$META's Muse can search, compare, and help complete purchases for a user. That raises a simple question: what happens when a purchase begins with an AI agent instead of a platform like Booking com, Expedia, or Uber? The answer is more complicated than “agents replace aggregators.” So we followed the process from the initial prompt through checkout to understand which companies remain involved, who gets paid, and why agent adoption creates additional CPU demand. $META has spent years facing questions about what its enormous AI investment will ultimately become beyond better ads and recommendations. Muse may be the first glimpse of a broader consumer AI business. The Age of Agentic Consumers. Link below

Why am I so focused on networking over raw compute? Look at GPU utilization 53% of surveyed data center operators report running at only 50–70% capacity during peak demand. GPUs aren't sitting idle because they lack compute power, they're sitting idle because they're waiting for data. They wait on storage, they wait on CPUs, and above all, they wait on other GPUs to send results back across the network. If you add more compute to a cluster without upgrading the pipes, you just get diminishing returns on your capex. $LITE $COHR $AAOI $CRDO

So much for pacing the frontier. Per the NYT, Anthropic plans to go from roughly 1.5 GW of compute last year to 5 GW by year-end, then roughly 10 GW next year, while preparing for an IPO. Nearly 7x the capacity in two years.

$EXPE and $BKNG built much of their moat by helping consumers discover and book travel. If agents like Instinct and Meta’s Muse take over that process, how much of that moat survives, and what happens to the margins built on it?

*FED HIKES 25 BPS DOT PLOT SIGNALS MORE TIGHTENING AHEAD Fed funds rate: 3.75–4.00% 2026 median rate: 4.1% vs. 3.8% previously 2027 median rate: 4.1% vs. 3.6% previously 2026 core inflation: 3.4% vs. 3.3% previously

Just a game theory scenario running through my head ahead of FOMC. Markets are currently pricing in a multi-year hiking cycle, but the primary goal of a rate hike right now is simply regaining control of 10Y yields and stopping rate volatility. If Warsh hikes rates while making it clear a hike cycle isn't starting, long yields fall and consensus short positioning gets trapped in a classic clearing squeeze.

The semiconductor industry took 50 years to reach $1T in sales and per BofA it's set to double that in four. Even with a potential hike tomorrow, the demand data isn't weakening. B200 GPU rental pricing sits at $5.67/hr, up consistently over the past two months. Memory pricing is flat week over week, with no signs of demand destruction. 2027 is fully booked across compute ($NBIS, $AMZN), networking ($LITE, $AAOI), and memory ($MU, $SNDK). And even though SOX is up 67% YTD, it still trades at 18x forward P/E, below the S&P's 19x, on 139% YoY EPS growth. Semis are growing 7x faster than the broader market and trading at a cheaper multiple. A rate hike will add a lot of volatility, but we all know where this buildout is going.

I think this AI selloff will look overdone in hindsight. My expectation is that all this leads too is labs testing each other’s models alongside independent evaluators before release, with more spending on evaluations, monitoring and logging. This whole “pacing the frontier” push also feels like a way to protect the labs’ margins. A coordinated slowdown could give them more time to monetize each model before competition forces another expensive training cycle. I don’t see that stopping the infrastructure buildout, especially with B200 rental prices still near March highs and BofA raising its semiconductor growth outlook. My biggest concern is how they get China to participate. Amodei himself says global pacing would be “much harder to achieve.” Even if China agrees, how does either side verify that the other is following the rules? I don’t expect the US to meaningfully slow down either way, because I can’t see Washington or the labs accepting a pause that lets China catch up.

Twitter is once again consumed by the debate over slowing down AI. Anthropic is calling for slower development of frontier models and stronger safety oversight. We had a similar discussion a few weeks ago. This time, though, Dario has laid out a serious, detailed argument in a lengthy essay, and both Sam and Elon have expressed support. There’s plenty of skepticism. The most common argument is that training models has become too expensive, and frontier labs want an excuse to slow down. Or perhaps they want to let the market catch up with model capabilities, then ramp up training again once demand grows. An important distinction here is that slowing down releases does not necessarily mean slowing down training. Both Anthropic and OpenAI have developed advanced internal models that are not publicly available. And the safety risks AI poses to people vary substantially across industries. In some fields, AI is rapidly dismantling organizational structures and ways of working that have been in place for decades. For the first time, capital can be used to rapidly scale up compute and focus it on a single problem, without the years of effort traditionally required to train people and build organizations. Agents are designed to follow instructions. Look at what just happened in mathematics. On September 8, OpenAI announced a solution to the Navier–Stokes existence and smoothness problem. The Hodge conjecture and the Riemann hypothesis could be next. A similar way of organizing work is emerging in cybersecurity. In cases recently disclosed by Anthropic, attackers were already using multiple subagents to divide up tasks such as reconnaissance, code review, and verification of findings. Offensive and defensive operations will increasingly move too fast and operate at too large a scale for human engineers to keep track of. This also gives frontier labs, with their vast amounts of compute and capital, something resembling a god’s-eye view of human society. In these fields, it is important to slow the pace or find ways to work with existing systems—even if every industry will eventually have to go through the transformation coding has experienced over the past two years. Yet many industries still lack the data needed for reinforcement learning, particularly workflow traces and action logs. Coding, mathematics, and cybersecurity are fields where data is relatively accessible and feedback is often readily available. In other industries, even a standardized context layer is missing. Take finance. You have the questions you ask GPT every day and the notes you keep in Notion. But much of an analyst’s work happens in the process of conducting research, deciding how and why to revise an EPS model, and reaching a conclusion. Analysts have long lacked—and still lack—the infrastructure to systematically capture these decision-making traces the way software engineers can record and revisit their work. We’ve spent a long time tackling this problem while building FUNDA’s own context layer. One reason coding can support progress toward recursive self-improvement, or RSI, is the abundance of accessible, high-quality data that can be reviewed and traced back to its source. In most other fields, we first need to address the data bottleneck. This challenge extends beyond frontier models: the robotics field has been grappling with similar issues for years. You can’t assume that every industry will compress five to ten years of progress into two years, as coding has. When we discuss regulation and slowing down AI, we’re dealing with several different realities: -In some fields, LLMs have already reached the limits of human capabilities. -In others, substantial work remains to label data and change everyday work habits. -Regulators and researchers who take a god’s-eye view, using the most advanced fields as their reference point, see enormous threats and feel a need to push back. -Practitioners looking at the data available in their own industries, the capabilities of AI, and the ROI see a much longer road ahead. They see a need for a society-wide effort to build the foundations for reinforcement learning, as happened in coding, before the next wave of rapid progress becomes possible. So even if we slow down, we should do so sector by sector, in stages. We should pay attention to the fields where AI is moving fastest, but also to the many industries still struggling to catch up. Ideally, we would put more effort into helping those industries advance, rather than slowing everyone down to the same pace. Beyond the differences between fields, coordination is another major challenge. The debates over distillation and model-routing platforms that have dominated the past month already show how difficult it is for frontier labs to stay in sync. Anthropic can cut off model-routing platforms access that threatens its business or its lead in model capabilities. But that spending could then shift to OpenAI. The biggest challenge in trying to plan social progress from a god’s-eye view is that a market economy won’t simply follow a plan. Competition and commercial interests will always shape the outcome. We already live in a society where the gap between ordinary people and AI researchers is widening. A more detailed plan, tailored to each sector and implemented in stages, would do more to earn public trust. Cooperation among the labs today could be the first step.

