germany

17 1.3% 1.2% Korea Republic v Netherlands

18 1.3% 1.3% Netherlands v Senegal

19 1.1% 0.8% Denmark v Korea Republic

20 1.1% 1.0% Germany v Korea Republic

# HA+ HA- Team

1 46.9% 47.6% France

2 29.5% 29.6% Netherlands

3 17.1% 17.3% Germany

4 14.8% 14.9% Morocco

5 12.2% 10.9% Switzerland

6 10.8% 9.8% Korea Republic

7 10.8% 9.1% Denmark

8 10.0% 9.9% Brazil

9 6.9% 7.0% Ecuador

10 6.3% 6.2% Japan

11 6.0% 5.1% Italy

12 5.5% 6.0% Canada

13 4.4% 4.5% Senegal

14 4.1% 8.1% Mexico

15 3.6% 3.6% Scotland

16 2.1% 1.6% Australia

17 1.9% 1.8% United States+

18 1.8% 1.8% Norway

19 1.5% 1.3% Paraguay

20 1.5% 1.3% Turkey

M96 - Round of 16 (W85 v W87)

Jul 7, Vancouver (BC Place)

# HA+ HA- Teams

1 13.9% 3.7% Canada+ v Portugal

2 8.5% 11.4% Italy v Portugal

3 8.3% 12.5% Portugal v Switzerland

4 7.7% 2.0% Canada+ v Colombia

5 4.7% 6.3% Colombia v Italy

6 4.6% 6.9% Colombia v Switzerland

7 3.6% 4.4% Egypt v Portugal

8 3.4% 0.9% Canada+ v Panama

9 3.1% 3.7% Iran v Portugal

10 2.1% 0.6% Canada+ v Croatia

11 2.1% 2.5% Colombia v Egypt

12 2.0% 3.0% Panama v Switzerland

13 2.0% 2.2% Belgium v Portugal

14 1.9% 2.5% Italy v Panama

15 1.7% 2.0% Colombia v Iran

16 1.5% 1.8% Algeria v Portugal

17 1.3% 1.7% Croatia v Italy

18 1.2% 1.9% Croatia v Switzerland

19 1.1% 1.3% Belgium v Colombia

20 1.0% 1.3% Austria v Portugal

# HA+ HA- Team

1 45.0% 45.2% Portugal

2 30.8% 8.1% Canada+

3 24.9% 25.0% Colombia

4 19.0% 25.3% Italy

5 18.3% 27.6% Switzerland

6 10.7% 10.4% Panama

7 7.9% 9.7% Egypt

8 6.9% 8.3% Iran

9 6.8% 6.8% Croatia

10 4.3% 5.0% Belgium

11 4.1% 4.7% Algeria

12 3.0% 3.4% Austria

13 2.9% 2.9% England

14 2.4% 2.0% Australia

15 1.9% 2.4% Japan

16 1.9% 1.7% Paraguay

17 1.5% 1.9% Ukraine

18 1.4% 1.2% Turkey

19 1.2% 2.0% United States

20 1.2% 1.4% Netherlands

M95 - Round of 16 (W86 v W88)

Jul 7, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 16.4% 18.7% Argentina v Iran

2 11.8% 9.5% Argentina v United States+

3 10.5% 9.8% Argentina v Australia

4 8.7% 9.6% Argentina v Belgium

5 6.7% 7.7% Argentina v Paraguay

6 6.6% 4.2% Argentina v Turkey

7 5.2% 5.9% Argentina v Egypt

8 4.9% 5.5% Iran v Uruguay

9 3.5% 2.9% United States+ v Uruguay

10 3.2% 2.9% Australia v Uruguay

11 2.6% 2.9% Belgium v Uruguay

12 2.0% 2.3% Paraguay v Uruguay

13 1.9% 1.2% Turkey v Uruguay

14 1.9% 2.2% Iran v Spain

15 1.5% 1.8% Egypt v Uruguay

16 1.4% 1.1% Spain v United States+

17 1.2% 1.2% Australia v Spain

18 1.0% 1.1% Belgium v Spain

19 0.8% 0.9% Austria v Iran

20 0.8% 1.0% Argentina v Slovakia

# HA+ HA- Team

1 67.0% 67.0% Argentina

2 24.6% 27.9% Iran

3 19.9% 19.9% Uruguay

4 17.6% 14.3% United States+

5 15.7% 14.7% Australia

6 13.0% 14.4% Belgium

7 9.9% 11.5% Paraguay

8 9.8% 6.3% Turkey

9 7.8% 7.9% Spain

10 7.7% 8.9% Egypt

11 3.3% 3.3% Austria

12 1.4% 1.4% Algeria

13 1.2% 1.5% Slovakia

14 0.5% 0.5% Saudi Arabia

15 0.3% 0.4% Romania

16 0.1% 0.1% New Zealand

17 0.1% 0.1% Cabo Verde

18 0.0% 0.0% Kosovo

19 0.0% 0.0% Jordan

M94 - Round of 16 (W81 v W82)

Jul 6, Seattle (Lumen Field)

# HA+ HA- Teams

1 19.8% 7.9% Belgium v United States+

2 6.2% 2.4% Algeria v United States+

3 5.7% 2.3% Iran v United States+

4 4.7% 1.9% Norway v United States+

5 4.3% 6.9% Belgium v Turkey

6 4.0% 1.6% Austria v United States+

7 3.3% 3.0% Belgium v Switzerland

8 2.7% 1.1% Senegal v United States+

9 2.5% 2.2% Belgium v Italy

10 2.3% 6.3% Belgium v Canada

11 1.6% 3.7% Australia v Belgium

12 1.4% 0.6% Egypt v United States+

13 1.4% 2.1% Algeria v Turkey

14 1.3% 1.8% Belgium v Japan

15 1.2% 2.0% Iran v Turkey

16 1.1% 1.5% Belgium v Norway

17 1.0% 1.7% Norway v Turkey

18 1.0% 1.5% Belgium v Ukraine

19 1.0% 0.9% Algeria v Switzerland

20 0.9% 0.9% Iran v Switzerland

# HA+ HA- Team

1 47.4% 19.0% United States+

2 41.7% 41.7% Belgium

3 13.2% 13.1% Algeria

4 12.4% 13.5% Norway

5 11.9% 11.8% Iran

6 10.4% 16.6% Turkey

7 8.5% 8.4% Austria

8 8.0% 7.1% Switzerland

9 7.1% 7.5% Senegal

10 5.9% 5.3% Italy

11 5.5% 15.1% Canada

12 3.8% 8.9% Australia

13 3.2% 4.3% Japan

14 3.0% 2.9% Egypt

15 2.5% 3.5% Ukraine

16 2.1% 4.7% Paraguay

17 2.0% 2.1% France

18 1.9% 2.4% Netherlands

19 1.8% 2.6% Poland

20 1.7% 2.4% Wales

M93 - Round of 16 (W83 v W84)

Jul 6, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 23.1% 23.1% Croatia v Spain

2 21.5% 21.5% Colombia v Spain

3 13.0% 13.0% Portugal v Spain

4 11.8% 11.8% England v Spain

5 3.6% 3.6% Panama v Spain

6 2.7% 2.7% Austria v Croatia

7 2.5% 2.5% Austria v Colombia

8 2.3% 2.3% Croatia v Uruguay

9 2.1% 2.2% Colombia v Uruguay

10 1.5% 1.5% Spain v Uzbekistan

11 1.5% 1.5% Austria v Portugal

12 1.5% 1.5% Argentina v Croatia

13 1.4% 1.4% Argentina v Colombia

14 1.4% 1.4% Austria v England

15 1.3% 1.3% Portugal v Uruguay

16 1.2% 1.2% England v Uruguay

17 1.0% 1.0% Algeria v Croatia

18 1.0% 1.0% Algeria v Colombia

19 0.9% 0.9% DR Congo v Spain

20 0.8% 0.8% Argentina v Portugal

# HA+ HA- Team

1 75.3% 75.3% Spain

2 30.6% 30.6% Croatia

3 28.6% 28.6% Colombia

4 17.3% 17.2% Portugal

5 15.6% 15.6% England

6 8.8% 8.8% Austria

7 7.5% 7.5% Uruguay

8 4.8% 4.7% Argentina

9 4.7% 4.7% Panama

10 3.4% 3.4% Algeria

11 2.1% 2.0% Uzbekistan

12 1.1% 1.1% DR Congo

13 0.1% 0.1% Jordan

14 0.0% 0.1% Saudi Arabia

15 0.0% 0.0% Ghana

16 0.0% 0.0% Jamaica

17 0.0% 0.0% Cabo Verde

M92 - Round of 16 (W79 v W80)

Jul 5, Mexico City (Estadio Azteca)

# HA+ HA- Teams

1 34.6% 18.5% England v Mexico+

2 10.1% 5.4% Croatia v Mexico+

3 4.5% 2.5% Mexico+ v Norway

4 4.0% 7.1% England v Scotland

5 3.0% 6.4% Denmark v England

6 2.7% 1.4% Mexico+ v Senegal

7 2.7% 5.6% England v Korea Republic

8 2.5% 1.3% Colombia v Mexico+

9 2.1% 3.5% England v Morocco

10 2.1% 1.1% Mexico+ v Portugal

11 1.3% 2.0% Ecuador v England

12 1.2% 0.6% Mexico+ v Uruguay

13 1.1% 2.0% Croatia v Scotland

14 1.1% 1.6% Brazil v England

15 1.1% 1.8% England v Japan

16 1.0% 0.5% Ecuador v Mexico+

17 1.0% 1.7% Côte d’Ivoire v England

18 1.0% 0.5% Côte d’Ivoire v Mexico+

19 0.9% 1.5% England v Germany

20 0.9% 0.5% France v Mexico+

# HA+ HA- Team

1 63.6% 33.9% Mexico+

2 54.3% 54.3% England

3 15.8% 15.8% Croatia

4 7.4% 7.7% Norway

5 7.2% 12.9% Scotland

6 5.5% 11.7% Denmark

7 4.8% 10.3% Korea Republic

8 4.4% 4.5% Senegal

9 4.0% 4.0% Colombia

10 3.9% 5.4% Ecuador

11 3.8% 6.2% Morocco

12 3.3% 3.4% Portugal

13 3.3% 4.7% Côte d’Ivoire

14 3.0% 3.9% Germany

15 2.2% 2.4% Uruguay

16 2.0% 3.4% Japan

17 2.0% 2.8% Brazil

18 1.6% 2.8% Ukraine

19 1.5% 1.5% France

20 1.3% 1.9% Netherlands

M91 - Round of 16 (W76 v W78)

Jul 5, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 13.8% 13.8% Brazil v Senegal

2 12.5% 12.5% Brazil v Ecuador

3 7.4% 7.4% Brazil v Germany

4 6.1% 6.1% Brazil v Norway

5 6.0% 6.0% Brazil v France

6 5.9% 5.9% Japan v Senegal

7 5.4% 5.4% Ecuador v Japan

8 3.7% 3.7% Morocco v Senegal

9 3.4% 3.4% Ecuador v Morocco

10 3.2% 3.2% Germany v Japan

11 3.0% 3.0% Netherlands v Senegal

12 2.7% 2.7% Ecuador v Netherlands

13 2.6% 2.6% Japan v Norway

14 2.6% 2.6% France v Japan

15 2.0% 2.0% Germany v Morocco

16 1.9% 1.9% Brazil v Côte d’Ivoire

17 1.6% 1.6% Morocco v Norway

18 1.6% 1.6% France v Morocco

19 1.6% 1.6% Germany v Netherlands

20 1.3% 1.3% France v Netherlands

# HA+ HA- Team

1 48.0% 48.0% Brazil

2 28.8% 28.8% Senegal

3 26.1% 26.0% Ecuador

4 20.6% 20.6% Japan

5 15.5% 15.4% Germany

6 13.0% 12.9% Morocco

7 12.7% 12.8% Norway

8 12.5% 12.5% France

9 10.5% 10.5% Netherlands

10 4.1% 4.1% Côte d’Ivoire

11 2.6% 2.6% Ukraine

12 2.2% 2.3% Scotland

13 1.6% 1.6% Poland

14 1.2% 1.2% Tunisia

15 0.3% 0.3% Sweden

16 0.3% 0.3% Iraq

17 0.0% 0.0% Albania

18 0.0% 0.0% Curaçao

19 0.0% 0.0% Bolivia

20 0.0% 0.0% Haiti

M90 - Round of 16 (W73 v W75)

Jul 4, Houston (NRG Stadium)

# HA+ HA- Teams

1 10.7% 10.0% Netherlands v Switzerland

2 8.6% 7.4% Denmark v Netherlands

3 7.9% 7.2% Korea Republic v Netherlands

4 6.5% 6.6% Canada v Netherlands

5 5.9% 5.5% Morocco v Switzerland

6 4.8% 4.5% Italy v Netherlands

7 4.7% 4.1% Denmark v Morocco

8 4.3% 4.0% Korea Republic v Morocco

9 3.6% 5.8% Mexico v Netherlands

10 3.5% 3.6% Canada v Morocco

11 3.1% 3.0% Brazil v Switzerland

12 2.8% 2.6% Japan v Switzerland

13 2.6% 2.5% Italy v Morocco

14 2.5% 2.2% Brazil v Denmark

15 2.3% 2.1% Brazil v Korea Republic

16 2.2% 1.9% Denmark v Japan

17 2.0% 1.9% Japan v Korea Republic

18 2.0% 3.2% Mexico v Morocco

19 1.9% 1.9% Brazil v Canada

20 1.7% 1.7% Canada v Japan

# HA+ HA- Team

1 43.9% 43.8% Netherlands

2 24.4% 22.8% Switzerland

3 24.0% 24.2% Morocco

4 19.6% 16.9% Denmark

5 18.0% 16.5% Korea Republic

6 14.8% 15.0% Canada

7 12.9% 13.0% Brazil

8 11.5% 11.4% Japan

9 10.9% 10.3% Italy

10 8.2% 13.2% Mexico

11 5.7% 5.7% Scotland

12 1.8% 2.5% Wales

13 1.4% 1.5% Czechia

14 1.1% 1.1% Ukraine

15 0.5% 0.6% Poland

16 0.3% 0.5% South Africa

17 0.3% 0.6% Qatar

18 0.3% 0.3% Ireland

19 0.2% 0.2% Tunisia

20 0.1% 0.1% Sweden

M89 - Round of 16 (W74 v W77)

Jul 4, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 25.6% 26.1% France v Germany

2 12.0% 12.2% Ecuador v France

3 10.1% 9.0% France v Korea Republic

4 5.1% 4.0% Denmark v France

5 3.6% 3.6% Germany v Senegal

6 2.4% 1.3% France v Switzerland

7 2.2% 1.8% Australia v Germany

8 1.8% 1.0% France v Italy

9 1.8% 1.5% Germany v Paraguay

10 1.7% 1.7% Ecuador v Senegal

11 1.7% 1.7% Germany v Norway

12 1.7% 2.6% Canada v France

13 1.6% 1.6% Germany v Scotland

14 1.4% 1.2% Korea Republic v Senegal

15 1.3% 1.1% Germany v Turkey

16 1.2% 4.6% France v Mexico

17 1.2% 1.7% Germany v United States+

18 1.1% 0.9% Australia v Ecuador

19 1.0% 1.0% Germany v Morocco

20 0.9% 0.7% Ecuador v Paraguay

# HA+ HA- Team

1 63.0% 63.9% France

2 40.7% 40.9% Germany

3 19.0% 19.1% Ecuador

4 16.0% 14.1% Korea Republic

5 8.8% 8.9% Senegal

6 8.1% 6.3% Denmark

7 5.5% 4.4% Australia

8 4.5% 4.4% Scotland

9 4.4% 3.6% Paraguay

10 4.1% 4.1% Norway

11 3.9% 2.1% Switzerland

12 3.2% 2.7% Turkey

13 2.9% 4.3% United States+

14 2.9% 1.6% Italy

15 2.8% 2.7% Morocco

16 2.6% 4.1% Canada

17 1.9% 7.2% Mexico

18 1.4% 1.4% Brazil

19 1.3% 1.3% Côte d’Ivoire

20 1.1% 0.9% Wales

M88 - Round of 32 (2D v 2G)

Jul 3, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 17.3% 16.2% Australia v Iran

2 12.5% 14.3% Iran v United States+

3 11.7% 13.5% Iran v Paraguay

4 9.2% 5.9% Iran v Turkey

5 7.5% 7.0% Australia v Egypt

6 7.3% 6.7% Australia v Belgium

7 5.4% 6.2% Egypt v United States+

8 5.2% 6.1% Belgium v United States+

9 5.1% 5.8% Egypt v Paraguay

10 4.9% 5.7% Belgium v Paraguay

11 4.0% 2.6% Egypt v Turkey

12 3.9% 2.4% Belgium v Turkey

13 1.9% 2.4% Iran v Slovakia

14 0.8% 1.1% Egypt v Slovakia

15 0.8% 1.0% Belgium v Slovakia

# HA+ HA- Team

1 53.1% 53.1% Iran

2 32.6% 30.4% Australia

3 23.5% 27.0% United States+

4 23.0% 23.0% Egypt

5 22.3% 22.3% Belgium

6 22.0% 25.4% Paraguay

7 17.3% 11.1% Turkey

8 3.6% 4.5% Slovakia

9 1.6% 1.5% New Zealand

10 1.1% 1.5% Romania

11 0.1% 0.1% Kosovo

M87 - Round of 32 (1K v 3_DEIJL)

Jul 3, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 30.5% 29.6% Panama v Portugal

2 18.3% 17.8% Colombia v Panama

3 9.2% 9.3% Croatia v Portugal

4 5.6% 4.7% Australia v Portugal

5 5.4% 5.4% Colombia v Croatia

6 4.8% 4.2% Paraguay v Portugal

7 3.1% 2.6% Australia v Colombia

8 3.0% 3.0% England v Portugal

9 2.7% 2.3% Colombia v Paraguay

10 2.6% 2.2% Portugal v Turkey

11 2.2% 2.2% Algeria v Portugal

12 1.7% 1.7% Colombia v England

13 1.5% 1.5% Austria v Portugal

14 1.4% 1.2% Colombia v Turkey

15 1.4% 4.0% Portugal v United States+

# HA+ HA- Team

1 61.9% 62.0% Portugal

2 49.6% 48.2% Panama

3 35.8% 35.7% Colombia

4 14.9% 15.0% Croatia

5 8.9% 7.5% Australia

6 7.7% 6.7% Paraguay

7 4.8% 4.8% England

8 4.1% 3.6% Turkey

9 3.5% 3.5% Algeria

10 2.4% 2.4% Austria

11 2.2% 6.4% United States+

12 1.6% 1.6% Uzbekistan

13 0.7% 0.7% DR Congo

14 0.6% 0.6% Norway

15 0.4% 0.5% Slovakia

M86 - Round of 32 (1J v 2H)

Jul 3, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 68.0% 68.1% Argentina v Uruguay

2 11.1% 11.2% Argentina v Spain

3 7.0% 7.0% Argentina v Saudi Arabia

4 6.3% 6.2% Austria v Uruguay

5 3.1% 3.1% Algeria v Uruguay

6 1.7% 1.7% Argentina v Cabo Verde

7 1.0% 1.0% Austria v Spain

8 0.6% 0.6% Austria v Saudi Arabia

9 0.5% 0.5% Algeria v Spain

10 0.3% 0.3% Algeria v Saudi Arabia

11 0.2% 0.2% Austria v Cabo Verde

12 0.1% 0.1% Algeria v Cabo Verde

13 0.0% 0.0% Jordan v Uruguay

14 0.0% 0.0% Jordan v Spain

15 0.0% 0.0% Jordan v Saudi Arabia

# HA+ HA- Team

1 87.8% 87.9% Argentina

2 77.5% 77.4% Uruguay

3 12.6% 12.7% Spain

4 8.1% 8.0% Austria

5 8.0% 7.9% Saudi Arabia

6 4.0% 4.0% Algeria

7 1.9% 1.9% Cabo Verde

8 0.1% 0.1% Jordan

M85 - Round of 32 (1B v 3_EFGIJ)

Jul 3, Vancouver (BC Place)

# HA+ HA- Teams

1 14.5% 5.3% Canada+ v Egypt

2 9.9% 14.7% Egypt v Switzerland

3 9.8% 12.9% Egypt v Italy

4 8.2% 3.0% Canada+ v Iran

5 5.8% 7.7% Iran v Italy

6 5.7% 8.6% Iran v Switzerland

7 4.7% 1.8% Algeria v Canada+

8 3.9% 1.4% Belgium v Canada+

9 3.2% 4.9% Algeria v Switzerland

10 3.1% 4.1% Algeria v Italy

11 2.7% 3.6% Belgium v Italy

12 2.6% 4.0% Belgium v Switzerland

13 2.6% 1.0% Austria v Canada+

14 1.8% 0.7% Canada+ v Ukraine

15 1.8% 2.8% Austria v Switzerland

# HA+ HA- Team

1 41.2% 15.3% Canada+

2 34.7% 33.8% Egypt

3 29.3% 39.0% Italy

4 28.2% 42.8% Switzerland

5 19.9% 19.9% Iran

6 11.1% 11.0% Algeria

7 9.4% 9.3% Belgium

8 6.3% 6.3% Austria

9 4.8% 5.1% Ukraine

10 4.4% 4.7% Japan

11 3.7% 4.0% Poland

12 2.0% 2.1% Netherlands

13 1.2% 2.6% Wales

14 0.9% 0.9% Tunisia

15 0.8% 0.9% Norway

M84 - Round of 32 (1H v 2J)

