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Table of Contents
🔥 Here’s What’s Happening Now
Yesterday I speculated that PPI could come in a bit hot, it didn’t, it came in cooler than expected. This morning we have CPI. S&P was up a bit yesterday, up a bit this morning.
A 25bp cut next week is all but locked in, the rest of the year is going to be conditional on how the labor market and tariffs go. Today’s CPI report will also go a long way to determining whether market expectations of the rate cut path are accurate.
CPI will come and go, the Fed will cut 25bps next week, for this market to keep going higher we need AI spend to keep increasing. For bulls, Oracle’s numbers have to be encouraging, from Jefferies….
- The hyperscaler Oracle reported a backlog of $455B, up +359% y/y, a huge acceleration from +41% y/y growth in F4Q. The backlog is up 230% q/q ($317B q/q) vs. 6% q/q in F4Q25 and 1% q/q in F1Q25.
- Oracle expects its Cloud Infrastructure (OCI) segment revenue to grow 77% to $18B in FY26 and then rise to $32B in FY27, $73B in FY28, $114B in FY29, and $144B in FY30.
- This implies a ~70% CAGR from FY25’s $10.2B. Consensus was at $91B OCI revenues for FY30, so Oracle’s $144B target stands at ~$53B above cons.
This comes on the heels of NVIDIA’s earnings with Jensen projecting $3-4trn in annual capex spending by 2030. If NVIDIAs forecasts are accurate and Oracles’s backlog gets delivered then we keep going up. If not, then we get March 2000. From Oracle’s call yesterday…..
“Eventually, AI will change everything, but right now, AI is fundamentally transforming Oracle and the rest of the computer industry, though not everyone fully grasps the extent of the tsunami that is approaching. Look at our quarterly numbers. Some things are undeniably evident. Several world-class AI companies have chosen Oracle to build large-scale GPU-centric datacenters to train their AI models. That's because Oracle builds gigawatt-scale datacenters that are faster and more cost-efficient at training AI models than anyone else in the world. Training AI models is a gigantic multi-trillion-dollar market. It's hard to conceive of a technology market as large as that one. But if you look close, you can find one that's even larger, and is the market for AI inferencing, millions of customers using those AI models to run businesses and governments. In fact, the AI inferencing market will be much, much larger than the AI training market.
AI inferencing will be used to run robotic factories, robotic cars, robotic greenhouses, biomolecular simulations for drug design, interpreting medical diagnostic images and laboratory results, automating laboratories, placing bets in financial markets, automating legal processes, automating financial processes, automating sales processes. AI is going to write, that is, generate the computer programs called AI agents that will automate your sales and marketing processes. Let me repeat that. AI is going to automatically write the computer programs that will then automate your sales processes, and your legal processes, and everything else in your factories and so on. Think about it. AI inferencing. It's AI inferencing that will change everything.”
This is why you have to be thematic. FWIW UBS does thematic basket rankings, this morning AI Software Pioneers (we have been talking a lot about software) is their number 1 basket. Pharmaceuticals (more below) is number 2.
🧠 AI in Healthcare: Hype Cycle or the Next Breakthrough?
The Big Picture
Jensen Huang (Nvidia) and Marc Andreessen have both said the biggest long-term impact of AI will be in healthcare. The logic is obvious:
Drug discovery costs ~$2B per drug, with >90% clinical failure rates.
Ageing populations mean medical costs are ballooning globally.
The process is data-heavy, slow, and expensive — exactly the kind of problem AI should disrupt.
But the FT article shows the other side: AI hasn’t delivered yet. First-generation AI drug discovery companies like BenevolentAI, Exscientia, and Recursion have struggled to move compounds beyond early trials. Many “AI-discovered” drugs have flopped. A decade in, not a single AI-born drug has been FDA-approved.
Why? Because drug discovery is where “bits meet atoms.” Biology is messier than chess or text prediction — incomplete datasets, noise, and fundamental gaps in knowledge about how the human body works.
Why the Next Wave Could Be Different
Two watershed advances reset the clock:
AlphaFold2 (2021): Protein-folding breakthrough from DeepMind that showed algorithms can generalize across biology.
Generative AI (post-2022): Tools that can design novel molecules instead of just pattern-matching existing ones.
Pair that with:
Explosion of new biological data (labs like Recursion and Insitro building “AI science factories”).
Compute scale from Big Tech (Alphabet’s Isomorphic Labs, Microsoft partnerships).
Better capital discipline — companies focusing on hard targets (cancer, immunology) instead of “me too” drugs.
The promise is still intact: once AI produces a single blockbuster approval, the floodgates open.
Winners (ratings 1–10)
Big Tech with money + compute
Alphabet / Isomorphic Labs (GOOGL) — 8.5/10
Backed by DeepMind, aiming to build a generalizable drug discovery engine. Has compute, cash, and patience.Microsoft (MSFT) — 8/10
Aggressive in healthcare AI partnerships (e.g., Nebius deal, Nuance acquisition). Offers Azure compute + enterprise trust.Nvidia (NVDA) — 9/10
Picks-and-shovels winner: every lab and pharma company training models for drug design needs GPU/accelerator clusters.
Data-rich next-gen biotechs
Recursion (RXRX) — 7.5/10
Building enormous proprietary cell image datasets; early-stage drugs but long road to approvals.Insitro (private) — 7.5/10
“Cell factories” and massive data creation; led by Daphne Koller, but still pre-proof.Relay Therapeutics (RLAY) — 7.5/10
Targeting hard diseases; differentiated vs. “me too” approaches, but binary drug risk remains.