Wild 24 hours for AI and lots of different proposals have been made. TLDR; the only *tangible* new fact is that OpenAI and Anthropic are going to have embedded 3rd party evaluators from unknown organizations with Dario floating METR as a possibility. Having 3rd party evaluators is smart as there is no Section 230 style liability shield for model outputs and showing a “duty of care” will be important in future litigation. Several internet companies might have gone bankrupt without Section 230 so limiting liability really matters. There are minimal investment implications from this single new fact, but I do think that for anyone who wants a “smoother for longer” cycle then most constraints are good: wafers, watts, real rates and spreads. Excessive regulation is a different matter but I don’t think we are anywhere close to this even if the vector changed over the last 24 hours. To summarize the events: Dario made the most maximalist proposal of the weekend: embedded 3rd party evaluators, a national regulatory regime for models beyond a certain capability/ingredient threshold, a broad international regulatory pact between democracies, stricter limits on compute/distillation for China and then a different international regulatory regime that encompasses China. Before there is a national regulatory regime, he wants a Sherman act waiver so that Anthropic can safely coordinate with OpenAI and other frontier labs without antitrust fears. TBF, this latest proposal is much less maximalist than some of his prior proposals like “Policy on the AI Exponential,” where he advocated for an FAA for AI. I believe he is sincere in his beliefs. And despite all the protestations, all of this would also probably be good for his business over the long-term. Sam agreed that embedded 3rd party evaluators were a good idea and stated they would implement them. Again, this is smart as should help limit future liability. Elon said “Dario is right” and later specified that “Dario is right that there should be some oversight. Peer review of AI by competitors is the right way to start this off.” This would be a MPAA like self-regulatory structure for AI with regular calls between the labs plus a process where each new model is evaluated for safety by competitors for a 1-2 week period before being released. That is *wildly* different from Dario’s proposal and in-line with what David Sacks has been proposing. Elon also stated that nothing was going to slow down open-weight models. Demis said that Dario’s essay was a “step in the right direction.” Dario also said that he was also open to Demis’ idea of a FINRA like self-regulatory structure as part of his proposal. David Sacks had a thoughtful post where he said that Dario and Sam should pace unilaterally, called the antitrust waiver a cartel request and denied that METR was truly independent given their ties to Anthropic. Sriram Krishnan, former White House AI advisor, noted that it would be important to have the 3rd party evaluators come from independent organizations that are not affiliated with any lab, which is basically an indirect statement about the relationship between METR and Anthropic which Sacks was explicit about. Clem from Hugging Face said they were open to being a neutral 3rd party evaluator, which is interesting especially if Jensen was consulted before that post. Alexander Wang from Meta noted that alignment would be an increasing focus going forward. An executive order seems likely after all this and the language in this EO is going to be really important. It is possible to democratize and distribute AI broadly and safely without centralizing it in the hands of a few corporations who might each become more powerful than any single government. I do not want a few humans in control of intelligence. I want us all to have our own intelligences that reflect our own values and human variation in all of its richness. Intelligence distribution over intelligence centralization FTW.

What do you guys make of this? There seems to be a disconnect. When $SMH was bottoming on July 29: > SMH: ~$504 > 10Y yield: 4.67% > WTI: ~$85 > September hike odds: 58.4% Now: > $SMH: ~$562 > 10Y yield: ~4.95% > WTI: ~$102 > September hike odds: 64% SMH is roughly 11% higher despite higher oil, yields and hike odds. Are stronger earnings expectations enough to explain the resilience? Could institutions be staying long and hedging elsewhere, or are equities underpricing the macro risk? With CPI tomorrow and FOMC next week, could we see another setup like July?