Jul 2, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 50.6% 50.6% Austria v Spain

2 25.3% 25.3% Algeria v Spain

3 8.9% 8.8% Argentina v Spain

4 7.4% 7.5% Austria v Uruguay

5 3.7% 3.8% Algeria v Uruguay

6 2.1% 2.1% Jordan v Spain

7 1.3% 1.3% Argentina v Uruguay

8 0.3% 0.3% Jordan v Uruguay

9 0.1% 0.2% Austria v Saudi Arabia

10 0.1% 0.1% Algeria v Saudi Arabia

11 0.0% 0.0% Argentina v Saudi Arabia

12 0.0% 0.0% Austria v Cabo Verde

13 0.0% 0.0% Algeria v Cabo Verde

14 0.0% 0.0% Jordan v Saudi Arabia

15 0.0% 0.0% Argentina v Cabo Verde

# HA+ HA- Team

1 86.9% 86.8% Spain

2 58.2% 58.2% Austria

3 29.1% 29.2% Algeria

4 12.8% 12.9% Uruguay

5 10.3% 10.2% Argentina

6 2.4% 2.4% Jordan

7 0.2% 0.3% Saudi Arabia

8 0.0% 0.0% Cabo Verde

M83 - Round of 32 (2K v 2L)

Jul 2, Toronto (BMO Field)

# HA+ HA- Teams

1 31.6% 31.6% Colombia v Croatia

2 17.8% 17.9% Croatia v Portugal

3 12.5% 12.5% Colombia v England

4 8.9% 8.9% Colombia v Panama

5 7.1% 7.0% England v Portugal

6 6.0% 5.9% Croatia v Uzbekistan

7 5.1% 5.0% Panama v Portugal

8 3.7% 3.7% Croatia v DR Congo

9 2.4% 2.4% England v Uzbekistan

10 1.7% 1.7% Panama v Uzbekistan

11 1.5% 1.5% DR Congo v England

12 1.1% 1.1% DR Congo v Panama

13 0.3% 0.3% Colombia v Ghana

14 0.2% 0.2% Ghana v Portugal

15 0.1% 0.1% Croatia v Jamaica

# HA+ HA- Team

1 59.2% 59.2% Croatia

2 53.3% 53.4% Colombia

3 30.1% 30.1% Portugal

4 23.4% 23.4% England

5 16.8% 16.8% Panama

6 10.1% 10.1% Uzbekistan

7 6.3% 6.3% DR Congo

8 0.6% 0.6% Ghana

9 0.2% 0.2% Jamaica

10 0.0% 0.0% New Caledonia

M82 - Round of 32 (1G v 3_AEHIJ)

Jul 2, Seattle (Lumen Field)

# HA+ HA- Teams

1 24.5% 24.3% Algeria v Belgium

2 14.5% 14.6% Belgium v Norway

3 13.1% 13.1% Austria v Belgium

4 8.3% 8.2% Algeria v Iran

5 7.0% 7.1% Belgium v Senegal

6 5.6% 5.6% Iran v Norway

7 4.3% 4.3% Austria v Iran

8 3.2% 3.1% Algeria v Egypt

9 2.7% 2.7% Iran v Senegal

10 1.8% 1.7% Austria v Egypt

11 1.7% 1.7% Belgium v Uruguay

12 1.5% 1.3% Belgium v Korea Republic

13 1.4% 1.4% Egypt v Norway

14 1.4% 1.4% Belgium v France

15 0.8% 0.8% Argentina v Belgium

# HA+ HA- Team

1 67.8% 67.9% Belgium

2 36.0% 35.6% Algeria

3 24.1% 24.0% Iran

4 21.4% 21.7% Norway

5 19.2% 19.1% Austria

6 10.5% 10.5% Senegal

7 8.1% 8.0% Egypt

8 2.5% 2.4% Uruguay

9 2.2% 1.9% Korea Republic

10 2.0% 2.0% France

11 1.2% 1.2% Argentina

12 0.9% 0.8% Denmark

13 0.8% 0.8% Saudi Arabia

14 0.8% 0.8% Côte d’Ivoire

15 0.5% 0.5% Czechia

M81 - Round of 32 (1D v 3_BEFIJ)

Jul 1, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 12.3% 4.5% Switzerland v United States+

2 11.5% 12.2% Canada v United States+

3 9.1% 3.4% Italy v United States+

4 5.5% 2.9% Ukraine v United States+

5 5.0% 2.7% Japan v United States+

6 4.7% 2.5% Norway v United States+

7 4.7% 2.4% United States+ v Wales

8 4.6% 2.5% Poland v United States+

9 3.7% 3.6% Switzerland v Turkey

10 3.4% 9.7% Canada v Turkey

11 2.7% 2.6% Italy v Turkey

12 2.3% 1.2% Senegal v United States+

13 2.3% 1.3% Netherlands v United States+

14 1.7% 2.4% Turkey v Ukraine

15 1.6% 2.3% Australia v Switzerland

# HA+ HA- Team

1 66.0% 37.8% United States+

2 19.8% 30.5% Turkey

3 18.7% 11.8% Switzerland

4 17.4% 31.9% Canada

5 13.7% 8.8% Italy

6 8.5% 19.4% Australia

7 8.4% 7.8% Ukraine

8 7.7% 7.3% Japan

9 7.2% 6.6% Norway

10 7.0% 6.6% Poland

11 7.0% 6.4% Wales

12 5.1% 10.9% Paraguay

13 3.5% 3.3% Senegal

14 3.5% 3.3% Netherlands

15 1.7% 1.6% Tunisia

M80 - Round of 32 (1L v 3_EHIJK)

Jul 1, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 18.6% 18.8% England v Norway

2 9.1% 9.2% England v Senegal

3 7.7% 7.6% Côte d’Ivoire v England

4 6.9% 6.8% Colombia v England

5 6.8% 6.6% England v Uzbekistan

6 6.6% 6.7% Croatia v Norway

7 5.2% 5.2% England v Portugal

8 3.9% 3.9% England v Uruguay

9 3.4% 3.5% Ecuador v England

10 3.3% 3.2% DR Congo v England

11 3.2% 3.2% Croatia v Senegal

12 2.6% 2.6% Croatia v Côte d’Ivoire

13 2.4% 2.4% Croatia v Uzbekistan

14 2.4% 2.4% Colombia v Croatia

15 2.2% 2.2% England v Germany

# HA+ HA- Team

1 71.7% 71.7% England

2 26.1% 26.3% Norway

3 25.2% 25.1% Croatia

4 12.6% 12.8% Senegal

5 10.7% 10.5% Côte d’Ivoire

6 9.5% 9.5% Colombia

7 9.4% 9.3% Uzbekistan

8 7.2% 7.3% Portugal

9 5.5% 5.5% Uruguay

10 4.7% 4.8% Ecuador

11 4.6% 4.5% DR Congo

12 3.2% 3.2% Panama

13 3.0% 3.0% Germany

14 2.5% 2.5% France

15 1.6% 1.6% Saudi Arabia

M79 - Round of 32 (1A v 3_CEFHI)

Jul 1, Mexico City (Estadio Azteca)

# HA+ HA- Teams

1 28.3% 20.2% Mexico+ v Scotland

2 9.8% 6.8% Côte d’Ivoire v Mexico+

3 8.9% 6.5% Mexico+ v Morocco

4 5.8% 4.1% Ecuador v Mexico+

5 5.4% 4.0% Mexico+ v Ukraine

6 5.0% 3.7% Japan v Mexico+

7 3.7% 2.6% Germany v Mexico+

8 3.6% 2.6% Mexico+ v Poland

9 3.4% 7.3% Korea Republic v Scotland

10 3.3% 2.3% Brazil v Mexico+

11 3.2% 6.8% Denmark v Scotland

12 2.3% 1.7% Mexico+ v Netherlands

13 1.2% 2.5% Côte d’Ivoire v Korea Republic

14 1.1% 2.3% Côte d’Ivoire v Denmark

15 1.1% 2.3% Korea Republic v Morocco

# HA+ HA- Team

1 80.2% 57.5% Mexico+

2 35.2% 35.0% Scotland

3 12.3% 11.8% Côte d’Ivoire

4 11.1% 11.2% Morocco

5 9.6% 20.4% Denmark

6 9.3% 20.0% Korea Republic

7 7.2% 7.2% Ecuador

8 6.8% 7.0% Ukraine

9 6.3% 6.5% Japan

10 4.6% 4.6% Germany

11 4.4% 4.5% Poland

12 4.1% 4.1% Brazil

13 2.9% 2.9% Netherlands

14 1.2% 1.2% Tunisia

15 1.1% 1.1% Norway

M78 - Round of 32 (2E v 2I)

Jun 30, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 29.1% 29.1% Ecuador v Senegal

2 15.3% 15.3% Germany v Senegal

3 14.8% 14.8% Ecuador v Norway

4 9.2% 9.2% Côte d’Ivoire v Senegal

5 8.9% 8.9% Ecuador v France

6 7.8% 7.8% Germany v Norway

7 4.7% 4.7% France v Germany

8 4.7% 4.7% Côte d’Ivoire v Norway

9 2.8% 2.8% Côte d’Ivoire v France

10 1.0% 1.0% Ecuador v Iraq

11 0.5% 0.5% Germany v Iraq

12 0.4% 0.4% Curaçao v Senegal

13 0.3% 0.3% Côte d’Ivoire v Iraq

14 0.2% 0.2% Curaçao v Norway

15 0.1% 0.1% Bolivia v Ecuador

# HA+ HA- Team

1 53.9% 53.9% Ecuador

2 53.9% 54.0% Senegal

3 28.4% 28.4% Germany

4 27.5% 27.4% Norway

5 17.0% 17.0% Côte d’Ivoire

6 16.5% 16.5% France

7 1.9% 1.9% Iraq

8 0.7% 0.7% Curaçao

9 0.2% 0.2% Bolivia

10 0.0% 0.0% Suriname

M77 - Round of 32 (1I v 3_CDFGH)

Jun 30, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 20.0% 15.8% Australia v France

2 17.4% 14.3% France v Paraguay

3 15.8% 15.8% France v Scotland

4 9.0% 7.7% France v Turkey

5 6.0% 5.9% France v Morocco

6 4.8% 13.6% France v United States+

7 3.7% 2.9% Australia v Senegal

8 3.3% 2.6% Paraguay v Senegal

9 2.9% 2.9% Scotland v Senegal

10 2.2% 2.1% Brazil v France

11 2.0% 1.6% Australia v Norway

12 1.7% 1.4% Norway v Paraguay

13 1.7% 1.4% Senegal v Turkey

14 1.5% 1.5% Norway v Scotland

15 1.2% 1.2% France v Slovakia

# HA+ HA- Team

1 77.8% 77.9% France

2 25.7% 20.3% Australia

3 22.3% 18.3% Paraguay

4 20.3% 20.2% Scotland

5 14.5% 14.4% Senegal

6 11.6% 9.8% Turkey

7 7.7% 7.6% Morocco

8 7.6% 7.6% Norway

9 6.2% 17.5% United States+

10 2.8% 2.8% Brazil

11 1.5% 1.6% Slovakia

12 0.4% 0.4% Japan

13 0.3% 0.3% Ukraine

14 0.3% 0.4% Romania

15 0.2% 0.2% Netherlands

M76 - Round of 32 (1C v 2F)

Jun 29, Houston (NRG Stadium)

# HA+ HA- Teams

1 37.3% 37.3% Brazil v Japan

2 14.1% 14.1% Brazil v Netherlands

3 12.6% 12.6% Japan v Morocco

4 6.6% 6.6% Brazil v Ukraine

5 5.8% 5.8% Brazil v Tunisia

6 4.9% 4.9% Brazil v Poland

7 4.8% 4.7% Morocco v Netherlands

8 3.1% 3.2% Japan v Scotland

9 2.2% 2.2% Morocco v Ukraine

10 1.9% 1.9% Morocco v Tunisia

11 1.6% 1.6% Morocco v Poland

12 1.4% 1.4% Brazil v Sweden

13 1.2% 1.2% Netherlands v Scotland

14 0.6% 0.6% Scotland v Ukraine

15 0.5% 0.5% Scotland v Tunisia

# HA+ HA- Team

1 70.3% 70.3% Brazil

2 53.0% 53.0% Japan

3 23.7% 23.6% Morocco

4 20.0% 20.0% Netherlands

5 9.5% 9.4% Ukraine

6 8.2% 8.2% Tunisia

7 6.9% 6.9% Poland

8 6.0% 6.0% Scotland

9 2.0% 2.0% Sweden

10 0.4% 0.4% Albania

11 0.0% 0.0% Haiti

M75 - Round of 32 (1F v 2C)

Jun 29, Monterrey (Estadio BBVA)

# HA+ HA- Teams

1 39.3% 39.4% Morocco v Netherlands

2 15.7% 15.8% Brazil v Netherlands

3 15.0% 14.9% Netherlands v Scotland

4 13.1% 13.1% Japan v Morocco

5 5.3% 5.3% Brazil v Japan

6 5.0% 4.9% Japan v Scotland

7 1.7% 1.7% Morocco v Ukraine

8 1.0% 1.0% Morocco v Poland

9 0.7% 0.7% Brazil v Ukraine

10 0.6% 0.6% Scotland v Ukraine

11 0.5% 0.5% Morocco v Tunisia

12 0.4% 0.4% Haiti v Netherlands

13 0.4% 0.4% Brazil v Poland

14 0.4% 0.4% Poland v Scotland

15 0.2% 0.2% Brazil v Tunisia

# HA+ HA- Team

1 70.4% 70.5% Netherlands

2 55.7% 55.9% Morocco

3 23.5% 23.5% Japan

4 22.4% 22.4% Brazil

5 21.3% 21.1% Scotland

6 3.0% 3.0% Ukraine

7 1.8% 1.8% Poland

8 0.9% 0.9% Tunisia

9 0.6% 0.6% Haiti

10 0.4% 0.4% Sweden

11 0.0% 0.0% Albania

M74 - Round of 32 (1E v 3_ABCDF)

Jun 28, Boston (Gillette Stadium)

# HA+ HA- Teams

1 26.9% 23.7% Germany v Korea Republic

2 13.8% 12.1% Ecuador v Korea Republic

3 11.6% 9.0% Denmark v Germany

4 5.9% 4.6% Denmark v Ecuador

5 5.1% 2.7% Germany v Switzerland

6 4.7% 7.3% Canada v Germany

7 3.8% 2.1% Germany v Italy

8 2.9% 3.2% Czechia v Germany

9 2.6% 1.4% Ecuador v Switzerland

10 2.6% 2.0% Germany v Wales

11 2.6% 9.9% Germany v Mexico

12 2.5% 3.9% Canada v Ecuador

13 2.0% 1.1% Ecuador v Italy

14 1.8% 1.6% Côte d’Ivoire v Korea Republic

15 1.7% 1.8% Czechia v Ecuador

# HA+ HA- Team

1 63.1% 63.2% Germany

2 42.6% 37.4% Korea Republic

3 32.6% 32.6% Ecuador

4 18.4% 14.2% Denmark

5 8.1% 4.3% Switzerland

6 7.5% 11.6% Canada

7 6.0% 3.3% Italy

8 4.7% 5.1% Czechia

9 4.2% 3.2% Wales

10 4.2% 4.2% Côte d’Ivoire

11 4.1% 15.7% Mexico

12 1.9% 1.7% Scotland

13 0.8% 1.3% South Africa

14 0.6% 0.6% Morocco

15 0.5% 0.8% Ireland

M73 - Round of 32 (2A v 2B)

Jun 28, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 16.4% 13.6% Korea Republic v Switzerland

2 15.5% 12.3% Denmark v Switzerland

3 12.6% 11.4% Canada v Korea Republic

4 12.0% 10.3% Canada v Denmark

5 7.3% 6.1% Italy v Korea Republic

6 6.9% 5.5% Denmark v Italy

7 6.4% 9.4% Mexico v Switzerland

8 4.9% 7.9% Canada v Mexico

9 2.9% 4.2% Italy v Mexico

10 1.9% 2.4% Korea Republic v Wales

11 1.9% 1.8% Czechia v Switzerland

12 1.9% 2.2% Denmark v Wales

13 1.5% 1.5% Canada v Czechia

14 1.1% 1.4% South Africa v Switzerland

15 0.9% 1.3% Korea Republic v Qatar

# HA+ HA- Team

1 41.8% 39.0% Switzerland

2 39.1% 34.9% Korea Republic

3 37.2% 31.4% Denmark

4 32.3% 32.8% Canada

5 18.6% 17.5% Italy

6 15.3% 24.0% Mexico

7 5.0% 6.8% Wales

8 4.5% 4.5% Czechia

9 2.6% 3.7% South Africa

10 2.2% 3.7% Qatar

11 1.2% 1.3% Ireland

12 0.1% 0.1% North Macedonia

13 0.0% 0.1% Northern Ireland

14 0.0% 0.1% Bosnia and Herzegovina

M72 - Group Stage (Group K - IC Playoff 1)

Jun 27, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 82.2% 82.3% DR Congo v Uzbekistan

2 17.3% 17.2% Jamaica v Uzbekistan

3 0.5% 0.5% New Caledonia v Uzbekistan

# HA+ HA- Team

1 100% 100% Uzbekistan

2 82.2% 82.3% DR Congo

3 17.3% 17.2% Jamaica

4 0.5% 0.5% New Caledonia

M71 - Group Stage (Group K)

Jun 27, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 100% 100% Colombia v Portugal

# HA+ HA- Team

1 100% 100% Portugal

2 100% 100% Colombia

M70 - Group Stage (Group J)

Jun 27, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 100% 100% Argentina v Jordan

# HA+ HA- Team

1 100% 100% Jordan

2 100% 100% Argentina

M69 - Group Stage (Group J)

Jun 27, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 100% 100% Algeria v Austria

# HA+ HA- Team

1 100% 100% Algeria

2 100% 100% Austria

M68 - Group Stage (Group L)

Jun 27, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 100% 100% Croatia v Ghana

# HA+ HA- Team

1 100% 100% Ghana

2 100% 100% Croatia

M67 - Group Stage (Group L)

Jun 27, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 100% 100% England v Panama

# HA+ HA- Team

1 100% 100% Panama

2 100% 100% England

M66 - Group Stage (Group H)

Jun 26, Guadalajara (Estadio Akron)

# HA+ HA- Teams

1 100% 100% Spain v Uruguay

# HA+ HA- Team

1 100% 100% Uruguay

2 100% 100% Spain

M65 - Group Stage (Group H)

Jun 26, Houston (NRG Stadium)

# HA+ HA- Teams

1 100% 100% Cabo Verde v Saudi Arabia

# HA+ HA- Team

1 100% 100% Saudi Arabia

2 100% 100% Cabo Verde

M64 - Group Stage (Group G)

Jun 26, Vancouver (BC Place)

# HA+ HA- Teams

1 100% 100% Belgium v New Zealand

# HA+ HA- Team

1 100% 100% Belgium

2 100% 100% New Zealand

M63 - Group Stage (Group G)

Jun 26, Seattle (Lumen Field)

# HA+ HA- Teams

1 100% 100% Egypt v Iran

# HA+ HA- Team

1 100% 100% Iran

2 100% 100% Egypt

M62 - Group Stage (Group I - IC Playoff 2)

Jun 26, Toronto (BMO Field)

# HA+ HA- Teams

1 70.3% 70.2% Iraq v Senegal

2 27.0% 27.1% Bolivia v Senegal

3 2.7% 2.7% Senegal v Suriname

# HA+ HA- Team

1 100% 100% Senegal

2 70.3% 70.2% Iraq

3 27.0% 27.1% Bolivia

4 2.7% 2.7% Suriname

M61 - Group Stage (Group I)

Jun 26, Boston (Gillette Stadium)

# HA+ HA- Teams

1 100% 100% France v Norway

# HA+ HA- Team

1 100% 100% France

2 100% 100% Norway

M60 - Group Stage (Group D)

Jun 25, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 100% 100% Australia v Paraguay

# HA+ HA- Team

1 100% 100% Australia

2 100% 100% Paraguay

M59 - Group Stage (Group D - UEFA Playoff C)

Jun 25, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 59.4% 59.4% Turkey v United States+

2 26.0% 25.9% Slovakia v United States+

3 11.3% 11.3% Romania v United States+

4 3.3% 3.3% Kosovo v United States+

# HA+ HA- Team

1 100% 100% United States+

2 59.4% 59.4% Turkey

3 26.0% 25.9% Slovakia

4 11.3% 11.3% Romania

5 3.3% 3.3% Kosovo

M58 - Group Stage (Group F - UEFA Playoff B)

Jun 25, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 100% 100% Netherlands v Tunisia

# HA+ HA- Team

1 100% 100% Tunisia

2 100% 100% Netherlands

M57 - Group Stage (Group F)

Jun 25, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 41.2% 41.1% Japan v Ukraine

