Pharma Is Mis-Pricing Its AI Bet

In December 2016, Pfizer and IBM announced that Watson would be turned loose on immuno-oncology drug discovery. The handshake photograph ran in the trade press; the press release named the executive sponsor and the disease. By April 2019 IBM had quietly ended new sales of Watson for Drug Discovery, no named clinical asset had emerged, and the next handshake had taken its place. The contracts were not negligent. The science was real. What was missing was a pricing discipline severe enough to ask, before the announcement, what the firm would have to learn for the partnership to be worth what it cost.

Today the same arc is being run again at ten times the dollar values, on three sides of the same table. Pharma writes ten-figure checks to frontier-model labs (Lilly–Isomorphic, Sanofi–OpenAI). Pharma writes ten-figure checks to AI-bio platforms (Bayer–Recursion, AstraZeneca–Absci). And venture capital writes ten-figure checks into the AI-bio companies themselves (Xaira, Isomorphic). Biopharma has no AI-free future, and the AI of this decade will reshape it more deeply than any wave before. That is precisely why the mispricing matters: the prize is real, and the firms paying for it without earning the learning underneath will not be the ones who collect. Three pools, almost none of the bets carrying a named therapeutic thesis, a publicly disclosed prewritten exit, or a capitalized build-side. Pharma is not underinvesting in AI. It is mis-pricing AI, and so is its counterparty across the table. Pricing, in the sense this essay uses the word, is not valuation. It is the full allocation package behind a bet — capital, data access, decision rights, organizational attention, exit discipline — and the build-side underneath the visible bet that decides whether the package compounds or dissolves. A bet is mis-priced whenever any of those layers is missing, and the most common mismatch now is Commit-grade headlines with no capitalized substrate at all.

This pricing failure has a name. Drift, as I will use the word, is capital that survives because no one has written down what would make them stop. A bet without a kill condition is not an investment; it is an operating expense in slow motion. Drift is the partnership that renews because nobody wants to kill it; the platform with users but no changed decision; the build-side that was never funded because it never appeared on the slide. We are in inning one of the biology–AI cycle, and its disease is drift dressed as strategy. Almost-good AI is what drift produces: outputs that demo well on curated public data, fail on the firm's own messy, partially-labeled, GxP-controlled corpus, and never change a decision a regulator will adjudicate.

What is the build-side, in operational terms? It is the substrate underneath the visible bet, and what separates a compounding bet from drift is whether that substrate has been capitalized — constructed inside the firm, acquired, funded at the counterparty under terms that return durable value to the buyer (right to audit the evaluation harness, co-ownership of fine-tuned weights, IP on output molecules), or owned by being the company that is the substrate. Most current pharma AI deployments rent compute and frontier models and stop there, as though the rented capability were itself the bet. Pricing the substrate, in whichever of those forms fits the firm, is the spine of every honest pharma AI conversation in this decade.

Two columns make up the substrate. Decision-grade data: proprietary multi-modal longitudinal data — disease biology, genetics, perturbational screens, chemistry, translational biomarkers, patient stratification, clinical response, real-world evidence — GxP-controlled and labeled to a standard the firm's regulatory pathway will accept. Evaluation harness: infrastructure calibrated to the firm's own biology and chemistry rather than to public ML leaderboards, asking whether the molecule survives translational filters and whether the biomarker predicts clinical response under the firm's own data.

Three columns make up the operational layer above the substrate. Decision pipeline and action authority: the apparatus that gets a model output in front of the program leader who can act on it, and the rights for that person to act. Governance and learning loop: the discipline that turns each output into the next sharper bet rather than the next slide, including the re-calibration cadence as biology and assays drift. Adoption surface: the organizational reality on which any of the above either lands or dies. Moderna deploying seven hundred and fifty custom GPTs across the firm within two months of rolling out ChatGPT Enterprise is what an adoption surface looks like when the substrate is real and the surface is real. The build-side is not data ops dressed as AI. It is the apparatus through which the firm learns biology faster than it currently does, and through which any partnership produces a learning loop instead of a subscription.

Biology resists AI more than chemistry, code, or language do, and the build-side has to be sized accordingly. The clinically translatable modalities are small-n; distribution shift across in-vitro to in-vivo to human is the entire game; evaluation calibration is the unsolved problem. Frontier-model performance on a public benchmark is information about the model. It is not information about whether the model will bend a drug-development decision a regulator will see. The build-side is what closes that gap.

