Recent advancements in one-shot AI protein / antibody development by Chai, Nabla, AI Proteins, Generate, and a few others are accelerating the *main* theme in biotech: Value of building the molecule is going down. The value of novel targets, novel translational ideas, AND also the value of clinical execution is going UP Here's where the value graph is moving towards: The twin forces of AI and China are quickly driving down price of mlc dev across many modalities: For AI - mainly Ab right now, emerging for genetic medicines, small mlc, ADCs, cell therapy; For China - Abs, cell and gene therapy, small mlc, and soon genetic medicines Having a "best in class" mlc is no longer enough - many tech platforms will soon offer you a mlc priced on metered compute (getting cheaper) and China CROs / biotechs will continue to eat the world with (over)capacity (continued involution). To make a valuable drug, you must differentiate on either: a) Novel translational ideas. Novel targets, novel mechanisms, but not just that - connecting targets with diseases; novel application of certain targets in new disease settings, new intuition on which patient pops have widest therapeutic index for a drug, etc OR b) Clinical execution. Determining the appropriate endpoints in a trial. Recruiting the right patients. Appropriate relationships with the right PIs / clinical sites. Ability to finance registrational studies in US markets ($10s to 100s of Ms) Either be a translational target discovery engine / tech platform that unlocks new modalities (which unlocks new translational hypotheses) OR get a team of grizzled clin dev / CMO vets and go raise $X00M+ to validate a clinical hypothesis Living in the middle (ie being "full stack") is dangerous work (at least for a startup)

for the next 18-24 mo., the biggest bottleneck in AI drug discovery is experimental validation and verification however it is increasingly clear that in 36 mo., the biggest bottleneck in AI drug discovery will be clinical trial scarcity the challenge with US clinical trial infrastructure goes far beyond simply regulatory reform in fact, outside of Australia, US FDA is the fastest in the world in getting INDs reviewed (30 day response time vs 60 in UK and Western Europe, and 60 working days in China) Yet, median time to site activation after trial regulatory approval for oncology trials is: US (academic oncology site) - 171 days (~6 mo) Australia Phase 1 - 63 days (~1.5 mo) China - as quick as 4 weeks It gets worse. Based on July 2026 analysis, US sites not only take median 124 days to get sites activated, it takes *another* 93 days to get FPI (first patient in). Australia sites can usually get from site activation to FPI in 2 weeks. To top it off, it takes *309 days* on average for US trials to get 75% of sites in a trial activated what's going on? the challenge is multifactorial - in the US, drawn out budget negotiations at every clinical site, significant overlapping local ethics and protocol reviews (esp at high profile academic research centers), and extensive site contract negotiations lead to months / quarters / years of delays. There is no magic bullet solution - no fell swoop of regulatory or policy change will address this - wholesale revamp is needed. simultaneously, US and western biotechs (and frontier labs) can't just wait around for systemic change to unfold. They need to play the game on the ground in order to establish a human data edge a new clinical execution paradigm + AI-native translational layer is sorely needed