2 36.7% 36.8% Japan v Poland

3 15.8% 15.8% Japan v Sweden

4 6.4% 6.4% Albania v Japan

# HA+ HA- Team

1 100% 100% Japan

2 41.2% 41.1% Ukraine

3 36.7% 36.8% Poland

4 15.8% 15.8% Sweden

5 6.4% 6.4% Albania

M56 - Group Stage (Group E)

Jun 25, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 100% 100% Ecuador v Germany

# HA+ HA- Team

1 100% 100% Ecuador

2 100% 100% Germany

M55 - Group Stage (Group E)

Jun 25, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 100% 100% Curaçao v Côte d’Ivoire

# HA+ HA- Team

1 100% 100% Côte d’Ivoire

2 100% 100% Curaçao

M54 - Group Stage (Group A)

Jun 24, Monterrey (Estadio BBVA)

# HA+ HA- Teams

1 100% 100% Korea Republic v South Africa

# HA+ HA- Team

1 100% 100% Korea Republic

2 100% 100% South Africa

M53 - Group Stage (Group A - UEFA Playoff D)

Jun 24, Mexico City (Estadio Azteca)

# HA+ HA- Teams

1 68.8% 68.9% Denmark v Mexico+

2 20.4% 20.3% Czechia v Mexico+

3 9.0% 8.9% Ireland v Mexico+

4 1.8% 1.8% Mexico+ v North Macedonia

# HA+ HA- Team

1 100% 100% Mexico+

2 68.8% 68.9% Denmark

3 20.4% 20.3% Czechia

4 9.0% 8.9% Ireland

5 1.8% 1.8% North Macedonia

M52 - Group Stage (Group B - UEFA Playoff A)

Jun 24, Seattle (Lumen Field)

# HA+ HA- Teams

1 69.7% 69.8% Italy v Qatar

2 27.4% 27.3% Qatar v Wales

3 1.5% 1.5% Northern Ireland v Qatar

4 1.4% 1.3% Bosnia and Herzegovina v Qatar

# HA+ HA- Team

1 100% 100% Qatar

2 69.7% 69.8% Italy

3 27.4% 27.3% Wales

4 1.5% 1.5% Northern Ireland

5 1.4% 1.3% Bosnia and Herzegovina

M51 - Group Stage (Group B)

Jun 24, Vancouver (BC Place)

# HA+ HA- Teams

1 100% 100% Canada+ v Switzerland

# HA+ HA- Team

1 100% 100% Canada+

2 100% 100% Switzerland

M50 - Group Stage (Group C)

Jun 24, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 100% 100% Haiti v Morocco

# HA+ HA- Team

1 100% 100% Haiti

2 100% 100% Morocco

M49 - Group Stage (Group C)

Jun 24, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 100% 100% Brazil v Scotland

# HA+ HA- Team

1 100% 100% Scotland

2 100% 100% Brazil

M48 - Group Stage (Group K - IC Playoff 1)

Jun 23, Guadalajara (Estadio Akron)

# HA+ HA- Teams

1 82.2% 82.3% Colombia v DR Congo

2 17.3% 17.2% Colombia v Jamaica

3 0.5% 0.5% Colombia v New Caledonia

# HA+ HA- Team

1 100% 100% Colombia

2 82.2% 82.3% DR Congo

3 17.3% 17.2% Jamaica

4 0.5% 0.5% New Caledonia

M47 - Group Stage (Group K)

Jun 23, Houston (NRG Stadium)

# HA+ HA- Teams

1 100% 100% Portugal v Uzbekistan

# HA+ HA- Team

1 100% 100% Portugal

2 100% 100% Uzbekistan

M46 - Group Stage (Group L)

Jun 23, Toronto (BMO Field)

# HA+ HA- Teams

1 100% 100% Croatia v Panama

# HA+ HA- Team

1 100% 100% Panama

2 100% 100% Croatia

M45 - Group Stage (Group L)

Jun 23, Boston (Gillette Stadium)

# HA+ HA- Teams

1 100% 100% England v Ghana

# HA+ HA- Team

1 100% 100% Ghana

2 100% 100% England

M44 - Group Stage (Group J)

Jun 22, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 100% 100% Algeria v Jordan

# HA+ HA- Team

1 100% 100% Jordan

2 100% 100% Algeria

M43 - Group Stage (Group J)

Jun 22, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 100% 100% Argentina v Austria

# HA+ HA- Team

1 100% 100% Argentina

2 100% 100% Austria

M42 - Group Stage (Group I - IC Playoff 2)

Jun 22, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 70.3% 70.2% France v Iraq

2 27.0% 27.1% Bolivia v France

3 2.7% 2.7% France v Suriname

# HA+ HA- Team

1 100% 100% France

2 70.3% 70.2% Iraq

3 27.0% 27.1% Bolivia

4 2.7% 2.7% Suriname

M41 - Group Stage (Group I)

Jun 22, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 100% 100% Norway v Senegal

# HA+ HA- Team

1 100% 100% Senegal

2 100% 100% Norway

M40 - Group Stage (Group G)

Jun 21, Vancouver (BC Place)

# HA+ HA- Teams

1 100% 100% Egypt v New Zealand

# HA+ HA- Team

1 100% 100% New Zealand

2 100% 100% Egypt

M39 - Group Stage (Group G)

Jun 21, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 100% 100% Belgium v Iran

# HA+ HA- Team

1 100% 100% Belgium

2 100% 100% Iran

M38 - Group Stage (Group H)

Jun 21, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 100% 100% Saudi Arabia v Spain

# HA+ HA- Team

1 100% 100% Saudi Arabia

2 100% 100% Spain

M37 - Group Stage (Group H)

Jun 21, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 100% 100% Cabo Verde v Uruguay

# HA+ HA- Team

1 100% 100% Uruguay

2 100% 100% Cabo Verde

M36 - Group Stage (Group F)

Jun 20, Monterrey (Estadio BBVA)

# HA+ HA- Teams

1 100% 100% Japan v Tunisia

# HA+ HA- Team

1 100% 100% Japan

2 100% 100% Tunisia

M35 - Group Stage (Group F - UEFA Playoff B)

Jun 20, Houston (NRG Stadium)

# HA+ HA- Teams

1 41.2% 41.1% Netherlands v Ukraine

2 36.7% 36.8% Netherlands v Poland

3 15.8% 15.8% Netherlands v Sweden

4 6.4% 6.4% Albania v Netherlands

# HA+ HA- Team

1 100% 100% Netherlands

2 41.2% 41.1% Ukraine

3 36.7% 36.8% Poland

4 15.8% 15.8% Sweden

5 6.4% 6.4% Albania

M34 - Group Stage (Group E)

Jun 20, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 100% 100% Curaçao v Ecuador

# HA+ HA- Team

1 100% 100% Ecuador

2 100% 100% Curaçao

M33 - Group Stage (Group E)

Jun 20, Toronto (BMO Field)

# HA+ HA- Teams

1 100% 100% Côte d’Ivoire v Germany

# HA+ HA- Team

1 100% 100% Côte d’Ivoire

2 100% 100% Germany

M32 - Group Stage (Group D)

Jun 19, Seattle (Lumen Field)

# HA+ HA- Teams

1 100% 100% Australia v United States+

# HA+ HA- Team

1 100% 100% Australia

2 100% 100% United States+

M31 - Group Stage (Group D - UEFA Playoff C)

Jun 19, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 59.4% 59.4% Paraguay v Turkey

2 26.0% 25.9% Paraguay v Slovakia

3 11.3% 11.3% Paraguay v Romania

4 3.3% 3.3% Kosovo v Paraguay

# HA+ HA- Team

1 100% 100% Paraguay

2 59.4% 59.4% Turkey

3 26.0% 25.9% Slovakia

4 11.3% 11.3% Romania

5 3.3% 3.3% Kosovo

M30 - Group Stage (Group C)

Jun 19, Boston (Gillette Stadium)

# HA+ HA- Teams

1 100% 100% Morocco v Scotland

# HA+ HA- Team

1 100% 100% Scotland

2 100% 100% Morocco

M29 - Group Stage (Group C)

Jun 19, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 100% 100% Brazil v Haiti

# HA+ HA- Team

1 100% 100% Haiti

2 100% 100% Brazil

M28 - Group Stage (Group A)

Jun 18, Guadalajara (Estadio Akron)

# HA+ HA- Teams

1 100% 100% Korea Republic v Mexico+

# HA+ HA- Team

1 100% 100% Mexico+

2 100% 100% Korea Republic

M27 - Group Stage (Group B)

Jun 18, Vancouver (BC Place)

# HA+ HA- Teams

1 100% 100% Canada+ v Qatar

# HA+ HA- Team

1 100% 100% Canada+

2 100% 100% Qatar

M26 - Group Stage (Group B - UEFA Playoff A)

Jun 18, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 69.7% 69.8% Italy v Switzerland

2 27.4% 27.3% Switzerland v Wales

3 1.5% 1.5% Northern Ireland v Switzerland

4 1.4% 1.3% Bosnia and Herzegovina v Switzerland

# HA+ HA- Team

1 100% 100% Switzerland

2 69.7% 69.8% Italy

3 27.4% 27.3% Wales

4 1.5% 1.5% Northern Ireland

5 1.4% 1.3% Bosnia and Herzegovina

M25 - Group Stage (Group A - UEFA Playoff D)

Jun 18, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 68.8% 68.9% Denmark v South Africa

2 20.4% 20.3% Czechia v South Africa

3 9.0% 8.9% Ireland v South Africa

4 1.8% 1.8% North Macedonia v South Africa

# HA+ HA- Team

1 100% 100% South Africa

2 68.8% 68.9% Denmark

3 20.4% 20.3% Czechia

4 9.0% 8.9% Ireland

5 1.8% 1.8% North Macedonia

M24 - Group Stage (Group K)

Jun 17, Mexico City (Estadio Azteca)

# HA+ HA- Teams

1 100% 100% Colombia v Uzbekistan

# HA+ HA- Team

1 100% 100% Colombia

2 100% 100% Uzbekistan

M23 - Group Stage (Group K - IC Playoff 1)

Jun 17, Houston (NRG Stadium)

# HA+ HA- Teams

1 82.2% 82.3% DR Congo v Portugal

2 17.3% 17.2% Jamaica v Portugal

3 0.5% 0.5% New Caledonia v Portugal

# HA+ HA- Team

1 100% 100% Portugal

2 82.2% 82.3% DR Congo

3 17.3% 17.2% Jamaica

4 0.5% 0.5% New Caledonia

M22 - Group Stage (Group L)

Jun 17, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 100% 100% Croatia v England

# HA+ HA- Team

1 100% 100% Croatia

2 100% 100% England

M21 - Group Stage (Group L)

Jun 17, Toronto (BMO Field)

# HA+ HA- Teams

1 100% 100% Ghana v Panama

# HA+ HA- Team

1 100% 100% Panama

2 100% 100% Ghana

M20 - Group Stage (Group J)

Jun 16, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 100% 100% Austria v Jordan

# HA+ HA- Team

1 100% 100% Jordan

2 100% 100% Austria

M19 - Group Stage (Group J)

Jun 16, Kansas City (GEHA Field at Arrowhead Stadium)

# HA+ HA- Teams

1 100% 100% Algeria v Argentina

# HA+ HA- Team

1 100% 100% Algeria

2 100% 100% Argentina

M18 - Group Stage (Group I - IC Playoff 2)

Jun 16, Boston (Gillette Stadium)

# HA+ HA- Teams

1 70.3% 70.2% Iraq v Norway

2 27.0% 27.1% Bolivia v Norway

3 2.7% 2.7% Norway v Suriname

# HA+ HA- Team

1 100% 100% Norway

2 70.3% 70.2% Iraq

3 27.0% 27.1% Bolivia

4 2.7% 2.7% Suriname

M17 - Group Stage (Group I)

Jun 16, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 100% 100% France v Senegal

# HA+ HA- Team

1 100% 100% France

2 100% 100% Senegal

M16 - Group Stage (Group G)

Jun 15, Seattle (Lumen Field)

# HA+ HA- Teams

1 100% 100% Belgium v Egypt

# HA+ HA- Team

1 100% 100% Belgium

2 100% 100% Egypt

M15 - Group Stage (Group G)

Jun 15, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 100% 100% Iran v New Zealand

# HA+ HA- Team

1 100% 100% Iran

2 100% 100% New Zealand

M14 - Group Stage (Group H)

Jun 15, Atlanta (Mercedes-Benz Stadium)

# HA+ HA- Teams

1 100% 100% Cabo Verde v Spain

# HA+ HA- Team

1 100% 100% Cabo Verde

2 100% 100% Spain

M13 - Group Stage (Group H)

Jun 15, Miami (Hard Rock Stadium)

# HA+ HA- Teams

1 100% 100% Saudi Arabia v Uruguay

# HA+ HA- Team

1 100% 100% Saudi Arabia

2 100% 100% Uruguay

M12 - Group Stage (Group F - UEFA Playoff B)

Jun 14, Monterrey (Estadio BBVA)

# HA+ HA- Teams

1 41.2% 41.1% Tunisia v Ukraine

2 36.7% 36.8% Poland v Tunisia

3 15.8% 15.8% Sweden v Tunisia

4 6.4% 6.4% Albania v Tunisia

# HA+ HA- Team

1 100% 100% Tunisia

2 41.2% 41.1% Ukraine

3 36.7% 36.8% Poland

4 15.8% 15.8% Sweden

5 6.4% 6.4% Albania

M11 - Group Stage (Group F)

Jun 14, Dallas (AT&T Stadium)

# HA+ HA- Teams

1 100% 100% Japan v Netherlands

# HA+ HA- Team

1 100% 100% Japan

2 100% 100% Netherlands

M10 - Group Stage (Group E)

Jun 14, Houston (NRG Stadium)

# HA+ HA- Teams

1 100% 100% Curaçao v Germany

# HA+ HA- Team

1 100% 100% Germany

2 100% 100% Curaçao

M9 - Group Stage (Group E)

Jun 14, Philadelphia (Lincoln Financial Field)

# HA+ HA- Teams

1 100% 100% Côte d’Ivoire v Ecuador

# HA+ HA- Team

1 100% 100% Ecuador

2 100% 100% Côte d’Ivoire

M8 - Group Stage (Group B)

Jun 13, San Francisco (Levi’s Stadium)

# HA+ HA- Teams

1 100% 100% Qatar v Switzerland

# HA+ HA- Team

1 100% 100% Qatar

2 100% 100% Switzerland

M7 - Group Stage (Group C)

Jun 13, New York/New Jersey (MetLife Stadium)

# HA+ HA- Teams

1 100% 100% Brazil v Morocco

# HA+ HA- Team

1 100% 100% Brazil

2 100% 100% Morocco

M6 - Group Stage (Group D - UEFA Playoff C)

Jun 13, Vancouver (BC Place)

# HA+ HA- Teams

1 59.4% 59.4% Australia v Turkey

2 26.0% 25.9% Australia v Slovakia

3 11.3% 11.3% Australia v Romania

4 3.3% 3.3% Australia v Kosovo

# HA+ HA- Team

1 100% 100% Australia

2 59.4% 59.4% Turkey

3 26.0% 25.9% Slovakia

4 11.3% 11.3% Romania

5 3.3% 3.3% Kosovo

M5 - Group Stage (Group C)

Jun 13, Boston (Gillette Stadium)

# HA+ HA- Teams

1 100% 100% Haiti v Scotland

# HA+ HA- Team

1 100% 100% Haiti

2 100% 100% Scotland

M4 - Group Stage (Group D)

Jun 12, Los Angeles (SoFi Stadium)

# HA+ HA- Teams

1 100% 100% Paraguay v United States+

# HA+ HA- Team

1 100% 100% United States+

2 100% 100% Paraguay

M3 - Group Stage (Group B - UEFA Playoff A)

Jun 12, Toronto (BMO Field)

# HA+ HA- Teams

1 69.7% 69.8% Canada+ v Italy

2 27.4% 27.3% Canada+ v Wales

3 1.5% 1.5% Canada+ v Northern Ireland

4 1.4% 1.3% Bosnia and Herzegovina v Canada+

# HA+ HA- Team

1 100% 100% Canada+

2 69.7% 69.8% Italy

3 27.4% 27.3% Wales

4 1.5% 1.5% Northern Ireland

5 1.4% 1.3% Bosnia and Herzegovina

M2 - Group Stage (Group A - UEFA Playoff D)

Jun 12, Guadalajara (Estadio Akron)

# HA+ HA- Teams

1 68.8% 68.9% Denmark v Korea Republic

2 20.4% 20.3% Czechia v Korea Republic

3 9.0% 8.9% Ireland v Korea Republic

4 1.8% 1.8% Korea Republic v North Macedonia

# HA+ HA- Team

1 100% 100% Korea Republic

2 68.8% 68.9% Denmark

3 20.4% 20.3% Czechia

4 9.0% 8.9% Ireland

5 1.8% 1.8% North Macedonia

M1 - Group Stage (Group A)

Jun 11, Mexico City (Estadio Azteca)

# HA+ HA- Teams

1 100% 100% Mexico+ v South Africa

# HA+ HA- Team

1 100% 100% Mexico+

2 100% 100% South Africa

Today we officially launched VoteYesOrNoAi.com, where you can personally weigh in on this question in a live public vote tallied anonymously by country and U.S. state.

Where do you stand?

We’ve also created two new versions of our search engine that tune settings to cater to both sides:

* noai.duckduckgo.com - turns off Search Assist, buttons to Duck.ai, and automatically turns on the hide AI images filter.

* yesai.duckduckgo.com - tunes Search Assist to often, features Duck.ai on the homepage, and turns off the hide AI images filter.

You can also customize the search settings at duckduckgo.com for something in between.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Putting aside whether AI will eliminate jobs on net, it will certainly displace a lot of them. And the displaced people are unlikely to be the same people who will secure the higher-tech jobs that get created. For example, are most displaced truck drivers going to get jobs in new industries that require a lot of education?

Put these two problems together and maybe there is a solution hiding in plain sight: create millions of new jobs in housing. Someone has to build all the affordable homes we need, so why not subsidize jobs and training for those displaced by AI? These jobs will arguably offer an easier onramp and are sorely needed now (and likely for the next couple of decades as we chip away at this housing shortage).

Granted, labor may not be the primary bottleneck in the housing shortage, but it is certainly a factor and one that is seemingly being overlooked. There are many bills in Congress aimed at increasing housing supply through new financing and relaxed regulatory frameworks. A program like this would help complete the package.

None of this has been happening via market forces alone, so the government would therefore need to create a new program at a large scale, like the Works Progress Administration (WPA) at the end of the Great Depression, but this time squarely focused on affordable housing (and otherwise narrowly tailored to avoid inefficiencies).

There are a lot of ways such a program could work (or not work), including ways to maximize the long-term public benefit (and minimize its long-term public cost), but this post is just about floating the high-level idea. So there you have it. I’ll leave you though with a few more specific thought starters:

* Every state could benefit since every state has affordable housing issues. Programs become more politically viable when more states benefit from them.

* Such a program could be narrowly tailored, squarely focused on affordable housing (as mentioned above), but also keeping the jobs time-limited (the whole program could be time-limited and tied to overall housing stock), and keeping the wages slightly below local market rates (to complement rather than compete with private construction).

* It could also be tailored to those just affected by AI, but that doesn’t seem like the right approach to me. The AI job market impact timeline is unclear, but we can nevertheless start an affordable-housing jobs program now that we need today, which can also serve as a partial backstop for AI-job fallout tomorrow. It seems fine to me if some workers who join aren't directly displaced by AI, since the program still creates net new jobs we will need anyway and to some extent jobs within an education band are fungible.

* We will surely need other programs as well to help displaced workers specifically (for example, increased unemployment benefits).

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

* There is a rumor continuously circulating that we’re owned by Google, which of course couldn’t be farther from the truth. I was actually a witness in the U.S. v. Google trial for the DOJ. I think this rumor started because Google used to own the domain duck.com and was pointing it at Google search for several years. After my public and private complaining for those same years, in 2018 we finally convinced Google to give us the duck.com domain, which we now use for our email protection service, but the rumor still persists.

* We’ve been blocked in China since 2014, and are on-and-off blocked in several other countries too like Indonesia and India because we don’t censor search results.

* We’ve been an independent company since our founding in 2008 and been working on our own search indexes for as many years. For over fifteen years now (that whole time) we’ve been doing our own knowledge graph index (like answers from Wikipedia), over ten years for local and other instant-answer indexes (like businesses), and in the past few years we’ve been ramping up our wider web index to support our Search Assist and Duck.ai features. DuckDuckGo began with me crawling the web in my basement, and in the early days, the FBI actually showed up at my front door since I had crawled one of their honeypots.

* The plurality of our search traffic now comes from our own browsers. Yes, we have our own browsers with our search engine built in along with a ton of other protections. How do they compare to other popular browsers and extensions, you ask? We made a comparison page so you can see the differences. Our mobile browsers on iOS & Android launched back in 2018 (wow, that’s seven years ago), and our desktop browsers on Mac and Windows in 2022/23. Our iOS browser market share continues to climb and we’re now #3 in the U.S. (behind Safari and Chrome) and #4 on Android (behind Chrome, Samsung, and Firefox). People appreciate all the protections and the front-and-center (now customizable) fire button that quickly clears tabs and data in an (also customizable) animation of fire.

