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:

1. 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.

2. 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.

3. 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.

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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.1

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.2 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.3 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.4 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.5

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

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• 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

Many people want to opt out of AI features right now, but most tech companies are ignoring them. At DuckDuckGo our approach to AI is to only make AI features that are useful, private, and optional. Whether you are Yes AI or No AI, or somewhere in between, we think you deserve a product that gives you the option of whether or not to use AI features.

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.

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We have a housing shortage in the U.S., and it is arguably a major cause of long-term unrest about the economy.

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).

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2. 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.

3. 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.

4. 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.

5. 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.

   

6. 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.

7. 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).

8. 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).

   

9. 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.

10. 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.

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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?

1. 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.

2. 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.

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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:

1. 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.

2. 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.

3. 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.

4. 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.

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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.

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Inside DuckDuckGo

Behind the scenes with the DuckDuckGo team — sharing insights on product, engineering, leadership, and AI.

I’ll still be primarily writing here on my personal newsletter, but I’m also one of the hosts for the video/audio series on the new substack we’re calling Duck Tales, which takes you inside DuckDuckGo by casually interviewing team members about new features and other things we’re doing and thinking about.

It’s six episodes in and, in addition to Substack, you can consume it on YouTube, Apple Podcasts, or other players using the RSS feed. Episodes are 10-20 min, and the ones I’ve done so far are on filtering AI images, tuning AI personality, using AI to make easter-egg logos, excluding domains from search results, and our browser’s scam blocker, with more recorded and scheduled to come out soon.

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?

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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.

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1. 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.

2. 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.

3. 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.

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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.

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I previously called on Congress to ban AI surveillance because of its heightened potential to easily manipulate people, both for commercial and ideological ends. Essentially, we need an AI privacy law.

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:

1. 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).

2. 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.

3. 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.

4. 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.

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A couple weeks ago I wrote a post titled AI survelliance should be banned while there is still time. Someone submitted it to Hacker News where it got over 600 upvotes, so I decided to submit it myself to reddit (on /r/technology) where it got over 1,100 upvotes.

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:

1. 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).

2. 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.

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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.

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All the same privacy harms with online tracking are also present with AI, but worse.

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.

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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.

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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!

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