**vision for how AI native biotech become one person co's** (and why this is the path to "curing disease") Single scientist / translational clinician / founder has a target product profile idea ie a clinical need spec for which patients with disease need what kind of medicine Founder has a target in mind - (further confirmatory research with LLM) OR licenses a target to go after from a target screening platform that specializes in target discovery Queries a clinician network (via LLM) regarding the clinical unmet need to confirm the line of therapy the molecule will enter and what the comparators will be Queries a LLM the right modality to pursue the target (sm, ab, cell and gene therapy, etc), tradeoffs, technical risks etc Design first screens for initial molecules through LLM coordinated tool calling Send off experimental screens for initial molecules through LLM (LLM platform handles logistics, legal, and PO spend / budgeting - just plug in your credit card / corporate expense account) Iterate on first screens with help of modality experts who are brought in part time to consult on best design strategies for the molecule (contrived examples: hit allosteric pockets in sm, design epitope specific binding for ab, design tissue specific delivery for oligos, etc.) - modality experts coordinate their own LLMs for modality specific models CMC experts brought in on consulting basis to scale and synthesize molecule for direct to GLP testing after an in silico tox modeling effort (coordinated by LLM) LLM coordinates shipping of drug product for fill / finish / scaling for human trials Develop clinical trial design plan in conjunction with LLM (review trial design precedents, set appropriate positive and negative control arms, background therapy, inclusion / exclusion therapy) Query a clinical development network (via LLM) to reach PIs regarding potential open questions on clinical trial design from most recent comparable trials learnings Bring in clinical development expert to consult - co ride on the clin dev portion - to ensure that study design trial questions being asked are the right ones send scaled molecule to clinical execution org (via LLM) - all in one service, site engagement, patient recruitment, pharmacovigilance, safety, data management, regulatory filings - org which is using own LLM platform to prepare IND dossier, coordinate clinical team Pharma / vc / biotech capital pools blend together into a “pod-like” structure where there is an entity providing overhead infra but single person / small person teams drive multiple therapeutics programs simultaneously, unlocking full creativity of each scientist (a la millennium / citadel pod structures) Simultaneously the capital intensity needed to enter the "founder PM" space drops as overhead is cut, opening up many more entrants into the space However, capital and operational leverage quickly sniff out alpha, and 10-100x concentrates on exceptional scientist / founder "PM"s portfolio managers founder PMs directly participate in drug upside much more than current life science VC structure because of dramatically reduced overhead, incentivizing the "100x" founder PMs to create dramatically more therapies This is the path to therapeutics abundance (100,000+ NCEs) Onwards

**all biotech will become AI-native biotech within the next 5 years** will all diseases be cured within 5-10 years? I do not know but I do know that traditional biotech will cease to exist, fell not only by AI, but by the rise of China, by the private equitization of biotech financing, by the ever increasing costs of drug development in some senses, this statement shouldnt be controversial. do we not use genetic validation in all drug discovery now? do we not use computational chemistry modeling for all small mlc programs? these were all previous technology waves that permeated every corner of the industry and yet - the AI-native biotech wave seems to be of a different breed, primarily because it can and will change the fundamental market structure of biopharma. the traditional biotech market - the one where life science VCs take risk capital and back small scientist teams as they progress through value inflection points (preclinical, IND enabling, FIH etc), the one that created genentech, vertex, alnylam, and countless other american biotech success stories - that market is a shadow of itself, replaced with one that does several hundred million $ series A's intended to fund (multiple) programs all the way through clinical POC. the emergence of the chinese biotech engine keeps feeding a hungry, hungry private equity machine (nominally still called life science VC) with "de risked" assets (largely named so because of de-risking human data, produced at a fraction of the price of the US market) to feed a classic financial arbitrage play, ie package a chinese asset in a US newco (or the more modern variant - reverse merge a Chinese asset into a US publicly listed entity) and sell to pharma at significantly higher multiples but like all good financial arbitrages, eventually this oppty will be played out too. already, pharma (Lilly, azn, and others) are making a significant play in China to cut out the VC middle men, setting up preclinical R&D partnerships and R&D sites and early clinical infra in China. soon those plum asset flips to pharma will not feature any VC middlemen any longer. so where to next for american biotech? enter the AI native biotech. small, focused teams of elite scientific minds who are ultra capital efficient. AI native ops, infra, and R&D execution means you only need a clinical translational expert (CMO), translational biologist (CSO), and likely a chemist (SVP drug dev) for carrying a mlc all the way through clinic. if you were paying attention, the industry was already headed in this direction, but AI significantly accelerates it. Maybe the small core team shrinks even further to single entrepreneurial minded MD / PhD type who knows exactly the TPP they want to create and where to go in the clinic with it in the coming years, AI native biotechs are the only solve for both components of the venture equation in a dramatically re shaped biotech landscape: i) fighting ever increasing cost of innovating on drugs (small elite teams w minimal overhead and AI execution / infra coordination) and ii) the durable value creation (truly differentiated scientific expertise to go after novel targets / biology equipped with AI native infra but also specialized AI models for specific translational biology - areas with sustained advantages against China's unyielding incrementalism ecosystem) the writing on the wall is clear: go west, young man - truly AI native biotechs is the only viable path to carry on the american tradition of life science innovation onwards.