* About 13% of U.S. adults self-report as a “current user” of DuckDuckGo. That’s way more than most people think. Our search market share is lower since all of those users don’t use us on all of their devices, especially on Android where Google makes it especially hard. Once you realize that then it is less surprising that we have the highest search market share on Mac at about 4% in the U.S., followed by iOS at about 3%. I’m talking about the U.S. here since about 44% of our searches are from the U.S., and no other country is double digits, but rounding out the top ten countries are Germany, the United Kingdom, France, Canada, India, the Netherlands, Indonesia, Australia, and Japan.

* Our approach to AI differs from most other companies trying to shove it down your throat in that we are dedicated to making all AI features private, useful, and optional. If you like AI, we offer private AI search answers at duckduckgo.com and private chat at duck.ai, which are built-into our browsers. If you don’t like or don’t want AI, that’s cool with us too. You can easily turn all of these features off. In fact, we made a noai.duckduckgo.com search domain that automatically sets those settings for you, including a recent setting we added that allows you to hide many AI-generated images within image search. Another related thing you might find surprising is search traffic has continued to grow steadily even since the rise of ChatGPT (with Duck.ai traffic growing even faster).

* If you didn’t know we have a browser, you probably also don’t know we have a DuckDuckGo Subscription (launched last year), that includes our VPN, more advanced AI models in Duck.ai, and in the U.S., Personal Information Removal and Identity Theft Restoration. It’s now available in 30 countries with a similar VPN footprint and our VPN is run by us (see latest security audit and free trials).

* Speaking of lots of countries, our team has been completely distributed from the beginning, now at over 300 across about 30 countries as well, with less than half in the U.S. And we’re still hiring. We have a unique work culture that, among other things, avoids standing meetings on Wednesdays and Thursdays. We get the whole company together for a week once a year.

* We played a critical role in the Global Privacy Control standard and the creation of search preference menus. I have a graduate degree in Technology and Public Policy and so we’ve done more of this kind of thing than one might expect, even going so far to draft our own Do Not Track legislation before we got GPC going. We also donate yearly to like-minded organizations (here’s our 2025 announcement), with our cumulative donations now at over $8 million. Check our donations page for details going back to 2011. We can do this since we’ve been profitable for about that long, and more recently have even started investing in related startups as well.

If this hodge-podge of stuff makes you think of anything, please let me know. I’m not only taking requests for easter-egg logo ideas, but also for stuff to write about.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

GLP-1 medicines, initially developed for blood glucose and weight control, improve outcomes in people with cardiovascular, kidney, liver, arthritis, and sleep apnea disorders, actions mediated in part through anti-inflammatory and metabolic pathways, with some benefits partly independent of the degree of weight loss achieved.

Many millions of Americans would benefit from taking these drugs, but limited insurance coverage and high out-of-pocket costs limit their use. However, if the price was low enough to match their cost savings, then wider coverage could be justified.

What price would that need to be?

How can a drug be cost neutral (pay for itself)?

If a drug reduces future care expenditures by more than it costs, then it pays for itself (is cost neutral). Modeling this out can get complicated, especially for drugs whose benefits accrue over many years. That’s because you need to at least consider how those cost savings unfold as well as people who stop taking the drug (adherence rate).

What about GLP-1s?

The Congressional Budget Office (CBO) looked into this question in detail in 2024, using these approximate assumptions:

* 9-year time horizon (2026-2034)

* 35% adherence (continuation) in first year, ramping up to 50% by year 9

* 80% yearly continuation rate after first year of continuous use

* Available to Medicare patients who are classified as obese or overweight with at least one weight-related comorbidity

* $5,600/year cost (implying about ~$625/month cost if you assume a 75% reimbursement)

* Savings from reduced care of $50/year in 2026, reaching $650/year in 2034

CBO concludes in their report that these assumptions lead to expanding GLP-1 coverage to be very costly to the Federal government.

Doesn’t Medicare prescribe GLP-1s now?

Yes, but not for obesity writ large, which about doubles the qualified population. From the CBO report:

In 2026, in CBO’s estimation, 29 million beneficiaries would qualify for coverage under the illustrative policy. About half of that group, or 16 million people, would have access to those medications under current law for indications such as diabetes, cardiovascular coverage, and other indications approved by the FDA in the interim.

Still, CBO only expects a small percentage of eligible patients to use the drugs, due to activation and adherence. In the final year of their model (2034) they predict “about 1.6 million (or 14 percent) of the newly eligible beneficiaries would use an AOM [anti-obesity medication].”

What break-even price does the CBO report imply?

CBO doesn’t calculate a break-even price. They just say they expect $50 in average savings in year 1, rising to $650 in year 9, implying a 9% offset rate overall. If we assume a progression of increasing yearly savings to match these assumptions, you get a cumulative savings of about $4,000, or about $445 per year. If you assume on average the government picks up 75% of the bill, that implies a break-even drug price of about $50/month.

What has changed since 2024 that would modify this CBO estimate?

* Time Horizon. The CBO time horizon of 9 years is too low. They acknowledge that “from 2035 to 2044…the savings from improved health would be larger than they would be from 2026 to 2034”. So, let’s add 10 years (for a total of 19), and stipulate the last ten years average $800 in savings, rising from the year 9 savings of $650. That implies an increased average savings per year of about 1.4x.

* Emerging Benefits. The CBO only accounted for weight-loss benefits, using comparisons to Bariatric surgery and other weight-loss evidence, noting that “CBO is not aware of any direct evidence showing that treatment of obesity with GLP-1-based products reduces spending on other medical services.” However, the other emerging benefits reduce conditions that are very costly to Medicare like kidney, heart, and sleep apnea complications (e.g., dialysis, heart surgery, CPAP, etc.). I think we can speculatively call this a 2x multiplier.

So, then what break-even price does that imply today?

$50/month (CBO original estimate) x 1.4 (for increased time horizon) x 2 (for increased benefits) =~ $140/month.

That is, at $140/month, we would expect the Medicare costs to roughly equal the cost savings, and net out to 0 (be cost neutral). That’s still well below the recently negotiated prices starting in 2027 (for example, Ozempic at $274).

Why are you thinking about this again?

I’m seeing the expanding benefit profile and thinking we have to find a way to get these benefits to more people, as a way to generally increase our average standard of living (in this case by greatly increasing health-span/quality of life).

The best way I can see to get the benefits to the most people is if it were government subsidized/provided. But obviously health care costs are a major barrier to that method, and so framing expanding benefits as cost neutral seems most politically viable.

What if the price were $100/month?

At $100/month, then it would be a no-brainer (assuming the above math is correct) to make available to qualified Medicare patients (say, using at least the CBO obesity criteria) since it would then be clearly making the government money.

Additionally, at that price, I think you could start expanding it well beyond Medicare in waves, monitoring outcomes and cost savings. For example, you could start with programs where the government similarly runs both the cost and benefits like Medicare, such as for the military and other federal workers. Then you could expand to Medicaid / disability (with cross-state subsidies). Ultimately there could be justification to subsidize a subset of the public at large, for example people aged 55+ who will be on Medicare within the next ten years, such that the savings will be realized by the federal government and the whole program could still be cost neutral.

OK great, but how to you get GLP-1s at $100/month?

This may be a half-baked idea, but one approach is to offer up the market a yearly contract for expanded Medicare, and whoever shows up first gets it (to be renegotiated yearly).

I don’t think this is that crazy because the manufacturing cost is estimated to be a small fraction of the list price, and the UK previously had negotiated pricing in this ballpark. The volumes would be huge, and as more companies enter the market, I imagine eventually one of them would take the offer.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

I find the signal to noise ratio on social media and news sites/apps too low for me to have a consistently good experience on them. So, I developed an alternative, heavily curated approach to an information diet that I’m laying out here in hope people will give me suggesions over time to improve it.

It involves four main inputs:

* RSS, skewed towards “most upvoted” feeds

I use Reeder because it has a polished user interface, formats the feed in an aggregated, chronological timeline across devices, and has native reddit and filtering support, but there are many other RSS readers too.

I subscribe to around 25 feeds and 25 subreddits through Reeder. To increase the signal to noise, I try to find “most upvoted” feeds where possible. For example, for subreddits, I usually use the top posts for the week, which you can get for any subreddit like this: https://www.reddit.com/r/economics/top.rss?t=week (just replace ‘economics’ with the subreddit of your choice). Doing so will get you on the order of five top posts per day, but you can also change ‘week’ to ‘day’ to increase that number to about twenty or to ‘month’ to decrease it to about one, which I do for some feeds. To find generally interesting subreddits I looked through the top few hundred subreddits, and then I also added some niche subreddits for specific interests I have.

Below is part of my reddit list (alphabetical). You can see I have some really large subreddits (technology, science, todayilearned) mixed in with more niche ones (singularity, truereddit), as well as communities (rootsofprogress, slatestarcodex) and hobbies (phillyunion, usmnt). Getting about twenty five across a range of your interests makes a good base feed.

Many publications still have RSS feeds if you search for publication name+RSS. If they don’t, it’s likely RSS.app or Feedspot has made one you can use instead. There is usually support through one of these methods for sub-section publication feeds, for example the tech section.

Here are some other examples of non-reddit “most upvoted” feeds that might be more widely appealing:

* Hacker News RSS - for example, I added the 300 and 600 points feeds, meaning you get notified when a story hits 300 or 600 points (you can pick any number).

* NYT RSS - they have most emailed/shared/viewed

* Techmeme RSS - curated by the techmeme team

* LessWrong RSS - they have a curated feed

Then I also just consume the main RSS feeds of some really high signal publications like Ars Technica (full articles come through for subscribers), The Information, Quanta, etc.

Even with all this curation, the signal to noise for me isn’t that great. I skim through the timeline mostly, but I do end up getting a bunch of interesting articles this way every day. I do use the filtering feature of Reeder to drop out some really low hit keywords.

* Podcasts

I subscribe to about 20 podcasts via Overcast. I like the Overcast “Voice Boost (clear, consistent volume)” and “Smart Speed (shorter silences)” features as well as the ability to do a custom playback speed for each podcast.

The signal to noise ratio is better here than the RSS feeds, but I still don’t listen to every episode, and for ones I do I often skip around. I like having a queue to listen to in the car and at the gym.

I find new podcast discovery pretty hard. I’ve looked through the Overcast top podcasts lists in all the different categories, and tried lots of them, but not many stick for me.

* Email newsletters

I subscribe to about the same amount (20-25) of email newsletters, some daily but most weekly or less. Signal/noise is less than podcasts, but greater than the RSS feeds. I’d guess my hit rate is about 20% in terms of reading them through vs. maybe 50% for podcasts listening through and 5% for the full RSS amalgamation reading through.

About half of the email newsletters I subscribe to are through Substack and half are direct from websites/organizations.

* People sending me links

I really appreciate when people send me curated links, which happens less than I’d like but I can’t complain because the signal to noise here is the highest with a hit ratio maybe 80%. I try to encourage it by saying thank you and responding when I have thoughts.

With those four inputs, I feel decently covered, but sometimes I do wonder what I’m missing out on and occasionally relapse back to going directly to a news or social media app and skimming the front page. This method of course depends on having a good list of feeds, podcasts, and newsletters. But in general, I’m personally happier with this approach, though of course your mileage my vary. If you’re doing something similar and have any ideas on process tweaks or specific recommendations for feeds, podcasts, or newsletters, I’d love to hear them.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Currently, China’s working-age population is over 4 times the U.S.

According to Our World in Data, China’s working-age population is 983 million to the U.S.’s 223 million, or 4.4x.

In 2050, despite being in rapid decline, China’s working-age population is still projected to be over 3 times the U.S.’s

The projections put China’s 2050 working-age population at 745 million to the U.S.’s 232 million, or 3.2x.

In 2075, noting projections are more speculative, China’s working-age population is still projected to be about double the U.S.’s

The projections put China’s 2075 working-age population at 468 million to the U.S.’s 235 million, or 2.0x.

Noah Smith recently delved into this rather deeply in his post “China’s demographics will be fine through mid-century” noting:

China’s economic might is not going to go “poof” and disappear from population aging; in fact, as I’ll explain, it probably won’t suffer significant problems from aging until the second half of this century.

And even in the second half, you can’t count on their demographic decline then either, both because even by 2075 their working-age population is still projected to be double the U.S.’s under current conditions, but also because those conditions are unlikely to hold. As Noah also notes:

Meanwhile, there’s an even greater danger that China’s leaders will panic over the country’s demographics and do something very rash…All in all, the narrative that demographics will tip the balance of economic and geopolitical power away from China in the next few decades seems overblown and unrealistic.

OK, why does this demographic stuff matter again?

Check out my earlier article for details, but here’s a summary.

[A] U.S.-China tech tie is a big win for China because of its population advantage. China doesn’t need to surpass us technologically; it just needs to implement what already exists across its massive workforce. Matching us is enough for its economy to dwarf ours. If per person output were equal today, China’s economy would be over 4× America’s because China’s population is over 4× the U.S. That exact 4× outcome is unlikely given China’s declining population and the time it takes to diffuse technology, but 2 to 3× is not out of the question. China doesn’t even need to match our per-person output: their population will be over 3× ours for decades, so reaching ⅔ would still give them an economy twice our size since 3 × ⅔ = 2.

…With an economy a multiple of the U.S., it’s much easier to outspend us on defense and R&D, since budgets are typically set as a share of GDP.

…What if China then starts vastly outspending us on science and technology and becomes many years ahead of us in future critical technologies, such as artificial superintelligence, energy, quantum computing, humanoid robots, and space technology? That’s what the U.S. was to China just a few decades ago, and China runs five-year plans that prioritize science and technology.

…Our current per person output advantage is not sustainable unless we regain technological dominance.

…[W]e should materially increase effective research funding and focus on our own technology diffusion plans to upgrade our jobs and raise our living standards.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

It’s a measure of an economy’s ability to generate income from inputs—to do more with less…If an economy increases its total income without using more inputs…it is said to enjoy higher TFP [Total Factor Productivity].

TFP is an important macroeconomic statistic [because] improvements in living standards must come from growth in TFP over the long run. This is because living standards are measured as income per person—so an economy cannot raise them simply by adding more and more people to its workforce.

Meanwhile, economists have amassed lots of evidence that investments in capital have diminishing returns. This leaves TFP advancement as the only possible source of sustained growth in income per person, as Robert Solow, the late Nobel laureate, first showed in a 1957 paper.

So, it’s important. Critically important to long-term progress. To learn more about TFP, check out the full IMF primer referenced above and then this post I wrote about TFP titled “The key to increasing standard of living is increasing labor productivity,” which also has more links embedded in it. It explains how the only sustainable way to increase TFP is to “to invent new technology that enables workers to do more per hour.” And this is why I’m always going on and on about increasing research funding.

Let’s assume for a second that most people want more prosperity and that long-term prosperity does indeed primarily flow through Total Factor Productivity. Then why aren’t we talking about TFP a lot more? Why isn’t Total Factor Productivity front and center in our political agendas?

I think there are a host of reasons for that, including those I outlined in the paradox of progress. But another even simpler reason has to be that Total Factor Productivity is a terrible, inscrutable name, at least from the perspective of selling the concept to the mainstream public.

Every word of it isn’t great. It starts with “total,” which isn’t as off-putting as the other words, but doesn’t add much especially as the first word, let alone the fact that economists quibble that it isn’t an actual total. “Factor” seems like a math word and doesn’t add much either. And then you have “productivity,” which is confusing to most people because it has an unrelated colloquial meaning, and from a political perspective it also codes as job-cutting which is inherently unappealing.

Now, lots of economics jargon has similar problems, case in point “Gross Domestic Product” (GDP). Given GDP hasn’t been rebranded, I doubt TFP will either. That said, I think for anyone trying to communicate this concept to the public, we shouldn’t take the TFP name or acronym as a given, but try to use something more appealing and inherently understandable.

I’m looking to switch to something else but not sure to exactly what. My thinking so far has led me to work in the words “prosperity” or “innovation” directly like:

* Prosperity Driver

* Prosperity Component

* Innovation Multiplier

Do you have any other suggestions?

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

In the last 30 days, Gemini has been downloaded about the same amount of times as ChatGPT, and nothing else is even close.

While ChatGPT had a massive head start, Google is rapidly turning consumer AI into a duopoly. Despite endless headlines mentioning Anthropic, Perplexity, and others, none of the alternatives seem to be meaningfully gaining market share right now relative to ChatGPT, except Gemini. The reason is simple: the others don’t have the distribution channels to match Google’s.

The next phase of consumer AI competition will favor Google even more. As I recently noted, consumer internet workflows increasingly span across search, browsing, and AI. Who has the most entrenched position in search and browsing to complement consumer AI? Google. For example, their monopoly browser (Chrome) can get AI features to most consumers the fastest.

Google’s ability to leverage its market position to distribute its own AI products continues unabated, and U.S. v. Google made clear that distribution powers a scale advantage. That is, Google’s search assets are not easily replicable because of the vast user engagement data Google alone possesses. And an increasing number of sites don’t even allow web crawlers or access to their content except for Google.

We shouldn’t settle for a shift from Google’s search monopoly to an AI duopoly. Thus far, regulators have only addressed Google’s advantages at the margins. There remains time to address these dynamics and unlock innovation.

One possible (non-regulatory) response is deeper partnerships and consolidation between the other AI companies, search engines, and browsers in an effort to compete more with more scale in this new market. This has already started around the edges, for example we (at DuckDuckGo) have partnered with You.com to develop a better news search index and are looking to partner with others to advance the web index we’ve been working on, as well as to enhance our browser and AI features. But the market is ripe for larger deals.

To see where those might come from, here’s the top mobile (consumer AI is used primarily on mobile) search engines in the U.S. according to Cloudflare, who sees the most traffic. Google is #1, followed by us (DuckDuckGo) at #2, then Yahoo and Bing. Everyone else is sub 1%.

Similarly, here’s the top mobile browsers in the U.S. Safari and Chrome dominate, followed by Samsung Internet, DuckDuckGo and Firefox above 1%.

And finally, here are the top 20 consumer web destinations in the U.S., according to SEMRush. The top ten are Google, Reddit, Facebook, Amazon, Yahoo, Wikipedia, Instagram, DuckDuckGo, and ChatGPT.

Partnerships and consolidation between these companies could produce some more effective competition. So far, consumer AI has actually driven more traditional search and browser usage, not less. We see that in our numbers, and SparkToro reports similar for others. AI is driving people to do more information seeking in general, and as mentioned those workflows increasingly span across search, browsing, and AI. The best experiences seamlessly blend all three in the browser, and so it is natural companies with assets in some of the three areas would want to partner with companies with non-overlapping assets. Additionally, a company with a large consumer user base could help directly drive distribution of consumer AI, browsers, and search engines, especially if that company has unique content assets.

A duopoly in consumer AI will not just be bad for innovation, but will further erode privacy. That’s why I believe DuckDuckGo will remain an important alternative regardless of what happens, but I’m still a little hopeful that innovative partnerships and consolidation could challenge the rapidly emerging consumer AI duopoly.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

* Timescale mismatch. People want benefits now, not decades from now. U.S. politics runs on two-year cycles, while progress policies need decades to compound into large increases in living standards. For example, 2% vs. 3% growth, which would be a great outcome for a progress agenda, seems like a rounding error to most people even though it is meaningful when compounded. And the politicians championing these policies won’t be around to claim credit when they pay off decades later.Resolving this part of the paradox would involve articulating short-term benefits in some manner, for example that research funding is a jobs engine in the short term. It could also involve bundling longer-term investments like research funding in a particular field with shorter-term concrete results like rollout projects in that same field, which people can start seeing the phsyical results from within a couple of years.

* Change aversion. Advocating for far-future progress is selling a sci-fi world, which a lot of people take (and creative media often depict) as dystopian, not utopian. True progress means society changes for the better, delivering better-paid jobs using more advanced technology, and products that bring new conveniences and experiences. But change also means at least some disruption of current ways of life and thinking, and that creates winners and losers in the short term, which in turn creates reasonable anxiety.Resolving this part of the paradox would involve painting a clearer picture of what exactly will change in the short term, paired with explicit transition support for people most directly affected. It could also involve less focus on the far-future altogether, focusing instead on shorter timeframes that could be more easily contextualized.

* Lacking urgency. Not only is there not a clear picture, but progress agenda framing lacks urgency, emphasizing future opportunities rather than short-term crisis. Crisis framing comes with inherent urgency that opportunity framing lacks.Resolving this part of the paradox would involve reframing progress agendas as a response to crisis, such as the risk of China leapfrogging us in critical technology and the military and economic consequences that brings.

All these resolutions share a common thread: making distant abstractions concrete. I’m increasingly convinced that advocating for progress, whether it be basic research funding or otherwise, requires bundling long-term promises with near-term demonstrations, including explicit workforce transition plans, and framing progress as helping to address competitive threats we’re already facing.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Overlapping search, browse, and AI in the browser

Traditional web search, browsing, and AI chatting aren’t going away, even as many people will eventually start interacting more with agents that automate tasks. And all three of these core Internet activities increasingly overlap into complex workflows. Many queries you could start either with a web search or an AI chat, and many of those end with wanting to browse to a website. Sometimes you’re on a website and want to ask a question about it. Sometimes you are in a chat and want to run a related web search, and vice versa.