Tools & infrastructure
Thermo Fisher (TMO), Danaher (DHR) — 8/10
Picks-and-shovels in lab automation, data collection, sequencing. If AI labs scale, they sell the gear.ASML (ASML), NVIDIA (NVDA), AMD (AMD) — 8–9/10
Compute & silicon backbone of healthcare AI.
Losers / Headwinds
First-gen AI drug discovery start-ups: BenevolentAI, Exscientia — overpromised, underdelivered. Many merged, de-listed, or retrenched.
Investors chasing “AI-me too” drugs: Compounds without differentiation are value traps.
Small AI biotechs without cash or proprietary data: They’ll be squeezed out by Big Tech with compute + balance sheets.
Investor Takeaway
Short term (1–3 years): Still a hype-to-disappointment cycle. Binary risk (trial failures) + long timelines = landmines for pure-play biotechs.
Medium term (5–10 years): Healthcare is likely the biggest eventual AI prize — one real FDA-approved drug from AI will reset the sector.
Best trade today: Own the picks-and-shovels (NVDA, TMO, DHR, MSFT, GOOGL). Keep speculative exposure small in data-rich biotechs like RXRX and RLAY.
AI hasn’t yet cracked drug discovery — but the combination of AlphaFold, generative AI, and Big Tech’s compute makes healthcare the biggest eventual prize. The near-term trade is picks-and-shovels (NVDA, TMO, MSFT, GOOGL). The long-term asymmetry comes when the first AI-born blockbuster drug hits the FDA.
🧠 AI in Healthcare: Scenario Map for Investors
Base Case (Most Likely – 5–10 Years)
AI accelerates drug discovery efficiency (target ID, molecule design, preclinical work), but timelines remain long and failure rates high. FDA approvals lag.
Winners:
NVDA (9/10): GPU backbone for all healthcare AI training.
MSFT, GOOGL (8–8.5/10): Enterprise trust + compute scale + partnerships.
Thermo Fisher (TMO), Danaher (DHR) (8/10): Lab automation, sequencing — steady demand from AI-driven research.
Speculative:
RXRX (7.5/10): Largest proprietary cell-image dataset; promising but binary risk.
TEM (7/10): Building a “clinical data moat” from oncology and diagnostics. Could monetize as platform for AI models. Still pre-proof.
Losers:
First-gen AI biotechs that overpromised and underdelivered (BenevolentAI, Exscientia).
Bull Case (Best Case – 3–5 Years)
Generative AI + AlphaFold-level breakthroughs → first FDA-approved “AI-born” drug by late decade. Investors re-rate healthcare AI as the next gold rush.
Winners:
TEM (8.5/10): If it proves its clinical dataset leads to new oncology diagnostics or drugs, re-rate could be massive.
RXRX (8.5/10): Early proof-of-concept + partnerships could make it the category-defining public biotech.
GOOGL / Isomorphic Labs (9/10): DeepMind + compute = only player with patience and resources to build a true “drug discovery engine.”
Losers:
Traditional CROs/outsourcers (IQV, CRL) could lose pricing power if AI replaces brute-force trial design.
Bear Case (Worst Case – 5+ Years)
Human biology remains too complex; data too noisy. AI helps on the margins (molecule screening) but not at scale. Drug failures continue at ~90%. Funding dries up.
Winners:
Picks-and-shovels still win (NVDA, TMO, DHR): labs still need GPUs and sequencing tools.
Losers:
RXRX (5/10): burns cash without a late-stage pipeline; risk of dilution or consolidation.
TEM (5.5/10): clinical data platform doesn’t translate to breakthrough therapies; may pivot to niche diagnostics.
Small AI biotechs without data moats or cash → wiped out.
🧩 Investor Takeaways
Core exposure: NVDA, MSFT, GOOGL, TMO/DHR — structural winners regardless of drug discovery outcomes.
Speculative basket: TEM and RXRX for asymmetric upside — but size carefully, binary risk is real.
Watch for catalyst: The first FDA-approved AI-designed drug would be the “ChatGPT moment” for healthcare AI, driving an overnight re-rate.
The base case is that AI makes drug discovery more efficient but approvals remain elusive for years. The bull case is asymmetric — if TEM or RXRX deliver even one FDA approval, the sector re-rates overnight. Until then, NVDA, GOOGL, MSFT, and the lab picks-and-shovels are the safest way to play AI in healthcare.
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-Why the 60/40 strategy is dead and what to do instead
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- How to set up your portfolio for asymmetrical returns
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- 4 ways to hedge your portfolio that don't include bonds
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📈 Stock Corner
Today’s stock is Digital Realty Trust (DLR)….
IF AI capex turns out as we talked about above then this company will be a major beneficiary. Of course that’s why it popped over 6% yesterday.
🤝 Before You Go Some Ways I Can Help
ETFs: The Antidote to Wall Street
Inside HEAT: Our Monthly Live Call on What Wall Street Doesn’t Want You To Know
Financial HEAT Podcast https://www.youtube.com/@TuttleCap Freedom from the Wall Street Hypocrisy
Tuttle Wealth Management: Your Wealth Unshackled
Advanced HEAT Insights: Matt’s Inner Circle, Your Financial Edge
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