THIS is how AI will (and is) making drug discovery more efficient. Simple stuff, just faster and more comprehensively.

ai x bio has moved leaps and bounds since i last wrote this ~12 mo ago we are showing green shoots (or more) of progress in pretty much each of these steps below --> the overarching q: who is going to get this thing integrated and built?

There is a fundamental flaw with the argument: “U.S. is 70% of global profits in tx and therefore can dictate which therapies are allowed in the U.S., including excluding China assets". The weight bearing assumption is that China’s biotech industry will not be able to sustain itself without US market. This does not conform with reality. Prior to 2022, Chinese biotech industry had minimal capital inflows from outlicensing / newco’s to US / Western pharma AND a very austere tx pricing environment domestically. Yet significant companies grew to dominate the domestic market, and many Chinese biotechs managed to move up the innovation curve. Do you think that Chinese biotechs sprang up overnight in Dec 2022 when this out licensing wave started? Akeso, Keymed, Hengrui, Hansoh, Innovent, and so many others have been around for years - pretty much all of them starting off by making drugs for the domestic China market. US out licensing deals were few and far in between, and yet many of them became $ B+ or $10’s Bn companies in the process. Furthermore, in recent months the Chinese payor market is now getting better at rewarding true innovation. 2025 NRDL reform introduced a higher pricing tier for true innovation (not me-betters). Domestic economics for first-in-class assets are getting meaningfully stronger, regardless of the exit to Western pharma path. From a unit economics perspective, "removing the exit to the West will kill Chinese biotech progress" argument just isn’t supported by facts on the ground. Another serious drawback to the banning argument is that presumes Chinese drugs are only me betters or me toos that are knock offs of western efforts (and will continue to be). Otherwise, the interpretation is that US patients would be ok not having access to the latest and greatest drugs. This, too, does not conform with reality. Chinese biotechs are now making net new innovative drugs across ADCs, in vivo CARs, genetic medicines and many more modalities, going after novel targets, payloads, and incorporating delivery tech that we have not seen here in the west; and if they work clinically - US patients will want access to as they will improve the clinical treatment paradigm. I have spoken to the Chinese biotech CEOs and seen these programs. Some will work and some won’t (that’s just drug discovery), but they are coming, and there is no use in arguing about it. Do you think it will be acceptable that US patients will not have access to best in class / first in class drugs? so to summarize: in the US, we have an industry that is *losing* pricing power (no matter if R or Ds are in power, tx pricing pressure is on the docket), trying to keep drugs being made from a country with *increasing* pricing power (based on government support) out, at a time when those drugs are increasingly innovative. Who do you think the actual loser from this is going to be? US patients. all this to say: the back and forth arguing is a moot point, the innovation is coming **whether we like it or not**. I’d suggest we in the US get busy competing, investing in innovative science, deregulating trials, and doing less belly aching. Patients, American and otherwise, are waiting.

moolenaar's proposal would likely have the opposite of the intended effect. if u ban US pharma from acquiring chinese IP, what is likely to happen is EU/Japanese pharma will get that IP, and u hand them a structural advantage. if u somehow convince them to also ban china deals, then u force chinese biotech to forward integrate and compete directly sooner. hengrui and at least 10 other biotechs already have the resources to do so. then let's say u ban those companies from the US market, then u just have a replay of the EV story, chinese pharma will just take over the rest of the world. the proposal also tries to scare people by drawing the analogy to chips and rare earths. there is no such analogy. licensing deals are for IP, and almost always include the right to manufacture wherever you want. so there is no supply chain risk. in fact, in drug world, the vast majority of the value of drug IP accrues to whoever develops and commercializes a drug, not who invented it. so right now western pharma is the winner from this relationship. so if the Q is well how do we not lose to china longterm? the answer is we must compete. america is great, we should lean into our natural advantages. continue to lead in breakthru science, maintain fda's position as the preeminent regulator, apply our cutting edge technology, and entice the world's best with the most pro-innovation commercial market. these leads are larger than many realize. we should mitigate our weaknesses, most importantly the bureaucracy that comes from being around longer than others that slows us down.

