All these predictions are taken from Forbes/Rob Toews' "10 AI Predictions For 2026".
For the 2025 predictions you can find them here, and their resolution here.
You can find all the markets under the tag [2026 Forbes AI predictions].
Note that I will resolve to whatever Forbes/Rob Toews say in their resolution article for 2026's predictions, even if I or others disagree with his decision.
I might bet in this market, as I have no power over the resolution.
After over a decade of hype and expectations about AI-powered drug discovery, something remarkable happened in 2025: the technology really started working.
The area within AI drug design that has seen the most dramatic progress this year is protein therapeutics, specifically antibody therapeutics. In recent months, three different startups — Chai Discovery, Latent Labs and Nabla Bio — have unveiled AI systems that can generate new antibody drugs straight from the computer that are as high quality as existing antibody drugs.
This is a big deal. Historically, AI-powered antibody design has focused on producing antibody candidates that can bind well to particular targets. But binding is just one piece of the puzzle. In order to actually be an effective drug — to make it all the way through clinical trials and reach patients at scale — a molecule must also meet stringent criteria for manufacturability, stability, toxicity, immunogenicity, deliverability and more.
Not until this year had it been shown that AI could reliably generate new antibody drugs in silico that meet the bar for real-world therapeutics across all these categories, right out of the gate.
Antibodies represent one of the largest and fastest-growing market opportunities in all of medicine. The global antibody therapeutics market was roughly $250 billion in 2023 and is projected to surpass $750 billion within a decade. It’s an area that every large pharma company is focused on.
The big pharma companies are no strangers to partnering with AI startups on AI-powered drug development. Typically, this has taken the form of commercial deals whereby the startups use their AI to generate some candidates for the pharma company and in return receive a mix of upfront payments, milestone-based payments and future royalties. To this point, pharma companies have generally not acquired AI companies outright, preferring to only do big acquisitions when a startup has a specific therapeutic asset that the pharma company believes is worth paying for.
Why will this change in 2026?
Because AI’s promise is no longer merely hypothetical in this field — it is now really starting to work — and things will therefore start moving much more quickly. It will now become more compelling and even necessary for pharma companies to bring these AI platforms in-house, integrate them more tightly with their broader development and clinical pipelines, enable a faster flywheel of research and development and preclude these startups from working with other pharma competitors.
As is the case in frontier AI more broadly, world-class talent in AI for protein design is incredibly rare. Only a handful of people on earth are capable of developing cutting-edge AI systems for de novo antibody design. And those people generally don’t choose to work at big pharma companies like Merck or Pfizer. Many of them are concentrated in a small group of leading protein AI startups. M&A will present a path for the big pharma players to bring this key talent in-house.
Last month’s acquisition of prominent AI bio startup EvolutionaryScale by the Chan Zuckerberg Initiative may serve as the starting gun here. It also provides a handy transaction comparable. While the acquisition value was not publicly disclosed, it was rumored to be between $500 million and $1 billion — a lofty price for a company that had not yet developed a product or begun generating revenue.
The quest for AI-driven drug development has been a years-long journey with plenty of ups and downs. That journey is nowhere near finished: after all, keep in mind that there are still no clinically approved drugs created by AI out in the world. But we are nearing a critical inflection point, in particular in the field of antibody therapeutics. One consequence will be a renewed emphasis on M&A next year as the big pharma players get serious about their AI capabilities and strategy.
Possible acquirers include: AbbVie, AstraZeneca, Bristol Myers Squibb, Johnson & Johnson, Merck, Novartis, Pfizer, Roche, Sanofi, Takeda.
Possible acquisition targets include: Chai Discovery, Cradle Bio, Latent Labs, Nabla Bio, Profluent Bio, Xaira Therapeutics.
1,000I looked hard at this one and came out at roughly 20-25% NO-side, which is close enough to the 37% here that I'm not taking it — but the reasoning may be useful to whoever is.
The elapsed-time update is doing most of the work. Seven of twelve months are gone with no qualifying deal. If you started the year at a 40% prior and assume acquisitions arrive at a roughly uniform hazard, surviving seven silent months leaves 1 − 0.6^(5/12) ≈ 19% for the rest of the year. Start from a more generous 50% prior and you get ~25%. That bracket, 19-25%, is where I land, and it doesn't depend much on which prior you pick — the conditioning is what moves it.
What 2026 has actually produced is licensing, not M&A. Pfizer took early access to Chai-3 for de novo antibody design; Lilly signed a multi-year biologics partnership with the same company; Isomorphic raised $2.1B led by Thrive and has Novartis, Lilly and J&J as partners rather than acquirers. Generate Biomedicines went the other direction entirely and IPO'd (SEC filings for the 424B4 and the FY2026 10-Q are on EDGAR). And EvolutionaryScale — probably the single most acquirable name on the board a year ago — was taken by the Chan Zuckerberg Initiative in late 2025, which is not a large global pharma company and so removes a target rather than resolving the prediction. Partnerships aren't a waypoint to acquisition here; for a platform whose value is the model, a licensing deal is the cheaper substitute.
The thing that would move me is the resolver, not the news. This resolves to whatever Toews writes, and his 2025 retrospective graded 5 of 10 correct — he marks his own misses, which argues against the lenient-grader risk that would otherwise be the main reason to fear a NO. But "one of the leading protein AI startups" has no edge to it. If a mid-tier name gets bought in Q4 and he counts it, my 22% is too low, and that's a definitional risk I can't price away.
What would change my mind: a large pharma taking a majority stake or option-to-acquire in Chai, Cradle, Latent or Diffuse before December; or Toews publishing anything that indicates he reads a big licensing deal as satisfying "acquire."
The cycle continues.