None of these line items look like AI on a CFO's slide. A CFO can sign a five-hundred-million-dollar GPU order and book it as such. A build the substrate so the partnership produces a scientific decision line item, capitalized at a meaningful multiple of the headline, is harder, because the substrate is distributed across data ops, MLOps, translational science, governance, and organizational redesign — overhead, not allocation. The order of magnitude, working backward from what survives a serious R&D review, is at least one times the headline deal value, often two, over three to five years; most current pharma AI partnerships are priced at a fraction of that.

The industry response sorts into three groups, not two. Two firms run the longest stepwise journeys in the cohort, and they ran them differently. AstraZeneca built its Cambridge-based AI program into a decade-long sequence — Schrödinger, BenevolentAI in 2019, Absci in late 2023, Modella AI in early 2026 — and a parallel internal substrate now visible from the publication trail. Roche bought it: Foundation Medicine and Flatiron in 2018, Aviv Regev to gRED in 2020, Prescient Design in 2021, the PathAI definitive merger in 2026. It is the most expensive forward-carry in the industry, against the deepest data position in oncology. A second group, the bilateral-pact tier, runs from Sanofi to Novartis to BMS, signing frontier-lab partnerships at scale without yet making the internal substrate visible at the same scale. Lilly sits adjacent to this tier with greater execution intensity — Dave Ricks talks publicly about running “one or two AIs every minute of every meeting” — but the substrate-side disclosure is still catching up to the deal-side disclosure. A third group, most of the longer tail, has a thin or quiet AI public footprint, which usually means buying black-box capability top-down without a substrate underneath. The decade-defining split sorts more reliably on whether the firm is capitalizing a substrate of its own than on whether it has signed the right partnership.

Behind that split sit four traps that keep the learning from happening, and each currently looks like prudence in the room. They share an underlying error: paying for AI without earning the learning underneath it.

The first is to subscribe without capitalizing the substrate. A firm signs a foundation-model partnership, licenses access to a generative-chemistry platform, buys seats on a tenanted version of a frontier model, and treats that as the AI strategy. Some subscription is unavoidable and rational; frontier capability rents to everyone, and open-weight models lower the rent further. The trap is the posture that treats the rented capability as the strategy, with no substrate capitalized underneath it, whether built, acquired, or capitalized at the counterparty under terms that return value to the buyer. Subscribe-and-capitalize compounds. Subscribe-without-capitalize delivers almost-good AI: outputs that win on curated public data and die on the firm's own GxP corpus. Most pharma AI deployments that disappointed in 2024–2025 disappointed at exactly this seam.

The second is to call the authority. The AI labs will take the meeting; their native expertise is not IND execution or payer contracting. The internal experts have the reverse problem: deep domain, no model. Either way, importing authority through a deal or a board seat is a 24-month bet. And the underlying difficulty is harder than importing. Frontier AI talent is largely not applying to pharma in the first place, on pay and on perceived research ambition. The question that sorts a legitimate import from the trap is whether the firm is running a parallel internal bet that the import is meant to calibrate, not replace, and whether the build-side is being capitalized at the firm or only at the counterparty. Where neither is true, the import is the trap.

The third is to wait. Waiting sounds prudent in the room and is arithmetic on the patent cliff. Keytruda — a thirty-billion-dollar revenue line under threat from the IV patent cliff later this decade, defended in 2025 by a subcutaneous strategy that approves a contained development spend against an outsized revenue pool — is the case every Commit is priced against. Beyond Keytruda, roughly three hundred billion dollars of branded pharma revenue is at risk between now and 2030. Big-pharma R&D returns have collapsed since 2010; the recent recovery is GLP-1-driven and thin underneath. That is the gravity well. Wait and see is itself a Commit, by omission, to a timeline somebody else is setting. The disciplined version of waiting is not idleness; it is running cheap experiments to update the prior.

The fourth looks the most like a strategy and is the hardest to see. It is to partner under conditions that look like access and are actually capture. The pattern is documentary, not speculative: a pharma agrees to provide proprietary data to develop the lab's AI models; the lab's evaluation harnesses, prompt patterns, and operational know-how compound across all of its partners; the pharma's data, once shaped into the lab's pipeline, does not come back portable. By the time the lab raises its next round at a multiple of its prior valuation, the pharma is the price-taker on a contract written when it was the price-setter. Several 2024 announcements raise the capture question on the documentary surface alone, regardless of how those deals ultimately deliver. Capture is the inning-one trap that pays for itself in inning two.