Notes from China visit, Apr - May spring 2026, year of the Fire Horse Met 20+ companies, 5+ local VCs & crossover funds, 5+ local PIs, and many entrepreneurs in Shanghai and Beijing =========================== ** Local China biotech mood shifts away from newco formation - although IMO that deal structure will still survive; the new slogan: "don’t sell your babies” - ie push assets further independently so that they can capture the larger value inflection post FIH data / early clinical de risking. The challenge here is that there is still a gap on the type of data, clinical dev strategy that local Chinese biotech / pharma is producing and what is necessary to secure a significant M&A price from global pharma. Arguably a newco in the right hands significantly changes the probability of a success exit for that asset in Western capital / M&A markets. The most discerning Chinese entrepreneurs understand this, and the recent exits by Candid, Oura, Kailera, and others demonstrate the path to $B+ value creation ** Simultaneously, there is a push for established Chinese pharma to enter into earlier R&D collaboration with western large pharma. Western pharma wants to access R&D efficiencies and provide targets. Chinese pharma executes against these targets to produce assets and secures significant upfront payments while only locking themselves out of areas they do not consider strategically core. Particularly relevant to this theme is the Innovent / Lily deal. I suspect this trend will move from local Chinese pharma to local biotechs as well, as the quality and speed there can also be high. ** Another prominent slogan amongst China biopharma recently is to “Go global”. This is particularly relevant for China pharma like Hengrui, BeOne, Innovent, etc. See Innovent / Takeda ADC deal where Innovent will co-commercialize US market. The push for Chinese pharma to start to eye global markets is clear, however IMO these Chinese pharma will fundamentally remain Chinese in decision making and locus of control given their current governance structure. We are still (many?) years from a China pharma turning into a Takeda-like set up where regional pharma has turned into a true global organization ** Turning to the US biotech / pharma side of things - there were obvious signs American Biotech is waking up to the opportunity to work with Chinese R&D ecosystem. Primarily right now through the IIT path (see below) however also starting to explore working with Chinese biotechs beyond just using CROs. A few US VCs are fully exploring this path e.g. MPM with their K2 Therapeutics platform and RA building a China R&D platform for their internal portfolio, but vast majority of US / western VCs have not figured out a way to unlock the opportunity yet. Doing business in China is not the same as hiring CROs in the style of Western R&D ecosystems ** China’s IIT pathway comes of age. With Decree 818 having come into effect May 1, IIT path becomes more regulated and closer to the requirements of Chinese FDA IND path. This has been a progression away from complete “wild Wild West” of the IIT path in the early days ie prior to 2020, to gradual opening of the path to foreign firms (beginning in 2023/4), to now a significantly more standardized path. However, there are still significant advantages to the IIT path and spoke with 10+ US biotechs looking to leverage this path to get FIH data. Suspect this movement actually grows significantly with Decree 818 in place as it standardizes things / removing grey area risk, and as it is still a fraction of time and cost to do it in US. Perhaps even more important than speed is the flexibility as you can figure out dosing, patient cohorts, even run different constructs H2H that would not be possible in a US IND trial but is immensely valuable in figuring out clinical dev strategy of a mlc in FIH data Meanwhile China IND timelines continue to shorten and can be filed at 3 mo max (and 1 mo min); very similar to US IND timelines now, so the two paths (IIT vs China IND) have converged. ** Finally, following the boom of in vivo CAR-T players last year, there are still many players in the space but many fewer that actually have differentiated / proprietary delivery platforms. A significant amount of clinical data will be coming this year, but getting feeling in vivo technologies whether LNP or LVV based will quickly become commoditized (big M&A numbers from large pharma notwithstanding). Simply too many firms that will push clinical data in coming months =========================== The drum beat of progress and evolution in China biopharma is unrelenting. For American biotech, I continue to believe that harnessing China ecosystem efficiencies (in both preclinical and clinical) is the path forward to retain global competitiveness. More to come on this soon.