The best product lets you complete whole tasks with the least friction. Agents are one approach. The other approach, which keeps users in the driver seat, is to seamlessly integrate search, browse, and AI into one interface. Both are important fronts in the new browser wars.

The DuckDuckGo Browser

We’ve been working on AI at DuckDuckGo for several years now, with an overall approach to provide private, useful, and optional AI features—including chat and search instant answers—to people who want the productivity benefits of AI without the privacy risks. Our chat service at duck.ai, which allows you to chat privately with popular chatbots and get real-time answers from the web, has the highest satisfaction ratings we’ve ever seen in a new service, and Search Assist, our take on Google’s AI Overviews, is currently our highest-rated search feature.

While we’re the second largest search engine on mobile in the U.S., we’re also the 4th largest mobile browser (and #3 on iOS specifically). In fact, we think of ourselves as a browser company at this point. I believe the best search, AI chat, and web user experience is where they are all integrated deeply in the browser (vs. in separate apps or services), supporting workflows that allow you to seamlessly move between modes as needed.

Seamless workflows: what we’re creating at DuckDuckGo

I believe our browser is so popular because we focus not just on protection but on also continually refining the user experience, aiming for both dependability and delight. That’s why we have been actively working on creating such seamless workflows across search, browse, and AI for some time now. Here are a few examples.

Easily accessible AI chat sidebar

We have a sidebar in our desktop browser that is easily accessible from any webpage to ask an AI chat question, optionally using page context. From there, you can ask follow-ups, pop it out into a new tab if you need more space, or hide it to return to give more room to the website without losing your place.

Toggle between Duck.ai and search via address bar / homepage

We now have an optional address bar mode on mobile (coming to desktop next) that allows you to easily toggle between private web search and private AI chat when starting queries. Ideally this makes it just as easy to start a web search or an AI chat as in a standalone search or chat app, and further allows you to switch modes mid-query without re-typing.

The toggle is also available on (or coming soon to) our homepage and new tab page.

Handoff between search results and chat

If you start a web search, we’ve built in ways to more easily jump into chat mode if desired. You can click Duck.ai to go into AI mode, or the chat icon from within Search Assist to ask a specific follow-up question related to the answer, which will carry over the answer and sources as context.

Integrating traditional search into Duck.ai

From within Duck.ai chat conversations, we automatically search the web for you and provide links to related searches or websites when appropriate, allowing you to jump back into search or browse mode if desired.

We’re working on refining these features based on user feedback as well as designing more. The best user experience will improve the workflows of the many people that are bouncing between these modes dozens of times a day. Your thoughts are welcomed!

Keeping AI private and optional

As mentioned, our overall approach to AI features is to keep them useful, private, and optional. The above illustrates usefulness, so now a few closing words on private and optional. Similar to our search engine, our AI chats are anonymized. In addition, chats are not used to train AI models. There’s a lot more about private AI chats on our help pages.

We also make sure everything we do with AI is optional, since we know not all of our users want to use AI for a variety of reasons, and that is fine with us. All AI features can be turned off, in both our browser and our search engine.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Yet Congress has stalled on general privacy legislation for decades, even in moments of broad public privacy focus, like after the Snowden revelations and the Cambridge Analytica scandal. So, instead of calling for another general privacy bill that would encompass AI, I believe we should focus on an AI-specific privacy bill.

Many of the privacy frameworks floated over the years for general privacy regulation could essentially be repurposed to apply more narrowly to AI. For example, one approach is to enumerate broad consumer AI rights, such as rights of access, correction, deletion, portability, notice, transparency, opt-out, human review, etc., with clear processes to exercise those rights. Another approach is to create legally binding duties of care and/or loyalty on organizations that hold AI data, requiring them to protect consumers' interests regarding this data, such as to minimize it, avoid foreseeable harm, prohibit secondary use absent consent or necessity, etc.

There are more approaches out there and they are not mutually exclusive. While I have personal thoughts on some of them, my overriding goal is to get something, anything useful passed, and so I remain framework-agnostic. However, I believe within whatever framework Congress adopts, certain fundamentals are non-negotiable:

* Ban a set of clearly harmful practices. Start with what (I hope are) universal agreement items, like identity theft, deceptive impersonation, unauthorized deepfakes, etc. The key is explicitly defining this as a category so that we can debate politically harder cases like personalized pricing and predictive policing (both of which I think should also be banned).

* Practices near the ban threshold should face higher scrutiny. For example, if we can’t manage to outright ban using AI to assist in law enforcement decisions, at the very least this type of use should always be subject to human review, reasonable auditing procedures, etc. Using AI for consequential decisions, like for loan approvals, or for processing sensitive data, like health information, should at least be in this category. And many practices within this category, especially with regards to consumer AI, should be explicitly opt-in.

* Make everything else transparent and optional. Outside the bright-line bans and practices subject to higher scrutiny, any other AI profiling must be transparent and at least come with the ability to opt-out, with only highly limited exceptions where opt‑outs would defeat the purpose, like for legal compliance. Consumers also need meaningful transparency, including prominent disclosures that indicate clearly when you are interacting with an AI system. That means not just generic data collection notices or folding into existing privacy policies, but plain-language explanations shown (or spoken) prominently at the time of processing, which detail what AI systems are inferring and deciding.

* States must maintain authority to strengthen, not undermine, federal minimums. I wrote a whole post about why, with the gist being AI is changing rapidly, the federal government doesn’t react to these changes quickly enough, and states have shown they will act, both in AI and privacy.

Finally, these protections won't stifle progress. Some oppose any AI regulation because they believe it will hinder AI adoption or innovation. In terms of innovation, privacy makes a good analogy: Despite fears that a “patchwork” of state privacy laws would wreak havoc on innovation by going too far, they haven’t. Innovation hasn’t stalled, and neither have Big Tech privacy violations. In terms of adoption, the backlash against AI is real and rising, and smart regulation can help build the trust necessary for sustained AI adoption, not hinder it. We can get the productivity benefits of AI without the privacy harms.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

Because I submitted it, I was able to get “Post Insights” (pictured above, left) that indicated the post got 175,000 views. Similarly, substack reports “Traffic sources” (pictured above, right) and shows 310 views came from reddit. This roughly 1:500 ratio is consistent with others I’ve gathered across several different posts and subreddits, so I don’t think it is particularly anomalous. Reddit views count impressions (when posts appear in feeds), making this ratio also comparable to other platforms.

The bottom line is lots of views on social doesn’t equate to lots of clicks, and certainly not lots of email subscribers, which experiences another 1:100 type of ratio, that is, clicks to email subscribers. My takeaways:

* Social ≠ list growth. Social posts don't build email lists: social post views to new email subscribers is likely less than 50,000 to 1 (500 x 100).

* Optimize the headline. If you do chase social views, nail the headline since that's where 99% of the value lives given almost nobody clicks through. For example, you could expose your brand name or logo, or just raise awareness for a crisp point or concept you can fit in a headline.

0.2% is common for ads; I expected higher for a top organic post on a popular subreddit, but this data suggests otherwise. Of course, your mileage may vary, but I thought it would nevertheless be helpful to put out a real data point I found interesting.

Thanks for reading! Subscribe for free to receive new posts or get the audio version.

So what?

Population size matters because economy size (GDP) matters and GDP = population × output per person. For example:

* 100 million people × $50,000 per year = a $5 trillion economy

* 1 billion people × $50,000 per year = a $50 trillion economy

Then why hasn’t China’s economy already dwarfed America’s?

It’s because China’s output per person is still much lower than America’s. China has about 4× the people but about ¼ the output per person. 4 × ¼ = 1. This means that by any standard GDP measure, such as market exchange rates or Purchasing Power Parity, the two economies are in the same ballpark today.

OK, but why is China’s economic output per person so much lower than America’s?

A primary reason is that large swaths of its workforce aren’t yet at the technological frontier. About 23% of Chinese workers are in agriculture vs. about 1½% in the U.S. However, if China continues to educate its population, mechanize its workforce, and diffuse technology across it, that gap will continue to narrow and per-worker output will continue to climb. Only a decade ago, over 30% of China’s workforce was in agriculture, and per-person output has grown much faster than in the U.S. for decades.

Technology is the driving force enabling China to catch up with the U.S. in economic output per person. As long as China diffuses increasingly sophisticated technology through its workforce significantly faster than in the U.S., then it will keep raising output per person relative to the U.S., growing its economy faster. Diffusion is not automatic; it depends on continued private-sector dynamism and sound policy. It isn’t guaranteed, but it is certainly plausible, if not likely.

Put another way, a U.S.-China tech tie is a big win for China because of its population advantage. China doesn't need to surpass us technologically; it just needs to implement what already exists across its massive workforce. Matching us is enough for its economy to dwarf ours. If per person output were equal today, China’s economy would be over 4× America’s because China’s population is over 4× the U.S. That exact 4× outcome is unlikely given China’s declining population and the time it takes to diffuse technology, but 2 to 3× is not out of the question. China doesn't even need to match our per-person output: their population will be over 3× ours for decades, so reaching ⅔ would still give them an economy twice our size since 3 × ⅔ = 2.

Some may recall similar predictions about Japan in the 1980s that never materialized. But China is fundamentally different: Japan's population peaked at less than ½ the U.S., while China's is over 4× ours. Japan’s workforce had already reached the technological frontier when it stalled out, while China is still far behind with massive room to catch up.

And what does China win exactly?

China wins a much bigger economy. With an economy a multiple of the U.S., it’s much easier to outspend us on defense and R&D, since budgets are typically set as a share of GDP. Once China’s economy is double or triple ours, trying to keep up would strain our economy and risk the classic guns-over-butter trap. (This is the same trap that contributed to the Soviet Union’s collapse: too much of its economy steered toward military ends.) Alliances could help offset raw population scale, but only if we coordinate science, supply chains, and procurement, which we have not achieved at the needed scale.

What if China then starts vastly outspending us on science and technology and becomes many years ahead of us in future critical technologies, such as artificial superintelligence, energy, quantum computing, humanoid robots, and space technology? That’s what the U.S. was to China just a few decades ago, and China runs five-year plans that prioritize science and technology.

What can we do about it?

Our current per person output advantage is not sustainable unless we regain technological dominance. By dominance, I don’t mean a few months ahead like today’s AI cycles. I mean many years ahead in developing, diffusing, and commercializing frontier science and technology.

My takeaway: we need to recognize how quickly we are losing our privileged position to China. If its economy doubles or triples ours, it can outspend us to lock in technological and military dominance. That may not happen, but we shouldn’t bet on it. Instead, we should materially increase effective research funding and focus on our own technology diffusion plans to upgrade our jobs and raise our living standards.

What about AI automation?

The net job effect of AI automation is hotly debated, but any outcome doesn’t change this calculus. If employment levels remain about the same then the status quo population advantage remains. If net jobs drop dramatically due to an AI-dominated economy, staying ahead in AI systems becomes even more important. So, either way, doing more effective research and development is critical.

This should be the most important and bipartisan political issue. Research and technology diffusion isn’t everything, but it is the cornerstone of future prosperity. If we don’t get it right, we definitely lose, and we’re currently not getting it right.

Thanks for reading. Subscribe for free to receive new posts or get the audio feed.

While chatbot conversations resemble longer search queries, chatbot privacy harms have the potential to be significantly worse because the inference potential is dramatically greater. Longer input invites more personal information to be provided, and people are starting to bare their souls to chatbots. The conversational format can make it feel like you’re talking to a friend, a professional, or even a therapist. While search queries reveal interests and personal problems, AI conversations take their specificity to another level and, in addition, reveal thought processes and communication styles, creating a much more comprehensive profile of your personality.

This richer personal information can be more thoroughly exploited for manipulation, both commercially and ideologically, for example, through behavioral chatbot advertising and models designed (or themselves manipulated through SEO or hidden system prompts) to nudge you towards a political position or product. Chatbots have already been found to be more persuasive than humans and have caused people to go into delusional spirals as a result. I suspect we’re just scratching the surface, since they can become significantly more attuned to your particular persuasive triggers through chatbot memory features, where they train and fine-tune based on your past conversations, making the influence much more subtle. Instead of an annoying and obvious ad following you around everywhere, you can have a seemingly convincing argument, tailored to your personal style, with an improperly sourced “fact” that you’re unlikely to fact-check or a subtle product recommendation you’re likely to heed.

That is, all the privacy debates surrounding Google search results from the past two decades apply one-for-one to AI chats, but to an even greater degree. That’s why we (at DuckDuckGo) started offering Duck.ai for protected chatbot conversations and optional, anonymous AI-assisted answers in our private search engine. In doing so, we’re demonstrating that privacy-respecting AI services are feasible. But unfortunately, such protected chats are not yet standard practice, and privacy mishaps are mounting quickly. Grok leaked hundreds of thousands of chatbot conversations that users thought were private. Perplexity’s AI agent was shown to be vulnerable to hackers who could slurp up your personal information. Open AI is openly talking about their vision for a “super assistant” that tracks everything you do and say (including offline). And Anthropic is going to start training on your chatbot conversations by default (previously the default was off). I collected these from just the past few weeks!

It would therefore be ideal if Congress could act quickly to ensure that protected chats become the rule rather than the exception. And yet, I’m not holding my breath because it’s 2025 and the U.S. still doesn’t have a general online privacy law, let alone privacy enshrined in the Constitution as a fundamental right, as it should be. However, there does appear to be an opening right now for AI-specific federal legislation, despite the misguided attempts to ban state AI legislation.

Time is running out because every day that passes further entrenches bad privacy practices. Congress must move before history completely repeats itself and everything that happened with online tracking happens again with AI tracking. AI surveillance should be banned while there is still time. No matter what happens, though, we will still be here, offering protected services, including optional AI services, to consumers who want to reap the productivity benefits of online tools without the privacy harms.

Thanks for reading! Subscribe for free to get new posts or get the podcast.

In fact, most of us alive today have little to no personal experience with such a degree of technological stagnation. That’s a good thing because long-term technological stagnation puts an upper bound on possible increases in our collective standard of living.

From an earlier post:

[W]ithout new technology, our economic prosperity is fundamentally limited. To see that, suppose no breakthroughs occur from this moment onward; we get no new technology based on no new science. Once we max out the hours we can work, the education people will seek, and the efficiency with existing technology, then what? We’d be literally stuck. Fundamentally, if you don’t have new tools, new technology, new scientific breakthroughs, you stagnate.

That is, standard of living is fundamentally a function of labor productivity. To improve your standard of living, you need to make more money so you can buy better things, like housing, healthcare, leisure, etc. Once you get the best education you can, and maximize your hours, you are then limited in how much you can make based on how much you can produce, your output. How do you increase your output? Through better technology. At an economy-wide level, therefore, if we’re not introducing new technology, we will eventually hit a maximum output level we cannot push beyond.

This is a counterintuitive and profound conclusion that I think gets overlooked because we take technological progression for granted. Science and technology don’t just progress on their own. There were many periods in history where they essentially completely stagnated in parts of the world. That’s because it takes considerable effort, education, organization, and money to advance science and technology. Without enough of any one of those ingredients, it doesn’t happen.

And, if technological progression can go slower, perhaps it could also go faster, by better attuning the level of effort, education, organization, and money. For example, I’ve been arguing in this blog that the political debate now around science funding has an incredible amount of status quo bias embedded in it. I believe reducing funding will certainly slow us down, but I also believe science funding was already way too low, perhaps 3X below optimal levels.

Put another way, I think a primary goal of government and society should be to increase our collective standard of living. You simply can’t do that long-term without technological progression.

A couple of quick critiques I may tackle more in-depth in the future. Some people are worried that we’re just producing more stuff for the sake of producing more stuff, and that’s not really increasing the standard of living. First, with technological progression, the stuff gets both better and cheaper, and that is meaningful, for example, take medicines. Better medicines mean better health spans, and cheaper medicines mean more access to medicine. Second, people buy things, for the most part, on their own free will, and maybe people do want more stuff, and that’s not a bad thing in and of itself, as long as we can control for the negative externalities. Third, controlling for those negative externalities, like combating climate change effectively, actually requires new science and technology.

Another common critique is that technology causes problems, for example, privacy problems. As someone who started a privacy-focused company, I’ve been holding this position for decades and continue to do so. But we shouldn’t throw the baby out with the bathwater. We need to do a more effective job regulating technology without slowing down its progression.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

I’ve been having some cognitive dissonance about blogging. On the one hand, I don’t believe in doing things primarily for legacy purposes, since in the long arc of history, hardly anything is likely to be remembered or matter that much, and I won’t be here regardless. On the other hand, I also don’t like spending a lot of my writing time on crafting for the ephemeral—like a social media post—because it seems that same writing time could be spent on developing something more evergreen.

I stopped blogging a decade ago following that same logic, focusing my writing time on books instead, which are arguably more evergreen than blogging. But, I’m obviously back here blogging again, and with that context, here are some dissonant thoughts I’m struggling with:

Is the chasing of more evergreen content just a disguised form of chasing legacy?

I think not because long-term legacy is about after you’re dead, and I’m not looking for something that will last that long. I’m more looking to avoid the fate of most content that has a half-life of one day, such that my writing can have more of an impact in my lifetime. That is, it’s more about maximizing the amount of impact per unit time of writing than any long-term remembrance.

Is there more value in more ephemeral content than I previously thought?

I’m coming to believe yes, which is why I’ve started blogging again, despite most blog posts still having that short half-life I’m trying to avoid. Specifically, I think there can be cumulative value in more ephemeral content when it:

* Builds to something like a movement of people behind a thematic idea that can spring into action collectively at some point in the future, which is also why I started this up again on an email-first (push) platform.

* Helps craft a more persuasive or resonant argument, given feedback from smaller posts, such as how comedians build up their comedy specials through lots of trial and error.

This last piece reminds me of Grounddog Day (the movie) where he keeps revising his day to achieve the perfect day, much like you can try to keep refining your argument until it perfectly resonates.

In any case, it’s hard to achieve occasional evergreen content if you don’t have an audience to seed it with and if you don’t have a fantastic set of editors to help craft it (which hardly anyone does except in a professional context). That is, putting out more ephemeral content can be seen as part of the process of putting out quality evergreen content, both in terms of increasing its quality (from continuous feedback) and in terms of increasing its reach (from continuous audience building).

Should I spend as much time editing these posts as I do?

Probably not, given that it is very rare for one of these posts to go viral / become evergreen. The problem is, I like editing. However, trying to stick roughly to a posting frequency and using formats like this one (Q/A headings) really helps me avoid my over-editing tendencies.

What is the relationship between blogging frequency and evergreen probability?

There’s no doubt that some blog posts are evergreen in that people refer back to them years after they were written (assuming they are still accessible). Does the probability of becoming evergreen have any relationship to the frequency of posting? You can make compelling arguments for both sides:

* If you post more, you have more chances to go viral, and most people in a viral situation don’t know your other posts anyway, so the frequency isn’t seemingly inhibiting any particular post from going viral.

* If you post less, you will likely spend more time crafting each post, increasing each post’s quality, and thus increasing its chances of virality, which I think (though I am not sure) is a necessary condition of evergreenness.

My current sense is that if you post daily, then you are unlikely to be creating evergreen content in those posts. Still, you can nevertheless have a significant impact (and perhaps more) by being top of mind in a faster-growing audience and influencing the collective conversation through that larger audience more frequently. That’s because there does seem to be a direct relationship between posting frequency and audience growth. However, posting daily is a full-time job in and of itself, and one I can’t personally do (since I already have a full-time job) and one I don’t want to do (since I don’t like being held to a schedule and also like editing/crafting sentences too much).

So, yes, I do think there is a relationship between frequency and evergreenness, and there is probably some sweetspot in the middle between weekly and monthly that maximizes your evergreen chances. You need to be top of mind enough to retain and build an audience (including through recommendations), you need enough posting to get thorough feedback to improve quality, and you need enough time with each post to get it to a decent quality in the first place. The full-timers also have other options, like daily musings paired with more edited weekly or monthly posts.

But, isn’t there a conflict between maximizing audience size and maximizing evergreen probability?

Yes, I think there is. If you want to maximize audience size, the optimal post frequency is at least daily, vastly increasing the surface area with which your audience can grow, relative to a weekly posting schedule (or even less). But, that frequency, as previously stated, is not the optimal frequency for optimizing the probability of producing evergreen content.

So, you have a tradeoff—audience size vs. evergreen probability. And it is a deeper tradeoff than just frequency, since I also think the kind of content that best grows audience size is much shallower than the kind of content that is more likely to go evergreen.

As noted, you can relax this tradeoff with more time input, which I don’t have. So, for right now, acknowledging this tradeoff, I think I’m going to stick to a few, deeper posts a month, maybe edited a bit less though. I’d rather build a tighter audience that wants to engage more deeply in ideas that can last than a larger audience that wants to consume shallower content that is more ephemeral. I hope you agree and I could also use more feedback!

Thanks for reading. Subscribe for free to receive new posts or get the podcast.

For example, whatever you choose now will send you down a path that will shape your future decisions, a mental model known as path dependence. That is, your future path is both influenced and limited by your past decisions. If you enroll your child in a language immersion school, you significantly increase the chances that they will move to an area of the world where that language is spoken, decades from now. Maybe you're OK with that possible path, but you should at least be aware of it.