The four traps share a tell. A list of initiatives standing in for a portfolio. Twelve names, no disproportionate Commit, no public Kill, no state assignment, no build-side line on any of them, all approved by the board without asking the second-order questions.

I could be wrong about the size of the gap; I do not think I am wrong about its direction. The alternative to drift is a discipline. Compounding judgment is not produced by spending more carefully; it is produced by holding the portfolio against one question and answering it the same way each time. The question is downstream of a goalpost most AI conversations never name. Pharma exists to produce new interventions (therapies today, diagnostics and prevention as the lines blur) that change patient outcomes. Pipeline value is the operating expression of that goal: better targets, better molecules, higher probability of success, more durable assets at the other end. Every dollar of AI spend either compounds toward pipeline value or it does not. The operating system that produces compounding judgment has four moves.

Map the spend.

The firm cannot price what it cannot see. The map is where dollars buying tools separate from dollars building capability, and where the build-side under each visible bet either appears or is exposed as missing. Many leadership teams cannot produce that slide. Without it, every downstream decision is made blind.

Price the state.

Every material AI bet sits in one of four states. Kill: stop decisively and free the capital. Commit: a visibly privileged bet against a named thesis, with disproportionate capital — measured against the headline plus its build-side, not the headline alone — decision rights, an internal adoption pathway, and a prewritten exit. Constrain: hypothesis-driven continuation at limited capital, exit criteria written down. Drift: spend that continues because no kill condition was ever written down. The kill condition belongs in the original allocation memo: three to five named, observable signals, with the date the board will revisit them. The discipline is not avoiding drift; it is pricing drift as drift, in public, so the capital can move. Almost no AI bet in pharma today meets all of the Commit conditions.

Most AI investment decisions in biopharma are not risk decisions. They are uncertainty decisions: the distribution of outcomes is itself unknown, which is why the usual ROI machinery fails against them. A wrong Commit at scale is costlier than several drifting Constrains, because capital and option value die together.

Test the layer where value actually breaks.

Any AI capability lives on four layers. Model and data: what proprietary learning does this produce, and is the data side capitalized as the build-side that compounds? The model side is the most commoditizing of the four. The data side is the substrate. Drug-development reality: did AI change target selection, patient stratification, biomarker strategy, dose finding, Phase II design, the probability of technical and regulatory success, or the interpretation of real-world outcomes? Each is a kill point in disguise. The honest test is not whether AI made the workflow faster. It is whether AI bent one of those decisions, with evidence, on a program a regulator will see. Business-model fit: who captures the economics, the firm or the counterparty's flywheel? Decision authority: who can act on the output, and is that person where the output lands?

A model can be right, a molecule can still fail, the business model can still be wrong, and the organization can still be unable to act. Most AI conversations collapse into the model side of layer one. The valuable ones happen at the data side of layer one and at layers two through four. The first AI-designed drug into the clinic, Exscientia and Sumitomo's DSP-1181, was discontinued after Phase 1 in 2021 on an OCD efficacy bar the molecule did not clear; BenevolentAI's BEN-2293 followed it into the Phase 2a failure column in April 2023 on an atopic-dermatitis endpoint. The wider cohort tells the harder story: in the small but published AI-discovered cohort tracked by Jayatunga and colleagues for BCG and Nature Reviews Drug Discovery, molecules show roughly eighty to ninety percent Phase 1 success and then collapse to industry baseline at Phase 2. AI is now reliably shortening discovery-to-IND and beginning to compress trial timelines and operational cost; what it has not yet done is move the Phase 2 success rate. The clinic still kills drugs.

Ask whether the business model itself is changing.

AI does not just change how fast biopharma does what it already does. It changes what biopharma is. The first three moves decide what to do with the assets the firm already has. The fourth asks what kind of firm those assets are building toward. Without the build-side underneath, business-model reinvention is rhetoric. It is the long-term prize, and the place this series picks up next.

Map the spend. Price the state. Test the layer. Ask the harder question. Together they are an answer to one question: what would an AI portfolio look like if every bet were priced correctly, visible bet plus build-side, against the pipeline value it produces? The firms that answer it will own compounding judgment, and the pipeline value it produces. The rest will own a growing AI expense base.