traditional biotech VCs are dramatically underweighting the structural threat AI poses to their business model recently spoke w a US institutional LP - even top tier biotech VC return profiles are not competitive with what AI funds are now producing when an LP can put capital into an AI fund returning 5-10x+ net and the biotech fund is grinding out 2-3x on a good vintage, who do you think is going to get the capital? case in point: the largest bio venture fund raised in last 2+ years has been Sofinnova at $1.2b and Frazier at $1.3b; today Sequioa raised a $7bn AI focused opportunities fund; Thrive raised a $10bn fund a few months ago largely focused on AI most biotech GPs I talk to are treating AI as "yet another wave" in technology and most are pessimistic about near term AI impact in drug discovery; yes - better binder design is not remotely close to developing clinically impactful drugs; however, I'd encourage these GPs to look more broadly - the competition is no longer SaaS startups getting to $100M in ARR in 8 years; AI native startups are getting there in 8 quarters "but don't LPs want diversification?" — where was this argument when PE/VC 5x'd their allocation against other asset classes over the last 25 years? same thing is happening now, except with AI. intra asset class diversification is a misnomer. the real diversification is VC vs other asset classes, not biotech VC vs AI VC a lot of biotech VCs raised funds in 2021 and 2022 when times were good. many of those GPs will need to go back out in 26, 27, 28. it's going to be a rude awakening when that AI wave they expected to crest has not, and if AI IPOs ie OpenAI, Anthropic, actually IPO - watch out - LPs will be asking why this capital shouldn't just go into the asset class that are printing

**the emerging AI native life science R&D stack** the key question from mid 2025 til recently was whether frontier labs were actually serious about building products, capabilities, and orgs in AI x drug discovery or they were using it for marketing purposes in pursuit of ever larger rounds of funding. fair q when in a few week stretch in 2025, sam altman, demis,and dario all said that one of the biggest benefits of AI for humanity would be huge acceleration of tx development ("dozens of drugs in a decade!") - cue exasperated groans from the trad bio section of the peanut gallery a few cards have flipped in last few weeks: OAI: released GPT-rosalind, a life science research model, first vertical specific GPT Anthropic: acquired Coefficient bio to build biotech infra and a rumored bio model also dropping soon as the frontier labs' strategy in the space has become clearer, so too has the *AI-native life science R&D stack* a few comments on each layer of this 5 layer cake, starting with the middle: Intelligence Layer (Frontier + Specialized Models) ~ Ant, OAI, GDP all in running; will proprietary data end up being *the* differentiator? and if so, who actually has access? Wet Lab Coordination ~ speaking of proprietary data, can't get it at scale without some interface layer to the actual wet lab execution apparatus. in life sciences, that workflow is super outdated, phone calls, Excel, fax , PDFs, all just archaic. nearly no one has an API. are the frontier labs interested in tackling the long tail of assays and CROs that would need to be wired into a real wet lab coordination layer? nothing to suggest they will right now — but they are hungry for capturing value up and down the chain AWS Bio is first green shoots that another player will operate in this space but reviews on the ground have not been great - this may be the grittiest but also most unappreciated oppty in the stack Wet Lab Execution ~ life sciences has a massive long tail of CROs, and given this is where the actual proprietary data gets generated, so this layer can be a genuinely differentiating factor the interesting topic to watch: are any CROs going to become AI-native and start moving *up* the stack — doing wet lab coordination themselves, or perhaps even becoming preferred data providers to frontier labs? Early movers like Gingko and Adaptyv are making some noise, but this has to be a topic that the forward-thinking folks running AI strategy at Thermo, Wuxi, and others are thinking about Agents / Harness Layer ~ sitting on top of intelligence layer, lots of new startups have jumped into this space trying to coordinate models across life sciences specific workflows big risk looming over all of them is whether frontier labs will simply subsume this into their own product roadmap. Anthropic x Coefficient Bio is an ominous signal (but maybe $ 400M acqui-hire in 12 mo is an outcome that everyone involved is ok with) Application Layer ~ Benchling is the big gorilla here but if "attention is all you need" is *truly* all you need, the UX / UI with scientist layer becomes critically important, and potentially the most interesting place for a shake-up frontier labs could still move in. and new form factors could emerge enabling new startups. physical AI could change the whole workflow additionally is a notebook entry in a digital ELN even the right atomic unit of work in an AI-native workflow? finally, stepping outside the stack, the looming question that no one has fully answered yet - these are all *infrastructure* plays. what will the truly AI-native therapeutics company actually look like? the actual value creation that comes out of this stack? how will those AI-native biotechs look different in shape, value creation profile, and capital intensity compared to the biotechs we know today? stay tuned.

