Discussing the midterm elections, Joe Weisenthal said he expected Democrats to have a strong national night but singled out the Texas races as especially worth watching. In his view, Governor Greg Abbott is one of the few politicians associated with support for data centers and development, although Weisenthal said Abbott had been softening that stance for months. Texas’s technology and manufacturing sectors see the state as the future, but politicians may avoid taking clear positions on development and the environment ahead of an election.

Weisenthal said the Texas Senate race had begun to look competitive. He called Democrat James Talarico the favorite and described the Republican nominee as a weak candidate. He said a recent poll had Talarico’s opponent, Hinojosa, ahead by one point; betting markets put Talarico’s chances at about 60%. Weisenthal added that he personally rounds that estimate down to 50%—his own interpretation, not the market’s price.

Weisenthal did not consider New York’s races central. He allowed that the state’s Democrats could underperform other Democrats amid a possible anti-incumbent effect, but said he would be surprised if national attention focused on New York. He mentioned a report of possible power outages in Arizona’s Maricopa County as unverified, noting that he had not had time to check it. He also pointed to a major storm as a possible factor that could affect turnout.

As an unusual election outcome, Weisenthal imagined Abdullah El-Sayed being the only winning Democrat among several competitive races. He predicted that such a result would provoke an especially intense reaction online.

Joe Weisenthal said he generally trusts markets, including prediction markets: in his view, most people are unlikely to know more than the collective assessment reflected in bets. He thinks markets do a good job of distilling conventional wisdom and sees that as a strength, not a flaw.

A market’s value, Weisenthal said, is that it gives precision to a vague statement such as “Candidate A is favored”: a 55% probability is meaningfully different from 80%. So if a market gives a Democrat a 40% chance of winning Texas, he thinks that probability should be taken seriously, even though a win remains unlikely. This is Weisenthal’s view about the usefulness of market estimates, not a claim that markets are always right.

The participants questioned who will ultimately capture the profits from the AI boom: labs, chipmakers, or companies in other industries. Joe Weisenthal said there was no obvious consensus. He also responded positively to the hosts’ speculative idea that some in tech might support restrictions on data-center construction and computing capacity to create a supply bottleneck and push prices higher. This was presented as a theory, not an established fact.

One host argued that the AI investment thesis requires a major capabilities jump soon. He said agents had helped renew market optimism, but that, in his assessment, Anthropic’s growth had decelerated in the third quarter while remaining very rapid. He also relayed Elon Musk’s argument for focusing on a massive increase in computing supply without worrying too much about demand; the host said that strategy could prove successful, but did not present that outcome as certain.

The discussion included a retelling of an assessment by Torsten Slok of Apollo: the “Magnificent Seven” companies are seeing positive returns from AI, while many S&P 500 companies outside tech have a harder time increasing revenue or replacing jobs with AI. The participants also discussed the lack of consensus on AI’s potential productivity impact. Weisenthal said it is difficult to isolate the effects of the internet and mobile phones in productivity charts. He suggested that some of the recent U.S. productivity advantage might reflect layoffs and a stronger economy than Europe’s, rather than AI; he also acknowledged that he had not examined the Financial Times chart that had been mentioned.

The participants argued that today’s AI agents mostly perform only the tasks they are given and often resemble one another. One guest compared them to a room of 100 PhDs who do not talk to one another or look around. In his view, a next step would be an agent given a spending cap and a goal—such as improving a business—that decides how best to use the budget. He compared this with Meta advertising, where an advertiser sets a spend limit and asks the platform to find as many customers as possible.

Another participant suggested that autonomous multi-agent teams may be held back by their members being too alike. Making agents with genuinely different perspectives challenge one another could help, he said, while adding that he did not know whether anyone had found the right way to orchestrate such teams.

Emily recalled spending four days at home alone with her children while her wife was away. With different activities planned for both children, she asked ChatGPT where she needed to be in the next hour. She said it scanned her emails and helped her work out the schedule.

Emily said she had not booked a restaurant using AI, noting that she often has personal connections. Another participant said he had successfully asked AI to find and book a table for that evening; the system used OpenTable. He also described trying to book a flight with Meta Muse: it opened United’s website, but he did not complete the booking. He said conventional interfaces already make details such as price and connections easy to compare.