Trump might do something incredibly dumb at any moment that starts the cascade downwards . At any point this results in the tumble with SP500 headed to a 3000 handle. The stock market has never been on such tenuous context in the last century

What's happening in SF should make international news - we're in the middle of a fully transparent, out in the open right-wing takeover of a 'liberal" city - and one that's happening with 70%+ voter approval.

$CRWV isn’t paying 15% interest. Only 4% of their debt costs that. I went through the Q2 10-Q line by line. Out of ~$35B in debt, exactly one loan sits at 15%. It’s $1.3B, due March 2028, and already being paid down. The 15% chart going around is the market yield on their 2032 bond. A higher yield doesn’t change what CoreWeave pays. It works like a mortgage. If you locked in at 3% and rates go to 7%, you’re still paying 3%. CoreWeave locked that bond at 9.625% and still pays 9.625%. What they actually pay > $6.6B of convertibles at ~2% > ~$10B of bonds at 9-10% > Most secured loans around 9% > ~8.4% blended across everything They have roughly $20B of 2026 capex left to fund, and 2027 will need more. Their main funding tool has been loans backed by customer contracts. The investment grade one backed by Meta priced around 5.9% in March. If they keep borrowing that way, I think the math holds. Zooming out, the AI trade still looks intact to me. $ASML reported record Q3 profit this morning and said lithography demand will outrun their supply in 2027 and 2028, even after adding ~30% capacity each year. Customers are already asking about 2029 and 2030 slots. Demand is still outpacing supply everywhere I look. $CRWV $ASML

I’m betting this becomes a textbook case. How should you transform a company when the technology arrives to do it 100x better? Can you leverage existing network effects to get to scale faster? Can you use AI to dramatically multiply the value of a business and give everyone within it superpowers, not just cut cost? Dating has so much headroom - current products are primitive and extractive compared to what they could be. Ravi, John and Shrey are great friends - I’m very bullish on what they’ll do here.

Carlsbad this morning!

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis: robonomics.substack.com/p/ai…