I talked to builders, investors, developers and locals over the last two weeks in SF. A few observations: 1. Fundraising has become increasingly more binary. Investors want to see either exceptional growth or a team tackling something really really hard. The days of raising on an "AI for X" pitch alone seem to be done. 2. Some of the best developers barely look at code anymore. They write spec documents, orchestrate multiple coding agents, and spend their time reviewing the outcomes rather than the implementation itself. The bottleneck is shifting from generating code to verifying correctness, and security. 3. Agent security is becoming a serious priority. I attended several security events. CISOs at larger companies are actively thinking about what happens when AI agents have access to sensitive data, systems, and permissions. 4. Robotics was an incredibly hot topic last quarter. Companies are still raising significant capital, but investors seem increasingly skeptical about timelines, commercialisation, and whether the returns will justify the investment. 5. Interestingly, many investors I spoke to still want crypto exposure in their portfolios. My impression is that a strong crypto business with real traction might have an easier fundraising story than yet another undifferentiated AI application. 6. Everyone wants to feed the "AI gods." Ask people what they would build today, and you'll hear similar answers: data, compute, energy, model infrastructure. The consensus seems to be that the biggest opportunities are in supplying the bottlenecks of AI development. 7. The non-tech locals have a very different perspective on AI. In my conversations, roughly 8 out of 10 locals I asked were unhappy about SF becoming the center of the AI industry. 8. Waymo is everywhere! Autonomous vehicles are definitely making transportation safer and more convenient. But, they are definitely widening the social distance between people who can afford new technology and those who can't. Uber rides were one of those rare opportunities to meet and talk to people from completely different backgrounds.

1/ Are we overbuilding compute for AI? Introspection on eve of release of DeepInfer - the most economically valuable software release of history (on par with Claude Code). DeepInfer has even a bigger money printer, if your are to believe latest numbers compared to what @SemiAnalysis_ reported. They can make 149B revenue from 1GW of GB200 without slack, and 100B with very big slack at DeepSeek's current prices, while serving fast tokens at 230 tokens/sec per user at 38$ per GPU hr of GB200. The performance is reproducible. (I will explain the calculations in following tweets) These numbers are much higher than numbers reported by SemiAnalysis below at 15B revenue per GW with B200 GPUs (not GB200) with the same model DS Flash 4.1. The difference shows the power of NVL72 architecture are large scale up domain. Large domains afforted by NVL72 allow to have large scale up domain for decode of 64 GPUs (WideEP). This allows for much less use of HBM on each GPU for weights (spread out), and more use for long sequences and batches. This allows them to serve 200+ tokens per second per user and still make 38$ revenue per GPU. If those numbers check out, implications are massive! Impact on demand and supply: --------------- Now imagine with 50GW compute by 2027 that SemiAnalysis claims will come online (I trust them), even with 50%/25GW going to training and post training, how much would have to be token consumption in USD to use up all 25GW at DeepSeek rates? 2.5T USD off course (25 GW * 100B / GW) Leading US labs are more expensive in terms of token rates (say 4 times), so revenue would have to be 10T USD ARR to consume 25GW inference compute by 2027 end. That is 8% of Global GDP of 125T USD. This is at the time revenue may be slowing down, if we are to believe all the rumours. Industry Implications --------------- But, this newfound DeepSeek efficiency will make Meta Muse much cheaper to serve, for example. Overall, assuming US labs already have such efficiencies or they incorporate them, this is superbullish for US labs, as well as, existing hyperscalar who may be able to charge much more for existing GPUs by slowing down build outs beyond 2027 (with LTAs it is hard though). Coming back to question: How does 1 GW of GB200 compute turn into ~$148B/year (100B with slack) in revenue? The math behind the token economics vs. standard cloud rental 🧵👇 CC: @firstadopter @bubbleboi @GavinSBaker @zephyr_z9 @teortaxesTex kingy.ai/blog/deepinfer-deep…

“AI is going to take the old business models of ads and subscriptions and just annihilate them. Something will replace it, and I think there’s a good likelihood it’s going to be built on stablecoins.” - @eastdakota Was incredibly prescient of Matthew Prince (@cloudflare) with @benmilne (@bralexyz) at @averystableconf in March of this year. Can't wait to tell you what @ay_o @kerrykellogg and I are working on for next year!

From my experience, this is factually accurate..any anomalies and leaks with Liquid Cooling can take the entire AI FACTORY down for days!! This is just one of the many triggers and reasons. Today, there is a lack of end-to-end monitoring and unified correlation across critical environment, compute, and network layers, which is absolutely needed to deliver a reliable data center as a service. We need holistic telemetry correlation across environmental, compute, and networking domains to break down monitoring silos and deliver complete operational visibility end to end. In addition to that, we need topology-aware root cause analysis, which enables us to pinpoint root causes precisely and quickly. By combining the physical asset topology with real-time diagnostic signals, we can get to the root cause of the problem to solve it as soon as possible, and pinpoint exactly where it is happening. We cannot build assuming nothing will fail. It is about how resilient and fault tolerant we are. It is about how quickly and proactively we can detect, root cause, mitigate and recover end-to-end.

We are in an AI Infra Supercycle where procuring GPUs is only Step1 but maximizing the ROI needs continuous optimized hardware and software availability. From the time a GPU lands at the dock, we need to get it up and running in a record number of days and not months of debugging. The reality most teams are dealing with right now: 1. Spending several months in debugging hell just to get a cluster to Day-One. 2. Silent, low-level network and compute glitches compounding until a multi-million-dollar run crashes mid-checkpoint. 3. Zero correlated visibility across thermals, hardware, and fabric to catch failures before jobs die. Procurement isn’t really the bottleneck anymore. Keeping the cluster alive and actually driving ROI is.

X: Paris is on fire Threads: Paris is not on fire LinkedIn: 10 takeaways from You Can See Everything Nextdoor: Data centers kill humans

Instinct seems to have completely lost mindshare in my network. Current split: @Muse for personal, @bot for work.

Weekend musings: No matter what happens in the markets 1) ai remains a transformational technology 2) -5% of global knowledge workers use AI in some capacity (most very lightly) daily 3) we are compute constrained even at today’s high token prices and GPU rental rates. We have ~95% of knowledge workers to still deploy AI to into a hardware ready compute installed base of ~1 billion enterprise PCs. I’m not including any consumer adoption in this demand case.

SF is way too beautiful to spend all of RAISE week indoors. I’m organizing a gentle, scenic hike around my favorite loop for a small group next week. If you are in town for RAISE and would like to get outside with us, drop me a DM!

There is a throwaway line in a WSJ story about the Seattle mayor’s failures about how reps from her admin traveled to SF to learn how Lurie was leading such a successful turnaround and were stunned by how it seemed to be connected to hiring competent people who know what they’re doing.

Lurie’s tenure in SF will be a case study in the importance of leadership for years to come. Same piece of clay as prior admins had to work with, wildly different results. 💪🏼

Increasingly hearing from friends & family how much they are enjoying Grok in their Teslas. Who would’ve thought Tesla would be the Trojan horse for Grok.

Unlike the labs, Meta doesn’t need to sell intelligence to win. It just needs to make it useful to a few billion people.

So slick @XMoney