In a real sense, you aren’t just trading the immediate outcomes of one option for another, but one future path for another. From that perspective, you can consider path-related dimensions, such as how the various options vary in terms of future optionality or reversibility. It’s not that more optionality or reversibility is always better, as often they come at a cost, but just that these less-obvious trade-offs should be considered.

A related, less-obvious trade-off involves opportunity cost. If you pick from the options in front of you, what other opportunities are you forgoing? By explicitly asking this question, more options might reveal themselves. And, you should also always ask specifically what opportunities you are forgoing by deliberating on the decision. Sometimes waiting too long means you miss out on the best option; other times, putting off the decision means more options will emerge. Again, one side isn’t always better, since every situation is different. But the opportunity costs, including from waiting, should be explored.

Another common but less-obvious trade-off concerns the different types of errors you can make in a decision. From our book Super Thinking:

[C]onsider a mammogram, a medical test used in the diagnosis of breast cancer. You might think a test like this has two possible results: positive or negative. But really a mammogram has four possible outcomes…the two possible outcomes you immediately think of are when the test is right, the true positive and the true negative; the other two outcomes occur when the test is wrong, the false positive and the false negative.

These error models occur well beyond statistics, in any system where judgments are made. Your email spam filter is a good example. Recently our spam filters flagged an email with photos of our new niece as spam (false positive). And actual spam messages still occasionally make it through our spam filters (false negatives). Because making each type of error has consequences, systems need to be designed with these consequences in mind.

That is, you have to make decisions on the trade-off between the different types of error, recognizing that some errors are inevitable. For instance, the U.S. legal system is supposed to require proof beyond a reasonable doubt for criminal convictions. This is a conscious trade-off favoring false negatives (letting criminals go free) over false positives (wrongly convicting people of crimes).

To uncover other less obvious trade-offs, you can brainstorm and explicitly list out (for example, in a spreadsheet) the more subtle dimensions on which options differ. Think of a comparison shopping page that compares numerous features and benefits. The obvious ones — such as cost — may immediately come to mind, but others may take time to surface, like how choosing one option might impact your personal quality of life in the future.

The point is not to overthink decisions, but to be conscious about inherent trade-offs, especially the less obvious yet consequential ones. Just as I think you should take the time to write out assumptions explicitly, I also believe you should do the same for trade-offs.

See other Rules.

The Transporter (2002)

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

At the same time, we know a lot of people do want to use AI if it is actually useful and private (myself included). Our private chat service at duck.ai has the highest satisfaction ratings we’ve seen in a new feature, and Search Assist, our equivalent of Google’s AI Overviews, is currently our highest-rated search feature.

Our goal with Search Assist is to improve search results, not to push AI. We’ve been continually evolving it in response to feedback, seeking better UX, and here’s how we’re thinking about that UX right now, relative to Google’s AI Overviews:

* You can turn Search Assist off or turn it up—your preference.

* When it does show, Search Assist keeps vertical space to a minimum so you can still easily get to other search results.

* The initial Search Assist summary is intentionally short, usually two brief sentences. This brevity keeps hallucinations to a minimum since less text means less surface area to make things up. You also get the complete thought without having to click anything. However, you can still click for a fuller explanation. This is a subtle but important distinction: clicking more on Google is getting more of the same, longer summary; clicking more on DuckDuckGo is getting a new, completely independent generation.

* You can use the Assist button to either generate an answer on demand if one isn’t showing automatically, or collapse an answer that is showing to zero vertical space.

* When we don’t think a Search Assist answer is better than the other results, we don’t show it on top. Instead, we’ll show it in the middle, on the bottom, or not at all. This flexibility enables a more fine-tuned UX.

* All source links are always visible, not hidden behind any clicks or separated from the answer. We’ve also been keeping sources to a minimum (usually two) to both increase answer quality (since LLMs can get confused with a lot of overlapping information) and increase source engagement.

* Our thumbs up/down is also visible by default, not hidden behind a click. This anonymous feedback is extremely valuable to us as a primary signal to help us find ways to improve.

* To generate these answers, we have a separate search crawling bot for Search Assist answers called DuckAssistBot that respects robots.txt headers. By separating DuckAssistBot from our normal DuckDuckBot, and unlike Google, we allow publishers to opt-out of just our Search Assist feature.

* Like all of our search results, Search Assist is anonymous. We crawl sites and generate answers on your behalf, not exposing your personal information in the process or storing it ourselves.

I’m sure our Search Assist UX will evolve further from here as we’re actively working on it every day. For example, we’re working now on making it easier to enter a follow-up question in-line, which allows you to more easily stay in context when entering your question.

That is to say, the above is not set in stone and the answers for these queries will surely change over time, but I hope this post helps illustrate how we’re approaching Search Assist to be consistent with our overall approach to AI to be useful, private, and optional. Feedback is welcomed!

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

Gross Domestic Product (GDP) per capita, that is, the average economic output per person in a country, is often used as a proxy metric to compare the standard of living across countries. Of course, this proxy metric, being solely about money, doesn’t directly capture non-monetary aspects of standard of living associated with quality of life or well-being. However, most of these non-monetary aspects are tightly correlated with GDP per capita, rendering it a reasonable proxy.

Our World in Data features numerous plots of such measures against GDP per capita. Here are a few of the ones people tend to care about most:

These measures are clearly tightly correlated to GDP per capita, as are common aggregate measures such as the UN’s Human Development Index that combines lifespan, education levels, and GDP per capita.

These tight correlations are somewhat intuitive because GDP per capita by definition means more money to buy things, and that includes buying more healthcare, education, leisure time, and luxiries, which one would expect to be correlated to healthspan, life satisfaction, and other measures of quality of life and well-being.

Nevertheless, at some level of GDP per capita, you reach diminishing returns for a given measure, and we would then expect the corellation to cease for that measure. For example, here is access to clean (“improved”) water sources, which maxxes out at medium incomes after you reach 100% since you can’t go higher than 100% on this measure.

However, we haven’t seen that yet for the most important measures like life expectancy, the poverty line, and self-reported life satisfaction. All of those can go higher still, and are expected to do so with further increases to GDP per capita, certainly for lower GDP-per-capita countries (climbing up the existing curve) but also for the U.S. (at or near the fronteir). In other words, with enough broad-based increases in income, many are lifted out of poverty, the middle class is more able to afford much of the current luxury and leisure of the rich, and the rich gets access to whatever emerges from new cutting-edge (and expensive) science and technology.

We should continue to watch and ensure these correlations remain tight. But as they remain tight, I think it is safe to say right now that we would expect increases in standard of living to be tightly correlated with increasing GDP per capita. While there are other necessary conditions like maintaining rule of law, broadly giving people more money to buy better healthcare, education, and upgraded leisure time should increase standard of living. That part is pretty intuitive.

What’s not intuitive is how to do so. You can’t just print money, because that results in inflation. It has to be increases in real income, that is, after inflation. So, how do you do that? If you’re a country where a large % of the working-able population doesn’t currently have a job, the easiest way is to find those people jobs. Unfortunately, that won’t work for the U.S. anymore since most everyone who wants a job has a job. It worked for a while through the 1960s, 70s, and 80s as ever greater %s of women entered the workforce, but then plateaued in the 1990s.

You could try to get people with jobs to work more hours (and therefore make more money per person), but that also doesn’t work for the U.S. since we already work a lot relative to other frontier countries, and as people get more money they seem to want to work less, not more. For example, in the U.S. we’re working a lot less hours per worker than we did in 1950, let alone 1870. This makes intuitive sense since quality of life and well-being can’t get to the highest levels if you’re working all of the time.

That leaves upgrading the jobs people already have in the form of higher income for the same amount of hours worked. And this means, by definition, increasing labor productivity, which is the amount of goods and services produced per hour of labor. To pay people more without triggering inflation, they also have to produce more output. That’s the counterintuitive piece and also it is our biggest opportunity for higher GDP per capita, and therefore higher standard of living.

OK, but how do you increase labor productivity? I’m glad you asked. There are three primary ways, but only one has unbounded upside. Can you guess what it is?

First, you can educate your workforce more, providing them with, on average, better skills to produce higher quality output per hour worked, a.k.a. investment in human capital. The U.S. is currently going in the wrong direction on this front when you look at the % of recent high-school graduates enrolled in “tertiary” education (which includes vocational programs).

If we had continued to make steady progress through the 2010s and 2020s, we would be headed towards diminishing returns on this front. While it will surely be good to increase this further to get those gains—and there is more you can do than just tertiary education such as on-the-job training—like we saw earlier with access to clean water, there is effectively a max out point for education in terms of its effect on GDP per capita. Think of a point in the future where everyone who is willing and able has a college degree, or even a graduate degree.

Second, you can buy your workforce more tools, equipment, and facilities to do their job more efficiently, a.k.a. investment in physical capital. This isn’t inventing new technology, just spending more money to get workers access to the best existing technology. Again, you clearly reach diminishing returns here too, that is, another max out point, as you buy everyone the best tech. Think of the point where everyone has a MacBook Pro with dual Studio Displays—or whatever the equivalent is in their job—to maximize their productivity.

Third, and the only way that doesn’t have a max out point, is to invent new technology that enables workers to do more per hour. These are better tools than the existing tools on the market. Think of upgrading to the latest software version with updated features that make you a bit more productive. Or, more broad-based:

Think of how worker productivity increased in construction with the introduction of power tools and heavy machinery or in offices with the introduction of computers and the Internet. We need more of these, many times over: true leaps forward in technology applications that will dramatically increase our worker productivity. (The Great Race)

AI is likely one of these leaps, but by investing much more in basic research we can make higher labor productivity growth more continuous instead of the bumpy road it has recently been on.

These leaps don’t come out of nowhere. They require decades of investment in research, and that investment requires a decent level of government investment at the earliest stages. This was the case for AI, as it was for the Internet, and as it is for life-saving drugs.

This is actually good news, since it means we have a lever to pull to increase labor productivity that we’re not currently fully pulling: increase federal investment in basic research. The level we’ve ended at today is somewhat arbitrary, an output of a political process that wasn’t focused on increasing standard of living. In any case, I estimate at the bottom of this post that we’re off by about 3X.

If you want another view on this topic, here is a good post from the International Monetary Fund (IMF):

[I[mprovements in living standards must come from growth in TFP [Total Factor Productivity] over the long run. This is because living standards are measured as income per person—so an economy cannot raise them simply by adding more and more people to its workforce.

Meanwhile, economists have amassed lots of evidence that investments in capital have diminishing returns. This leaves TFP advancement as the only possible source of sustained growth in income per person, as Robert Solow, the late Nobel laureate, first showed in a 1957 paper.

TFP growth is also the answer to those who say that continued economic growth will one day exhaust our planet’s finite resources. When TFP improves, it allows us to maintain or increase living standards while conserving resources, including natural resources such as the climate and our biosphere.

Or, as Paul Krugmam put it even more succinctly in his 1990 book The Age of Diminished Expectations:

Productivity isn’t everything, but, in the long run, it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker. —Paul Krugman

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

Of course, I could tell the chatbot every time to keep its replies short and emoji-free, but pasting that constantly is enough friction that I rarely bother. OpenAI and Anthropic do offer customization options in their settings, yet those options are buried and feature intimidating blank text boxes, such that I highly suspect most people never touch them.

Recently, we’ve been considering this issue in the context of duck.ai. I’m sure what we’ll do here will continue to evolve as we get feedback, but to get started we’ve just introduced a much easier-to-find customization dialog. Not only does it make the responses feel better, it can make the actual content significantly better as well.

As you can see in the video, it provides customization guidance through drop-downs and fields, including options to customize:

* The tone of responses

* The length of responses

* Whether the chatbot should ask clarifying questions

* The role of the chatbot (for example, teacher)

* Your role (for example, student)

* The nickname of the chatbot

* Your nickname

All fields are optional, and you can also add additional info if desired, as well as inspect what the instructions will look like in aggregate. If you select role(s), then there are detailed instructions that get created specifically for those. Here’s an example using the ‘Tech support specialist’ role, which asks you clarifying questions to drill down faster to a solution vs. the more generic (and lengthier) default response.

Customized response:

Generic response:

All of this works through the “system prompt.” In an excellent post titled AI Horseless Carriages, Pete Koomen explains system prompts:

LLM providers like OpenAI and Anthropic have adopted a convention to help make prompt writing easier: they split the prompt into two components: a System Prompt and a User Prompt, so named because in many API applications the app developers write the System Prompt and the user writes the User Prompt.

The System Prompt explains to the model how to accomplish a particular set of tasks, and is re-used over and over again. The User Prompt describes a specific task to be done.

When you set the duck.ai customization options, the instructions that are created are appended to the default system prompt, which is repeated (in the background) when you start a new conversation. That is, the instructions will apply to the current conversation as well as subsequent ones, until you change them again.

Like everything we do at DuckDuckGo, these system prompt tweaks are also private. They are stored locally on your device only, along with your recent chats (if you choose to save them). When we ultimately add an optional ability to sync settings and chats across devices, it will be part of our end-to-end encrypted sync service, which DuckDuckGo cannot decrypt. And Duck.ai itself anonymizes chats to all model providers, doesn’t store chats itself, and ensures your chats aren’t used for AI training. More at the Duck.ai Privacy Policy.

Our approach to AI is to make features that are useful, private, and optional. We believe these new duck.ai customization options tick all three boxes, but please try them out and let us know what you think. As always, please feel free to leave comments here. However, the best method for sharing feedback about duck.ai is to do so directly through the product, as it will then be shared with the entire team automatically.

Thanks for reading. Subscribe for free to receive new posts or get the podcast.

However, this debate raises an even bigger question: How do we achieve sustained higher productivity growth forever, not just in occasional blips and bumps? I believe this is the most important economic question because higher productivity growth is the key factor that drives long-term higher standards of living. Here’s a concise, understandable explanation from the Bureau of Labor Statistics as to why:

How can we achieve a higher standard of living? One way might simply be to work more, trading some free time for more income. Although working more will increase how much we can produce and purchase, are we better off? Not necessarily. Only if we increase our efficiency—by producing more goods and services without increasing the number of hours we work—can we be sure to increase our standard of living.

To continuously produce more without increasing the number of hours worked per person, we need to continually develop better tools. To continually develop better tools, we need access to increasingly better science and technology. To get access to increasingly better science and technology, we need sustained higher investment in basic research.

Think of how worker productivity increased in construction with the introduction of power tools and heavy machinery or in offices with the introduction of computers and the Internet. We need more of these, many times over: true leaps forward in technology applications that will dramatically increase our worker productivity. (The Great Race)

That is, to ensure higher productivity growth, we must continually invest sufficiently in the next set of technologies that will generate and sustain these higher productivity levels. That requires increased investment in basic research. But, why can’t private industry do it all?

The private sector…is excellent at taking established scientific breakthroughs and turning them into products over a few years. That’s because there is a clear profit motive in doing so. However, it is not as great at coming up with those scientific breakthroughs in the first place or commercializing them on much longer timescales like decades, where the profit motive is significantly reduced. This activity still generally involves some government-funded research in the early stages. (The Great Race)

Yes, this includes AI too. A good post on this by Mark Riedl titled Visualizing the Influence of Federal Funding on the AI Boom takes seven key papers in AI, including Attention is All You Need (2017), and then traces which of their references explicitly acknowledge federal funding.

~18% of papers referenced by these 7 industry papers have acknowledged US federal funding.

~24% of papers referenced have US university authors.

~20% of papers referenced are industry lab-authored.

~42% of papers referenced do not have any industry authors.

The AI boom did not happen in an industry vacuum. As with all research, it was an accumulation of knowledge, much of which generated in university settings. There is a growing narrative that academia isn’t important to AI anymore and US federal funding has no role in the AI boom. It’s more correct to say that the AI boom could not have happened without US federal funding.

The same was true with the Internet boom. The same is also true in healthcare, for example, tracing federal funding through the most transformational drugs.

And yet, as I previously explored, science funding was already way too low before recent cuts. Quite simply, since the 1960s, we haven’t invested sufficiently in basic research to create sustained higher productivity. But we could change that.

And, as I've also previously detailed, this should be a no-brainer since, if done right, it literally pays for itself by expanding the economy, generating higher tax revenues, and ultimately lessening the debt-to-GDP ratio.

So while economists debate whether AI will boost productivity by 0.5% or 1.5% for the next decade, we should be asking: How can we better invest in basic research today to ensure we’re still growing faster in 2050? Short-term productivity bumps are a boon. But, if we want sustained productivity gains, we need sustained productivity investment.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

At the same time, I believe the AI backlash is real and growing, which is why I’m thinking and writing about it, and why we’re designing all our AI features to be useful, private, and optional. And, the backlash is real for good reason. AI poses a wide range of risks, including massive job displacement, extensive privacy concerns, and, at the extreme, existential risks.

That’s why this “pause” is particularly dangerous. I’m not taking a position here on which risks should be regulated, when, or how. More on that in future posts. But I am saying that AI will require at least some well-crafted regulation to address some of its risks over the next ten years, and yet Congress has proven incapable of taking action. The states, on the other hand, do take action.

Look no further than privacy law as a close parallel. It’s 2025, and the United States still lacks a comprehensive federal privacy law. The International Association of Privacy Professionals (IAPP) now tracks 144 countries with such laws (as of Jan 2025). The U.S. is a clear outlier:

The most populous countries without a comprehensive national privacy law include the U.S., Pakistan, Bangladesh, Iran, and Iraq.

This isn’t for lack of trying. Numerous bills have been proposed, and many hearings have been held, yet nothing has even come close to passing, not even after Snowden or Cambridge Analytica. Unrelated to privacy, Congress has proven unable to legislate effectively, and while we should work to fix that independently, we can’t wait for it.

Meanwhile, IAPP tracks 19 states that have managed to pass general privacy laws to protect consumers to some extent, including the two most populous states, California and Texas. Despite fears that a “patchwork” of state laws would wreak havoc on innovation by going too far, they haven’t. Innovation hasn’t stalled, and neither have big-tech privacy violations. That’s because state privacy laws, while better than nothing, in my opinion, don’t nearly go far enough, which is why we (DuckDuckGo) still need to develop dozens of overlapping protections to keep consumers safe online. Meta’s latest AI-chatbot leak foreshadows a bleak AI-privacy future if there are literally no regulations in sight.

State laws also provide Congress with both a blueprint for action and further incentive to enact laws. Nothing prevents a future AI bill from overriding (preempting) state AI laws. Of course, for that to happen, Congress would need to pass general tech legislation. I would love to witness that and have been working to help make it happen, but I am also realistic about Congress’s current capacity to regulate tech.

Finally, the current proposal would seemingly preempt the most protective provisions of existing state privacy laws. That would be a giant step backward for online privacy. We helped pioneer Global Privacy Control, an opt-out signal that is on by default in our browser and extension, which has legal effect in California and other jurisdictions. Senator Maria Cantwell, ranking member on the Senate Commerce Committee, notes that the bill would nullify provisions of many state privacy laws that “give consumers the right to opt-out of profiling.”

In the last 25 years, states filled the privacy law vacuum left by Congress; let them do the same for AI. We should not silence states from protecting their citizens from dangerous new risks for a decade. And, if Congress gets its act together, then great—those future bills can preempt any conflicting state provisions.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

I’ve seen this pattern repeatedly: teams spend hours debating the details of Option A versus Option B, only to discover they never considered Option C—or that their entire framing of the problem was based on outdated or misaligned assumptions. Generally, in these situations, or for any significant decision, it’s worth taking some time to literally enumerate the relevant assumptions and why they should be relied upon in this particular situation.

This process of enumerating assumptions is almost always illuminating in some way. Often, it reveals that particular assumptions need further de-risking. From our book Super Thinking:

You can de-risk anything: a policy idea, a vacation plan, a workout routine. When de-risking, you want to test assumptions quickly and easily. Take a vacation plan. Assumptions could be around cost (I can afford this vacation), satisfaction (I will enjoy this vacation), coordination (my relatives can join me on this vacation), etc. Here, de-risking is as easy as doing a few minutes of online research, reading reviews, and sending an email to your relatives.

Or, in the context of a startup idea, also from our book:

* My team can build our product—We have the right number and type of engineers; our engineers have the right expertise; our product can be built in a reasonable amount of time; etc.

* People will want our product—Our product solves the problem we think it does; our product is simple enough to use; our product has the critical features needed for success; etc.

* Our product will generate profit—We can charge more for our product than it costs to make and market it; we have good messaging to market our product; we can sell enough of our product to cover our fixed costs; etc.

* We will be able to fend off competitors—We can protect our intellectual property; we are doing something that is difficult to copy; we can build a trusted brand; etc.

* The market is large enough for a long-term business opportunity—There are enough people out there who will want to buy our product; the market for our product is growing rapidly; the bigger we get, the more profit we can make; etc.

Enumerating assumptions can seem pedantic, but I've found it extremely helpful because it clarifies your argument, much like writing a blog post or explaining something to someone in real-time. It helps ensure you’re drawing a logical conclusion from your assumptions.

A great way to help identify potentially shaky assumptions is to conduct a premortem. Here’s the template we use at DuckDuckGo for new project premortems:

Premortem

Many projects fail to meet their success criteria, so take some time to be pessimistic and ask questions:

* What are the key risks to this project and how can they be mitigated?

* What could slow down this project and how can we prevent that from happening?

* How might our actions in this project negatively affect user trust or be misunderstood?