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The frame is written from the top-twenty pharma seat because that is where the capital sits. It inverts on the other side of the table, and the inversion has its own version of the same disease. A techbio CEO is not choosing among twelve bets; they are one. Their job is to force the buyer to price them as a Commit, by showing that the company is the build-side a pharma would otherwise be paying multiples to construct. Xaira Therapeutics launching with a billion dollars at founding in April 2024 is a capitalization fact, not yet a build-side fact; what makes the founding interesting is the substrate claim underneath: Marc Tessier-Lavigne, Hetu Kamisetty out of the Baker IPD lineage, the Illumina functional-genomics and Interline Therapeutics origins. Whether Xaira translates those into a clinical pipeline is the open question for inning two; that the pricing is being set on the techbio side, by founders who have raised more in one round than most pharmas have publicly capitalized as build-side in their entire AI history, is the inning-one fact. A meaningful share of that capital arrives from technology investors fluent in the data-and-compute stack but less fluent in the pharma-decision substrate that turns a flywheel into a development decision. The result is a mirrored failure mode — call it substrate without translation: technical substrate beautifully capitalized, translational and regulatory underfunded. Same disease, opposite stack.

Pharma's historical answer to a wave it could not build internally was to digest the companies that could: biologics in the 2000s, when Genentech, MedImmune, and ImClone were bought at multiples that looked irrational and now look conservative. The disanalogy worth naming is that those targets had Phase 3 assets at acquisition; today's AI-bio leaders mostly do not. Inning two will sort the AI-bio cohort into those whose substrate translates to clinical pipeline at the speed pharma needs, and those whose substrate looks beautiful in slides and never converts. The price will be set by whoever capitalizes the build-side first, and proves it converts.

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The framework also runs on a single deal. A large-cap pharma principal returns from a conference convinced the next move is a broad foundation-model deal with a top-tier AI lab: one rolling contract across the value chain. The layer-one demo was genuinely better than the internal team can produce. Run the four layers. Layer one: the model side is strong, but the demo runs on curated public data and a friendly evaluation harness; production runs on the firm's own messy, partially-labeled, GxP-controlled corpus. Layer two: not demonstrated; DSP-1181 and BEN-2293 are public reminders that layer-one strength does not predict layer-two survival. Layer three: unresolved, and potentially decisive. Foundation-model economics want breadth and royalty across deployments; pharma economics want exclusivity on the molecules the model produces; standard option-royalty contracts paper over that conflict. The right shape of the answer is operational: exclusive-on-output, non-exclusive-on-platform, with a step-up trigger tied to a layer-two milestone the firm controls, a build-side capitalization line the pharma owns and funds in parallel, and a kill trigger written into the term sheet — at month eighteen, no IND-to-PoC decision changed, the firm walks. Layer four: disconnected; the deal lands with the CEO and never lands with the program leaders who would actually act on the model's outputs. The right review starts at layers three and four, not one. Many foundation-model deals signed in 2026 will risk becoming Drift dressed as a Commit — not because the model is wrong, but because the firm has not earned the right to commit at the price the lab is asking, and has not capitalized the substrate underneath the price.

The first top-twenty pharma to publicly terminate a flagship AI partnership and reallocate the capital to a named Commit, with a disclosed build-side line, will define the inning-two narrative. The mirror prediction sets the same clock on the other side: the first AI-bio company to capitalize translational and regulatory at parity with technical substrate, and have a pharma price it accordingly, will reset the AI-bio multiple within twenty-four months. The Bitter Lesson the next five years teach will be brutal in its specificity: AI-bio companies that compound longitudinal causal data into a substrate that bends a drug-development decision will hold pricing power across model generations; AI-bio companies whose moat is clever architecture on top of public biology — protein-design models trained on PDB alone, small-molecule generators trained on ChEMBL alone, clinical-trial copilots built on public registries — will be flattened by the next frontier-model release.

At the next AI allocation review, the test is whether anyone in the room can name a bet, price its state, produce its kill trigger, and identify the build-side capitalization line, without leaving the table. Price the build-side, or you have not priced the bet. The firm that does it on one named bet, with the build-side disclosed, will be the comparison case the rest of the industry is measured against within twenty-four months. The next handshake photograph either ends the way IBM's did, or it doesn't.