Translation: Western pharma purchases of Chinese biotech assets will continue

as an addendum to this, becoming more obvious that true venture style biotech investing is on the clock ... in the soon-coming future, vast majority of biotech venture investing will be AI enabled / AI native biotech investing: the only other flavor will be more PE style / cross over investing where clinical stage deals are being underwritten to 5x returns. These deals will need more risk reduction profile and that's where China comes in. More biotechs will have validated their platform / biological thesis in China, Australia, Eastern Europe and "biotech venture" will actually be just taking relatively human biology de-risked assets through to clinical value inflection points that are legible to pharma M&A teams what is left in the preclinical bucket will be AI enabled platforms that get to DC faster for specific modalities / solving specific pharmacological problems by training AI on large data sets this theme is further reinforced by the fact that we have gone from ~3 to at least 12+ modalities in the last~15 years, all of which needs to be "engineered" / optimized in order to become clinically meaningful drugs - a perfect set up for AI applications

given the early stage deal flow i'm seeing, being a tech bio VC that is not willing to go into tx investing is hard place to be on the tech / infra side you are crowded out by generalist funds by deeper pockets and better brand - why would a founder of "digital life sciences" play getting great traction not take that capital at the early stage? on the biotech tx side, there is real opportunity right now because traditional biotech VCs have pulled back (esp from early stage / preclinical investing), but most techbio VCs have become gun shy about taking "capital intensity risk" with therapeutic plays this is actually the exact worst time to do that as AI and AI investing eats everything else that doesn't require deep physical execution moats will be interesting to see who survives this culling

earendil labs $787M financing is eye catching for sure but the AI x China biotech theme is only getting started have spoken with a number of chinese biotechs (many which the west has not heard of) that are beginning to develop proprietary models. Including RNA therapies, cell therapies, degraders, small mlc's in a world where time and $ to FIH signal is king, combining AI's ability to cut time to DC and Chinese early clinical execution is going to be hard to beat

this is coming for drug discovery R&D workflows ... and I can't wait

the one unequivocal advantage that US biotech has over China biotech is proximity to clinical practice in the largest, deepest payor market in the world we know exactly what the treatment paradigm is. we know what the patients with most unmet need actually need. we know what the delta is between being successful in a 2nd or 3rd line therapy in every signfiicant market. in short, we know the clinical target product profile and the exact patient population that needs it the path to domination in the biopharma world still runs through the clinic (as always). we need to double down on this advantage

until AI x life sciences models can run agent generated hypotheses in the physical world with empirical real world feedback, there will be no meaningful value creation (or value capture) it will continue to be a nice toy in industrial workflows

the first in class, first in human data coming out of China in novel modalities (esp in vivo CAR-T, RNA therapies, gene therapies) in the coming 18 months will shock the world.

the only viable US biotechs going fwd are: i) novel platform tech (better way to find target, have better selectivity, or super power efficacy in a particular modality) OR ii) clinical team that can "king-make" a best in class mlc from China or an AI shop (ie uniquely experienced team for a particular disease indication that has specific expertise for shepherding programs through difficult to execute indications eg rare disease, respiratory, etc) every co in the middle (potential best in class mlc w/o differentiated clinical team, me-better approaches, and even novel targets without unique clinical strategy) is on the clock

Chinese biotech is steadily climbing the innovation curve Lots of evidence for this theme in recent weeks. at JPM in Jan, a full blown FIC targets symposium for chinese biotechs. last week, suzhou genhouse outlicenses novel target mat2a synthetic lethal ph1 ready small molecule to Gilead for $80M upfront. Last year, I wrote about the need for American biotech to lean into novel biology. How to respond if Chinese biotech is also moving into novel targets? For one, have seen American biotech still has advantage in developing *novel technology platforms* (for the time being). Am actively working with next gen cell therapy armoring technology, target logic gates for CAR signaling, etc that are all cutting edge platform technology companies. The challenge with these technologies is that they still need to reach value inflection point quickly (generally FIH clinical POC). Given the tx fundraising landscape, human data is still king, and it's far cheaper / faster to get that signal in China market this circles a ground truth that is market reality - even if not everyone recognizes it: American biotech needs to use every tool at disposal to remain competitive