The goal is to uncover problems or blindspots and then decide how to address them up front, such as by starting with the most uncertain part. If a project is destined to fail, failing fast is a success.

This principle extends beyond individual and team decisions to broader discourse. I'm constantly frustrated by political and policy debates where participants talk past each other—often because they're operating from different underlying assumptions or facts. If it is a good faith debate, then there should be the opportunity to enumerate and drill down on those underlying assumptions and facts, which can then help clarify exactly where the disagreement is occurring. For example, it might turn on just one misaligned assumption or fact among many. If that’s the case, a determination can be made on what specific evidence would be compelling enough to achieve alignment.

Rule #2—There are always underlying assumptions—is universal. You're always making assumptions. The key is examining them before they lead you astray.

See other Rules.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

* Hope—yes, we can agree on some important things.

* Despair—uh oh, our widespread agreement isn’t translating into actual change.

In other words, the existence of issues with durable, supermajority agreement, which nevertheless never materializes into change, is a strong indicator that we need some significant structural reform. I have ideas for that, but first, let me try to convince you that these magical issues exist!

1. Congressional Term Limits

From a 2023 Pew Research report:

Term limits for members of Congress are widely popular with both Republicans and Republican-leaning independents (90%) and Democrats and Democratic leaners (86%).

This isn’t new. A 2013 Gallup poll showed similar results, noting that these results were also “similar to those from 1994 to 1996 Gallup polls.”

2. Legalizing Marijuana

From a 2023 Gallup poll, which has been polling this issue for decades:

For the second straight year, majority support for legalization is found among all major subgroups, including by age, political party and ideology.

3. Digital Privacy

From a 2023 Pew Research report:

A majority of Democrats and Republicans say there should be more government regulation for how companies treat users’ personal information (78% vs. 68%).

They also note that “[t]hese findings are largely on par with a 2019 Center survey that showed strong support for increased regulations across parties.”

4. Limiting Money in Campaigns

From a 2024 American Promise poll:

“A proposed constitutional amendment would allow Congress and the states to reasonably regulate and limit money in our campaigns and elections. Would you support or oppose this amendment?”

5. Universal Background Checks

From a 2023 Fox News poll:

87% of Americans support requiring criminal background checks on all gun buyers, including 83% support from gun owning households.

This is a long standing result, for example this 2019 Quinnipiac poll:

Voters support 93-6 percent "requiring background checks for all gun buyers." Support is 89-10 percent among Republicans and 87-12 percent among gun owners. Support for universal background checks has ranged from 88 to 97 percent in every Quinnipiac University poll since February 2013, in the wake of the Sandy Hook massacre.

6. Negotiating Medicare Prescription Drug Prices

From a 2024 KFF poll:

A large majority (85%) of voters say they support allowing the federal government to negotiate the price of some prescription drugs for people with Medicare. This includes at least three quarters of Republican (77%), independent (89%) and Democratic (92%) voters.

7. Congressional Stock Trading Ban

From a 2023 Maryland School of Public Policy study:

Overwhelming bipartisan majorities favor prohibiting stock-trading in individual companies by Members of Congress (86%, Republicans 87%, Democrats 88%, independents 81%)

These issues aren’t anomalies.

I stopped at seven issues, but there are many more issues like these, for instance, increasing veterans’ benefits (similarly supported by Pew/Gallup polling), and I’m sure there will be even more in the future. I recognize that some issues appear bipartisan at times, and then once a political party takes up the cause, they immediately become more partisan. But gun control, health care, and drugs, for example, have all been politicized in this manner. Yet, as noted above, supermajorities exist for specifically reasonable policies, including universal background checks, negotiating Medicare drug prices, and federal legalization of marijuana.

Each issue has its own set of reasons for persisting stubbornly without change, typically a combination of special interests, regulatory capture, and general congressional dysfunction.

I contend that, fundamentally, we, the people, need another path to change. (Incidentally, some original printed copies of the U.S. Constitution had a comma after we.)

How can “we the people” change things?

“We the people” leads off our Constitution, and yet it doesn’t provide a direct way for us to change it. I think it should. It would still have to be very hard to change the Constitution, but I think there should be a citizen path to do so, not just one that goes through elected officials.

Sixteen states already allow for totally citizen-initiated state-level constitutional amendments. Typically, a threshold of signatures (between 5-15% of the votes cast in a recent election) needs to be achieved to get on the ballot, and then a direct vote happens where, if a threshold is reached (50-60%), then the amendment is ratified.

I envision a similar process, but scaled up to the national level: Something like a signature requirement in enough states triggers getting a binding resolution on a federal election ballot. Then, if a supermajority of citizens in enough states vote for it, it is automatically ratified without additional involvement from Congress or state legislatures.

Many people have proposed similar things in the past, and I plan to examine them and put forward my thoughts on a more concrete proposal in the future. As in the past, I don’t expect anything actually to happen, but it at least helps clarify my thinking, and you never know— maybe we’re approaching a moment when something can happen.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

That said, I believe it is clear that even in a net-positive scenario, many jobs will be displaced as new ones are created. For example, the World Economic Forum predicts by 2030 the creation of 170 million new jobs worldwide.

However, this growth is expected to be offset by the displacement of the equivalent of 8% (or 92 million) of current jobs, resulting in net growth of 7% of total employment, or 78 million jobs.

Supposing that this is true and a similar scenario unfolds in the U.S., I still think this amount of displacement is likely to have significant negative consequences for the displaced. That is, the displaced individuals are unlikely to be the same individuals who secure the latest jobs, and this will leave many with worse jobs, or in a place with no job at all. For example, are cashiers and truck drivers going to get new, fancy AI jobs in another industry, without significant help to do so? I highly doubt it.

So, recognizing that significant job displacement is on the horizon for many industries, regardless of how the overall total nets out, it would be ideal to provide affected individuals with a softer landing than we’ve managed to do in the past. Unfortunately, the likely policy outcome is that we do absolutely nothing until there is a considerable backlash to AI, and even then, we probably do nothing. The backlash would have to be big enough that politicians cannot ignore it, or it is seen as a political opportunity, such that it becomes a platform for elections.

In a recent post titled Will the AI backlash spill into the streets?, I made the case that, among the numerous anticipated AI harms to society, job displacement stands alone with the potential to significantly spill over into the streets in the form of sustained protests. I’m not saying that it is likely to happen, and I hope it doesn’t, but I also think we could increase the probability of preventing it if we had a clearer picture of what we’re trying to avoid. At the end of that last post, I pondered about historical analogs:

Recent protest movements seem more one-sided politically (e.g., climate change, Occupy Wall Street, Tea Party, etc.). Mid-century protests were arguably similar (civil rights movement, Vietnam War protests, etc.), though they were sustained for much longer and ultimately swayed public opinion and accelerated change. A better parallel here, though, would be something that is clearly bipartisan from the start, more squarely on an economic issue, and resulted in swift reforms.

This line of reasoning led me to the Yellow Vests protests in France from 2018 to 2020, which I’ve come to believe could provide a decent historical analog of what could happen if AI job displacement reaches a critical mass in the coming years. It’s recent, directly related to economic insecurity, and created policy changes, though significant disruption and violence as well. Ideally, we’d get the policy changes without the widespread disruption and violence.

Now I am not French, and I did not personally live through this protest movement. I do remember watching and reading coverage about it at the time, but that was the extent of my knowledge before I delved more deeply into the subject over the past week. (Below, facts are from Wikipedia unless otherwise noted.)

Subscribe for free to receive new posts.

What were the yellow vest protests about?

They began as a reaction to a fuel tax increase, but quickly expanded to a broader reaction against economic insecurity. The movement never had clear leaders, nor was it tied to a particular political party. One widely circulated list of 42 demands compiled from online surveys went viral, including many specific economic demands such as rolling back the fuel tax, implementing minimum pensions, indexing wages to inflation, and providing jobs for the unemployed. It was somewhat disjointed, as it also demanded education and immigration reforms, among others; however, the central theme centered on economics and jobs.

How many people participated?

Approximately 3 million unique protesters participated, which is roughly 4% of France’s population of nearly 69 million. The U.S. has a population of around 341 million, so an equivalent number of unique protesters would be approximately 14 million. That’s the same order of magnitude as the George Floyd protests.

The peak protest time was Nov-Dec 2018, with the highest single-day attendance of about 287,000; the U.S. equivalent would be about 1.5 million. Protests occurred across the entire country. After the initial period, there were still continual weekend demonstrations for about a year and a half in total, until the pandemic essentially brought them to an end.

Did the protests turn violent?

Unfortunately, yes. At least a dozen people died in the protests, with another five people losing their hands as a result of police grenades, and a reported twenty-three people losing their eyesight. More minor injuries were in the 1,000s for both protestors and police as a result of clashes.

How much public support did it have?

Very high. Public support for the movement reached a high of 75% in the initial phase, and then it declined over time.

What was the government’s reaction?

On December 10, 2018, about a month in, French President Macron gave a speech to the public, pledging a €100 increase in the monthly minimum wage, among other reforms. The speech was viewed live by more than 23 million people, or approximately a third of the entire population. The U.S. equivalent would be around 80-85 million. Concessions from the government ultimately totaled about €17 billion, which, converted to USD and scaled up to the U.S. economy, would be about $150 billion.

What was the demographic makeup of the protestors?

An academic study of the protesters found that approximately 47% were first-time protesters. The median income of the protesters was about 30% less than the country’s median income. Participation cut across the political spectrum, and the researchers concluded:

In short, this is indeed a revolt of the ‘people’…in the sense of the working class and the lower-middle class, people on modest incomes. Consequently, in several ways the gilets jaunes movement presents a different kind of challenge from the social movements of recent decades. In addition to its size, the strong presence of employees, people of modest educational qualifications and first-time demonstrators, and, above all, the diversity of their relationship to politics and their declared party preferences, have made roundabouts and tollbooths meeting places for a France that is not used to taking over public spaces and speaking out, as well as places for the exchange of ideas and the construction of collectives in forms rarely seen in previous mobilizations.

Why did they wear yellow vests?

As noted, the movement originated as a response to a proposed fuel tax increase. Independently, a different French law requires motorists to have a yellow vest in their car to wear in case of an emergency, so many motorists had them readily available. A petition against the tax went viral, and then some associated viral videos called to “block all roads” and included the idea of using the yellow vests.

What are some parallels to AI?

First, while the backlash to AI is just getting started, as I noted in my previous post, the ingredients are there for a potential future movement that could match a similar broad-based revolt that transcends political parties, if significant negative job impacts accumulate over several years:

* Cuts span industries, so outrage lands on both parties.

* Every income bracket—from cashiers to coders—takes a hit.

* Sudden, deep job cuts risk recession and years of high unemployment.

To be clear, we’re not there yet, and may never get there. Job losses may not materialize. Or, they may unfold over a much longer period of time that doesn’t lend itself to banding together across industries. For example, current AI labor organizing remains limited to specific sectors like entertainment and dockworking. But, if AI-driven job displacement accelerates across multiple industries simultaneously—affecting many millions of workers over the next 3-5 years—the conditions could ripen for a Yellow Vest-style eruption.

Second, as the Yellow Vest movement showed, it doesn’t actually have to be sparked by a sharp increase in job losses. Instead, if there is enough downward pressure on wages, unrest resulting from that pressure can build up until a critical mass is reached. In other words, that could still happen even if AI diffusion takes many years to touch many industries, as long as the impacts and resentment are sustained.

Third, if a critical mass is reached, that’s essentially a powder keg waiting to explode, which means any event could be the proximate cause that sparks it. Therefore, like the Yellow Vests movement, I could see a similar AI movement happening in a decentralized fashion. That is, one that begins with online viral calls to action, which then spills out into the streets.

What’s different?

One difference is that the Yellow-Vest movement was primarily rooted in the lower income brackets, whereas AI has the potential to draw in affected people from across the income brackets, as noted above.

Another difference is the Yellow-Vest spark occurred in response to an immediate economic pain from the fuel tax increase. It’s not clear exactly what that equivalent would be for AI, though I suspect if job displacement is vast enough, some match to light the powder keg will naturally emerge, like a layoff announcement.

And then there is the France of it all. France already has a much greater social safety net, and obviously a different history than the U.S., including with protests. For example, their unique system may increase the likelihood that they will take action in response to protests.

What could be the symbol equivalent of the yellow vest?

I don’t know, but I think that worked because it was readily available, so I could see everyone wearing a specific color, like black, as pictured above.

Could it similarly turn violent?

I hope not, but I can’t see why that wouldn’t be the case, especially in the U.S., considering historical violence in similarly-sized past protests, most recently the George Floyd protests. Add to that the fact that we have way more guns and that immigration protests are turning violent right now.

How can we avoid this?

To me, that’s the key question. Ideally, we can prevent violent protests by enacting necessary reforms ahead of time (soonish), rather than waiting until mass protests occur. That generally doesn’t happen for a couple of reasons. First, we tend to do things only under immense pressure. Second, the magnitude of the reforms to be enacted is not yet apparent. However, I’d like to work towards changing that, both to increase pressure now (as in this post) and to propose a list of sensible reforms. If you have particular ideas for that, I’d love to hear them, such as enhanced unemployment benefits, vouchers for private training programs, relocation assistance, etc. There are already numerous ideas circulating, and I plan to explore them soon.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

The returns justification—that science funding can literally pay for itself—may not seem like a critical rationale because it isn’t seemingly directly about science, but it’s the one that should end the debate. That’s because if many of the other justifications are valid, then paying for itself means that increasing science funding becomes a no-brainer, as it removes the downside (long-term cost). And crucially, research funding is the only policy with this pay-for-itself property that can scale to hundreds of billions in investment.

But, how exactly can a significant government expenditure actually pay for itself? That’s a bold claim that deserves a little unpacking.

Subscribe for free to receive new posts.

Unpacking how research funding can literally pay for itself.

It works by growing the economy so that, over time, the government collects more tax revenues than the initial expenditure. This is easier said than done, because the federal government currently collects only about 17% of GDP in federal revenue, so the growth must be substantial. Funding basic research, however, has been shown in many studies to achieve the growth rates necessary (more on that later).

Here’s roughly how it works: Let’s say we invest $500B more in federal science funding this year, which ultimately grows the economy by $1.5T (3 times the investment) each year, once discoveries are fully commercialized in the economy. Suppose the federal government takes in 17% of that increase in GDP, or about $250B a year in extra federal revenues. Then, after roughly 15 years in this toy example, these future increased federal revenues will more than pay for the initial $500B, even considering the time value of money (discount rate).

Of course, real-world models are more complicated, though those are roughly first-order accurate numbers for the U.S. In the previous post I referenced above, I had cited this IMF model (if you want to dig into it, search for innovation policy mix, with the underlying math in Online Annex 2.5). Here are their conclusions (note where it says pay for themselves near the end):

[T]he implied fiscal multiplier—the increase in output per dollar of fiscal cost—is 3 to 4 over the long term for the most effective tools (Online Annex 2.5). This implies that increasing fiscal support for R&D by 0.5 percentage point of GDP (or about 50 percent of the current level in OECD economies) through a combination of public research funding, grants to firms, and tax credits could raise GDP by up to 2 percent. The GDP impact reflects the complementarity between public and private research. The innovation policy mix also lowers the public-debt-to-GDP ratio by about 0.5 percentage point over an eight-year horizon, as the initial increase in debt from higher fiscal spending is gradually offset by higher GDP and revenue (Online Annex 2.5). However, while innovation policies can pay for themselves in the long term, countries with limited fiscal space may need to raise revenue or reprioritize other spending to finance the short-term costs of those policies (see Chapter 1).

In other words, increasing research funding at the margin today is expected to lower the national debt tomorrow by growing the economy, such that tax revenues eventually accumulate enough to start paying down debt. Of course, this assumes an advanced economy (like the U.S.) implementing a comprehensive and well-crafted policy mix (more on that in future posts).

Research funding is the only scalable pay-for-itself policy.

No other government expenditure category is like this in that it can arguably be raised to the order of $500B and realistically be expected to pay for itself and start reducing the long-term public debt-to-GDP ratio within a couple of decades (acknowledging we’d hit diminishing returns at some point). For example, infrastructure spending doesn’t pay for itself; according to the Congressional Budget Office (CBO), it is expected to reduce net costs by more like one-third (under deficit-neutral financing) or one-fourth (under debt-financing), not return multiple times the expenditure. Universal early childhood education has strong societal returns, but from a fiscal perspective, it takes much longer to break even, if at all, because most of the fiscal benefits occur as people grow up and earn more in middle age, forty to fifty years down the line. Targeted preventive health measures, like childhood vaccines, are also found to pay for themselves, but they don’t cost much from the federal government’s perspective, so they can’t take that much extra investment.

In other words, increasing federal basic research funding is the highest return on investment (ROI) at-scale budgetary expenditure we have available.

What are the returns for research funding?

No one entirely knows, of course, because the returns change based on the particular funding apparatus, and the marginal return is ultimately different from the average; that is, you get diminishing returns at some point.

In 2018, the CBO published this call for more research, noting “although extensive data exist on federal spending for nondefense R&D…[a] convincing synthesis of the results from the literature has proved elusive.” Since then, though, researchers have been heeding this call, including Karel Mertens at the Federal Reserve Bank of Dallas.

In a 2024 paper entitled “The Returns to Government R&D: Evidence from U.S.,” Mertens and Andrew Fieldhouse take a novel approach by examining the aftereffects of historical “shocks” in R&D funding across five federal agencies. They conclude:

[T]he implied rates of return to nondefense R&D are high. The reliable estimates range from around 140 percent to 210 percent…

Our estimates also suggest that federal investments in nondefense R&D are self-financing from the perspective of the federal budget, at least in the long run. Assuming a return of 171 percent, a $1 long-run increase in government R&D capital would improve the budget as long as the additional tax revenue raised per dollar of additional GDP is at least 9 cents (δ/ρ = 0.16/1.71 = 0.09), which is substantially below the historical ratio of federal tax revenues to GDP.

Note that 171% means $1 in yields $2.71 out, which is close to the IMF assumption above. They also have this summary blog post about their paper that gives a bit more color on the methodology and conclusions:

We find that shocks to nondefense R&D appropriations lead to significant increases in various measures of productivity and scientific innovation, but only with a delay—consistent with implementation lags and a gradual diffusion of new knowledge…

After about eight years, productivity starts to significantly and steadily increase. It continues rising and remains persistently elevated for at least 15 years after the increase in R&D appropriations. Put differently, greater nondefense government R&D appears to spur gains in long-term productivity, thus increasing living standards.

What are the implications?

The implications align with my thesis that science funding was already way too low before we started recently going further in the wrong direction. Mertens and Fieldhouse agree:

In terms of policy implications, our finding of large returns to government R&D implies substantial underinvestment of public funds in nondefense R&D…

I want to start developing a “prosperity platform” for a set of policies that will collectively maximize our future prosperity. I currently believe that dramatically increasing nondefense federal funding in basic research is #1 on that list. We need to reverse the trend and greatly increase this investment in our future:

Update

The Clark Center regularly polls a panel of economists, and recently they asked about this topic. The panel essentially universally agreed that historically U.S. science funding has paid for itself.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

Concern alone won’t flood the streets—but if AI wipes out paychecks fast, and Washington stalls, more sustained protests and strikes could follow.

An April 2025 Pew Research report found widespread AI concern across various topics—jobs, privacy, inaccurate information, loss of human connection, the environment, and bias—but I think jobs can potentially lead to a categorically different societal reaction.

Subscribe for free to receive new posts.

Why job losses could spark next-level backlash

* Cuts span industries, so outrage lands on both parties.

* Every income bracket—from cashiers to coders—takes a hit.

* Sudden, deep job cuts risk recession and years of high unemployment.

Privacy, inaccurate information, loss of human connection, and environmental harms are, of course, very real. Yet, each mirrors existing debates—data protection, misinformation, social-media outrage, and climate change—that have stirred headlines for years without sustained street action or sweeping federal reforms (our still-missing U.S. privacy law, for example). To be fair, climate activism has produced some huge U.S. marches—for example, 400,000 in NYC in 2014, 200,000 in D.C. in 2017, and 250,000 in NYC again in 2019—but these were single-day spikes rather than sustained efforts.

Bias is a little different. America did flood the streets after George Floyd in a more sustained manner, for many months, but major reforms stalled out once marches faded and partisan lines re-hardened. Job-loss protests have the potential to run even broader and longer because they could directly hit wallets across the partisan divide, for years on end.

The extent of the backlash depends on the size and duration of the economic disruption. As I showed in this Gallup-poll post, even most Republicans who believe tariffs will pay off say they’d tolerate at most one year of economic pain for those benefits. Patience for AI-driven job loss is likely just as thin, if not more so. If AI keeps unemployment high, backlash lasts until it recovers or Washington intervenes.

Two historical moments that resemble this pattern: When automated textile frames wiped out skilled jobs in the U.K. in the early 1800s, the Luddite riots turned violent enough (including killing a factory owner) that Parliament dispatched roughly 12,000 troops to restore order, which concluded with over a dozen executions. By contrast, in early-1960s America, still shaking off a recession and high unemployment, there was widespread fear that automation might be at least partially to blame and that it would cause high unemployment to persist. Ultimately, it spawned a presidential commission, but unemployment returned to normal relatively quickly, so the future everyone feared never materialized.

There remain many open questions, though, which I hope to explore more in future posts. For example…

What is the net job impact?

Some jobs will clearly be displaced, which is already happening. But new ones will also be created. Will this be a large net negative, or will it be close to net neutral, similar to previous technology cycles? The public and “experts” are currently split on this question.

In our current survey, 64% of the public thinks AI will lead to fewer jobs over the next 20 years. Far fewer experts surveyed say the same (39%).

How do we help the displaced?

Even if job displacement is closer to net neutral, the displaced people aren’t likely to be the same people who get the new jobs. What, concretely, are we going to do for them? Historically, we haven’t done much, for example, for people who lost U.S. manufacturing jobs. We can do better this time. Interestingly, the recommendations from the 1960s-era presidential commission included income guarantees, relocation assistance, federal unemployment benefits, education subsidies, and government jobs.

Who pays for new government programs to help the displaced?

If intervention happens, who foots the bill—general taxpayers or the AI “winners” best positioned to do so?

If AI diffuses slowly, does aid get perpetually kicked down the road?

We’ve already seen several strikes in Hollywood, an auto-worker strike (concerning, in part, increased automation), and a dockworkers strike (with similar concerns). But these were scattered enough in time that they haven’t yet coalesced into a larger movement, similar to other scattered past protests. If future effects take many years to unfold across industries, then a critical mass may never form to create a true reform moment.

What will be the nature of the economic disruption?

If millions of jobs are displaced, there will be some economic disruption, but it could look very different from the past depending on if AI produces significant growth and productivity benefits, and in what timeframe. For example, it seems possible (though I have no idea right now with what liklihood) that unemployment spikes, but its GDP effects are offset by AI growth tailwinds that prevent a recession.

Do protests even matter?

I need to dig in more, but the short answer seems to indicate yes, in a few ways. They seem make the people who attend them more politically motivated, at least for that issue. As a result, if enough people go to them, then they can swing elections.

On average, a wave of liberal protesting in a congressional district can increase a Democratic candidate’s vote share by 2% and reduce a GOP candidate’s share by 6%. A wave of conservative protests, like those by the Tea Party in 2010, will on average reduce the Democratic vote share by 2% and increase the Republican share by 6%.

But, can they actually change policy directly? Here, it seems pretty mixed, like in the examples mentioned above. The potential seems there, though, if they are large and sustained enough.

What’s the best historical parallel(s)?

Recent protest movements seem more one-sided politically (e.g., climate change, Occupy Wall Street, Tea Party, etc.). Mid-century protests were arguably similar (civil rights movement, Vietnam War protests, etc.), though they were sustained for much longer and ultimately swayed public opinion and accelerated change. A better parallel here, though, would be something that is clearly bipartisan from the start, more squarely on an economic issue, and resulted in swift reforms. Protests after the 1911 Triangle Factory Fire that sparked rapid labor reforms are a candidate.

If others come to mind, or if you have thoughts on these other questions, please let me know.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

That leaves one-third of Republicans, 28% of independents and 15% of Democrats willing to endure a multiyear economic disruption to realize the possible benefits of tariffs.

Subscribe for free to receive new posts.

The Republicans are most interesting in this poll since that group believes most in possible tariff benefits. In particular, it finds that “in the long run,” 77% of Republicans think tariffs “will bring more money into the U.S. than it ends up paying in tariffs to other countries” and that 85% think it is very or somewhat likely that tariffs will lead to more U.S. manufacturing jobs.

However, that pairs with 82%—about an equal amount of Republicans—who also think it is very or somewhat likely that tariffs will lead to “you paying more for products you buy.” This expectation of higher prices is reflected similarly in other recent polls, like this AP-Norc poll.

So, ~80% of Republicans believe in the short-term pain, long-term gain narrative, yet only ~33% are willing to entertain that short-term pain for longer than a year. Why the huge gap?

I don’t know, but here are a couple of non-mutually exclusive theories:

* You’re probably more willing to endure short-term pain in a good economy. However, Republican confidence in the overall economy is low. For example, another recent Gallup poll finds that about half (48%) of Republicans/Republican leaners think today’s economy is either slowing down (25%), already in a recession (15%), or already in a depression (8%). This data rhymes with another recent Harris poll.

* Most expect the short-term pain from higher tariffs to hit them personally in higher prices, but they expect the benefits (to the extent any are expected) to accrue mainly to others (given that most people don’t expect to be doing new manufacturing jobs themselves). This asymmetry seems like a potential pitfall for sacrifice-framed policies, unless the cost/benefit is more obviously positive at the individual level.

Another interesting question is when the clock starts on the economic disruption for people. The on-the-ground disruption from tariffs, such as higher prices and shortages, hasn’t yet fully manifested. However, gyrations in the stock market have already been occurring for a couple of months. Does that mean we’re already two months into the economic disruption period, or, given the stock market’s recent recovery, there is a kind of reset in people’s minds until it either goes back down or until inflation and shortages are clearer? In any case, if the disruption isn’t cleared up within a year from now, then it is likely to have significant effects on the 2026 midterm elections.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

Subscribe for free to receive new posts.

Justifications for increasing federal research funding

1. Longevity

Do you want your family members to live over 100? I assume so, but U.S. life expectancy is about 80 today. Where do you think the biomedical breakthroughs we need to increase it will come from? For example, Sickle Cell disease is now being cured in some patients via CRISPR gene editing. NIH grants were critical to this work, and that’s true for most transformational drugs. These types of cures can’t come fast enough; increasing research funding is the way to get them even quicker.

2. Defense

From my essay, The Great Race:

One or more key future technologies, such as artificial superintelligence, quantum computing, humanoid robots, space tech, etc., will likely afford the leader a significant military advantage. What if China becomes years ahead of us in all these critical technologies? Let it sink in that this is what we were to China just a few decades ago.

Falling behind on R&D today risks losing real wars tomorrow, and we’re already well on the path to falling behind China in particular. China has aggressively grown their R&D expenditures, 17× from 2000-2020 and now nearing U.S. aggregate levels, while U.S. federal R&D has crawled. We need to invest enough to keep ahead of China, and that’s more than we do today. Empirically, non-governmental funding isn’t keeping pace alone.

3. Returns

Science funding is one of, if not the best, dollar-for-dollar investments a country can make. I will do a deeper dive on this, though check out this Science|Business report that seems to have done a relatively thorough meta-analysis on academic studies looking into this question, concluding:

The many economic measurements of returns on investment to publicly funded R&I [Research & Innovation] vary wildly in range, but seem to cluster at around a 20% annual [social] return on investment.

That’s a higher rate of return than the stock market, and from the federal government’s perspective, it’s a significantly higher return relative to other federal investments available at scale. For example, returns on universal Pre-K are estimated to be more in the 10% range (I happen to think we should do that too, but science funding is even more critical).

If you believe the government should invest some of its money in the best investments for the country it has available, then science funding is a contender for #1. We should keep putting money into this investment until we reach diminishing returns, which we are nowhere near today.

In fact, the returns are so high that, under reasonable assumptions (for example, real Treasury rate ~3 %, about one-fifth of each extra dollar of GDP showing up as federal revenue, five-year gestation period, etc.), in the long run this investment will actually decrease our debt-to-GDP ratio (via an increased tax base). From the IMF’s April 2024 IMF Fiscal Monitor that ran such a scenario (emphasis added):

The innovation policy mix also lowers the public-debt-to-GDP ratio by about 0.5 percentage point over an eight-year horizon, as the initial increase in debt from higher fiscal spending is gradually offset by higher GDP and revenue (Online Annex 2.5)

Science funding isn’t an expense, it’s an investment with high returns!

4. Prosperity

Funding science pays for itself because scientific breakthroughs are the hidden force that grows our economy. In fact, without new technology, our economic prosperity is fundamentally limited. To see that, suppose no breakthroughs occur from this moment onward; we get no new technology based on no new science. Once we max out the hours we can work, the education people will seek, and the efficiency with existing technology, then what? We’d be literally stuck. Fundamentally, if you don’t have new tools, new technology, new scientific breakthroughs, you stagnate.

From my essay:

Think of how worker productivity increased in construction with the introduction of power tools and heavy machinery or in offices with the introduction of computers and the Internet. We need more of these, many times over: true leaps forward in technology applications that will dramatically increase our worker productivity.

This productivity increase means increasing output per hour worked (by definition), and therefore GDP (assuming hours are constant). If the U.S. had grown just one percent faster over the past fifty years, our average income (real GDP per capita) would be ~66% higher today. Research-driven productivity improvement is one of the few policy levers big enough to recreate that missed windfall, and more of it is better.

5. Innovation

Technological breakthroughs also mean better everyday products, so we get a higher standard of living for the same dollar output because we’re getting better, more innovative stuff to consume. Think of better household appliances, or in the future, self-driving cars vs. regular cars. Such innovations reduce household chores and costs, making our leisure time more enjoyable and giving us more of it. By increasing research funding, we’re buying future life satisfaction, and again, the more of that, the better.

6. Resilience

Will we have the science to respond to unexpected calamities, like new pandemics, asteroids, resistant antibiotics, etc.? Robust pathogen surveillance, AI-designed antibiotics, planetary-defense tech, and the like are insurance policies we aren’t buying enough of today. For example, we spend about 200M on antibiotic resistance, which already costs us $4.6 billion annually, let alone what that would balloon to if we had a severe, uncontrollable outbreak.

7. Jobs

As I noted above, research funding is a great investment long-term. It’s also a jobs engine in the short term. For example, this report from the nonprofit United for Medical Research found, with regards to NIH funding in particular (emphasis added):

As NIH funding is awarded to researchers in individual states, that funding supports employment and the purchase of research-related goods, services and materials. The income generated from these operational expenditures, along with that from capital asset expenditures (e.g., building, equipment, machinery, sophisticated software) cycles through the economy to produce new economic activity.

In 2022, that funding supported an average of 2,300 jobs and $353 million in new economic activity per state, or $2.3 dollars of economic activity for each dollar of NIH research funding.

More generally, research funding is a jobs win in three ways: First, you fund great jobs directly, today. Second, those jobs also fund supporting jobs today, as in the example. Third, the actual research creates better future jobs that will utilize the new technology being developed. Can we ever upgrade our jobs too much?

8. Frontier

Are we really going to cede the physical frontier, like Mars and the Moon, and the virtual frontier, like AI and the metaverse, to other countries? I hope not. We will need to spend more to win these races. What happened to the American frontier spirit?

9. Sovereignty

Maybe you don’t care about the physical or virtual frontier, fine. But what about ensuring critical technologies for our current infrastructure can be made in the U.S., like those that go into making semiconductors and energy. If we don’t stay at the forefront of these essential components, we risk being held hostage by other countries and at least partially losing our economic sovereignty, which has arguably already happened. From a 2023 report by the U.S. International Trade Commission:

Around 92 percent of the world’s most advanced chip manufacturing capacity is located in Taiwan. Any disruptions to Taiwan semiconductor manufacturing—whether caused by pandemics, natural disasters such as typhoons or earthquakes, power or water shortages, factory shutdowns, or international conflict—would potentially have large impacts on global semiconductor supply.

10. Environment

Similarly, we need new research to manage climate change effectively without throttling growth, such as in cheap energy storage and scaling carbon removal technologies. From the IPCC 2023 report:

Carbon dioxide removal (CDR) will be necessary to achieve net negative CO2 emissions. [CDR Fact Sheet]

Or, if you are more generally concerned about using up the Earth’s resources or other environmental impacts, it’s the same story. Increasing research funding means limiting more ecological damage by finding ways to be more energy efficient and finding less damaging energy pathways.

11. Power

Technological leadership buys global leverage. If you are technologically dominant, built on the back of research funding, everyone wants to trade with you for your superior technology and invest with you to get those returns mentioned above. Put differently: the more the world needs the next generation of U.S. chips, biomedicine, and clean-tech, the more it requires dollars to trade for them.

If we lose that leadership, however, in the worst case, we also lose all the soft power that comes with it, including being the world’s reserve currency. According to this 2024 IMF bulletin, this has already been happening for the last 25 years:

Again, from my essay:

We believe our ability to control inflation and interest rates is inadequate now—most other countries have much less control. Given our dominance, what we do has an enormous knock-on effect on their currencies. If the yuan becomes the dominant currency, we will experience similar knock-on effects from China’s actions, not vice versa.

I know, I know, I said 10 justifications, and I did 11…

Sold, but how much should we increase science funding?

The above essentially says in eleven different ways that more science funding is better, but doesn’t answer the question of how much more. How would we know when we reached an optimal level? I’m not sure exactly, but I’m positive the optimal level is much higher than it is now.

This post is already longish, so I’ll save details on that for another day, but here are two back-of-the-envelope swings that essentially get to the same place: something like 3x from where we were (before any recent cuts).

First, looking at it from the perspective of adequately matching China, which I explore more fully in my previously referenced essay:

To provide context, in 2022, total U.S. R&D investment reached approximately $900 billion, with China trailing closely behind. The federal government accounted for about $150 billion of this total. In the short term, the total U.S. R&D investment should at least double to more than $2 trillion, to more than double China’s investment. Suppose we assume half of this ~$1 trillion increase comes directly from the federal government and half from industry (matching/following government incentives/investment). In that case, we must allocate ~$500 billion more annually in federal R&D investment today.

Second, that result is approximately equal to how much we allocated in the Space Race, the last time we tried to invest in science on a large scale. At that time. Federal R&D as a % of the federal budget averaged around 10%, which again is about 3x more than today:

Of course, reaching that amount will require many new and innovative policy programs. I’ll post on that, too, in the future, but in the meantime, comments are welcome to further the conversation (and my thinking) and correct any errors. Finally, this scene from the movie Roxanne inspired me to write this post in this format.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

I’ve been noodling some rules over the past few years myself, on making good decisions.

Subscribe for free to receive new posts.

Rule 1: Reality is always more complicated.

I prefer the more general framing, but this is also phrased as “the map is not the territory” because every map is an imperfect representation of what it represents. For example, Apple Maps won’t tell me where the current potholes are, nor does it have all the trees placed correctly (at least not yet!). All maps have some fidelity limit — Apple will never get an accurate blade-of-grass count in every yard. Maps can also distort reality in various ways.

These omissions and misrepresentations are often okay if your decisions don’t turn on them. But if they do turn on them, then you need a better map (or description, model, etc.).

So many fallacies and bad decisions stem from not heeding this rule, which is why it is Rule #1. Paramount among those is the narrative fallacy, which I believe is the actual root cause of a lot of our societal problems: People listen to stories (narratives)—from politicians, influencers, etc.—and if they sound plausible, then some percentage of people will think that they are true, at least for some time. But just because something is conceivable doesn’t mean that it is true, or even likely true, and more to the point, a story is just another imperfect map. Reality is always more complicated.

Suppose you are faced with (as most modern societal debates are) a complex, dynamic system (for example, faced with fixing part of the economy), and you try to change it based on an overly simplistic model. In that case, you’ll very likely not get what you want. You’re also pretty much guaranteed to create unintended consequences that your basic model does not predict. Many cultural debates are similar, boiling down to again using some overly simplistic model (for example, a binary categorization of race, gender, political ideology, etc.), and then grappling with the numerous edge cases that struggle to fit neatly into that model.

This failure state isn’t limited to political or social issues, though. Developers often do this with refactors, aiming for “clean code,” only to painstakingly add back all the edge cases they removed over time as their code grapples with reality. In medicine, complex diagnoses fall through the cracks, sometimes for decades, because they aren’t labeled and categorized yet, as the official labels and categories haven’t yet caught up with the more complicated reality.

In physics, there’s “a joke about a physicist who said he could predict the winner of any race provided it involved spherical horses moving through a vacuum.” For every assumption you have, you have to decide how far down the rabbit hole you want to go with it; that is, how complex you want to make your model of reality. That’s because reality is always more complicated than your description of it. In a vacuum, a bowling ball and a feather fall at the same rate; in air, they don’t because of the added complexity of air resistance.

So, for any decision-making or problem-solving you’re doing, consider whether your current underlying assumptions (descriptions of reality) are reasonable for the situation at hand, or if you need to pursue better assumptions (more complex descriptions) first. This is often not as straightforward as it seems (more is always better, right?) because making things more complex has real costs: sometimes an actual financial cost, but always an opportunity cost and usually a communication cost too, as more complexity is more difficult to explain.

That’s why we default to simple narratives in the first place, as they minimize these costs. They are so easy to communicate—some people make them up on the spot—and AI makes this even easier. See for yourself. Go to a chatbot and ask for a narrative that explains why is a good or bad idea.

If you have two opposing narratives like this that predict real-world outcomes, ultimately, reality will show that things line up with one more than the other, which at least starts to expose the truth. But that is backward-looking and interminably frustrating. So what can you do instead?

First, you can heed Rule 1 and get appropriate models for the situation. As the world has become increasingly complex, successfully understanding and interacting with its dynamic systems requires increasingly sophisticated descriptions of reality, especially to generate specific outcomes. Simplistic models generally won’t cut it. Yet complicated models are hard to understand and easy to mess up. So, I think the best you can do is to ground your assumptions as best you can in trusted data and repeatedly run experiments in the real world to fine-tune them, with as tight a feedback loop as possible.

See other Rules.

Thanks for reading! Subscribe for free to receive new posts or get the podcast.

Subscribe for free to receive new posts.

The pieces referenced each make compelling cases for the shorter or longer timeframes (another good one on the longer side is The case for multi-decade AI timelines). These are all lengthy pieces, so I thought it would be helpful for me (and maybe you, too!) to consider the core assumptions that cause them to differ so much. I think it boils down to just two:

* Autonomous AI coders can advance AI much faster via recursive self-improvement.

AI 2027’s timelines forecast details the data points they extrapolate to predict that such recursive self-improvement will likely occur very soon, predicting about a 25x speedup in AI development pacing as a result. By contrast, AI as Normal Technology remains skeptical if this is even possible:

Perhaps recursive self-improvement in methods is possible, resulting in unbounded speedups in methods. But note that AI development already relies heavily on AI. It is more likely that we will continue to see a gradual increase in the role of automation in AI development than a singular, discontinuous moment when recursive self-improvement is achieved.

I’m skeptical that getting fully automated AI systems to improve themselves would be impossible. Still, I’m also not close enough to the cutting edge of this development to know when this might occur, or how much speedup we could expect from it when it does. Many potential bottlenecks have been flagged, such as a lack of inherent AI creativity, internal coordination issues between thousands of AIs, connecting the AIs to the whole stack of resources needed, compute scarcity, etc.

These bottlenecks are explained away in the aggressive timelines by the more intelligent and capable AIs operating at a much faster feedback loop, such that, to the extent these bottlenecks are significant, they would nevertheless find ways to address and break through them relatively quickly, like in months, not years. For example, they can make software more efficient (bypassing compute scarcity), iterate out of their coordination/creativity bottlenecks, etc. I get that, but one that seems harder to overcome is the potential for widespread societal backlash related to the second assumption.

* Advanced AI can control enough of the physical world to transform the global economy fast.

AI as Normal Technology contends that “the speed of diffusion [of AI through the global economy] is inherently limited by the speed at which not only individuals, but also organizations and institutions, can adapt to technology.” They point out that “AI diffusion lags decades behind innovation”, such as in medical and legal contexts, and that “there are already extremely strong safety-related speed limits in highly consequential tasks [like self-driving cars, nuclear, etc.]. These limits are often enforced through regulation, such as the FDA’s supervision of medical devices, as well as newer legislation such as the EU AI Act, which puts strict requirements on high-risk AI.”

By contrast, AI 2027 paints a picture of bypassing all of that slowness and regulation by granting AI companies special physical zones to operate independently:

Both the US and China announce new Special Economic Zones (SEZs) for AIs to accommodate rapid buildup of a robot economy without the usual red tape.

The design of the new robots proceeds at superhuman speed. The bottleneck is physical: equipment needs to be purchased and assembled, machines and robots need to be produced and transported.

The US builds about one million cars per month. If you bought 10% of the car factories and converted them to robot factories, you might be able to make 100,000 robots per month. OpenBrain [their Open AI equivalent in the forecasted scenario], now valued at $10 trillion, begins this process. Production of various kinds of new robots (general-purpose humanoids, autonomous vehicles, specialized assembly line equipment) are projected to reach a million units a month by mid-year [2028].

I think the AI backlash is real and will only intensify from here, especially as jobs are displaced at societally significant levels, “dark factories” (a.k.a lights out manufacturing) become more widely deployed, and humanoid robots become more visible. This backlash will create political pressure (and corresponding opportunities for politicians and political parties/movements) to slow things down.

On the opposite side is the arms race with China (and potentially others) for using AI in military applications. Superhuman AI will create a significant military advantage if the gap between countries in getting access to such runaway AI is significant. I’m honestly not sure how this nets out (backlash slowdown vs. military speedup), and some of the military side may happen in secret for some time, similar to the secrecy in the Manhattan Project. However, it will ultimately be hard to hide because to truly transform things, many physical objects must be made to revamp the military and the economy (vs. a small number of nuclear bombs out of the Manhattan Project).

So, what happens next with these two assumptions will largely determine the timeline. This leaves me thinking we still have to take the shorter timeframes seriously and accelerate societal preparations, to the extent that is even possible.

Avengers: Age of Ultron (2015)

Thanks for reading! Subscribe for free to receive new posts or get the podcast.