The recent uptick in IPOs is an encouraging signal after a drought for much of 2025. Experts point to AI as a driving force behind this resurgence.
After hitting a historic low in 2025—only eight biotechs went public, as per a BioSpace tally, the lowest in the post-pandemic era—initial public offerings (IPOs) seem to be on the upswing this year. And while the reasons for this resurgence are many, experts pointed to one largely underappreciated driver of this rebound: the rise of AI.
“AI is fundamentally changing the risk calculus in biotech investing,” Tyrone Lam, chief business officer at GATC Health, an AI-centric tech-bio company, told BioSpace in an email.
Drug development is an inherently risky enterprise, Lam explained. While many industry watchers pointed the finger at limited capital and high interest rates, Lam said what was really holding back investors “was a lack of any reliable signal” from biotechs about their likelihood of success.
Especially generalist investors coming from other data-driven sectors, Lam said, the risk they face in backing investigational drugs has been “too opaque to price with confidence.” But now, he continued, “AI is beginning to change that.”
Drugmakers, for instance, can use AI not just to improve their chances of success—optimizing aspects of program design such as enrollment criteria and dosage—but also to quantify risk, building a stronger business case for potential funders. Investors, on the other hand, can leverage advanced models for their own purposes, helping them perform due diligence on their target companies.
“The companies drawing the most serious investor attention right now, regardless if they’ve IPO’d, share a common characteristic: they’re using AI not just as a discovery accelerator, but as a risk communication tool,” Lam explained.
Ardy Arianpour, CEO and co-founder of AI-driven healthcare data company SEQSTER, agrees with Lam. “Startups can use AI strategically to strengthen their pitch to investors,” he told BioSpace by email.
Indeed, some of the biggest IPOs this year so far have touted their use of AI, almost like a buzzword, as a central tool for their drug development engines.
Eikon Therapeutics, which made its Nasdaq debut on Feb. 5 with a $381 million raise, designs assets by “integrating AI capabilities and advanced automation” into its workflow to track how proteins move inside cells, according to its website. Aktis Oncology, which closed a Nasdaq IPO with $318 million in January, uses AI to “help us select the best radiopharmaceutical biological targets,” according to the biotech’s prospectus.
Meanwhile, Generate:Biomedicines, which notched the largest IPO since 2024 with a $425 million raise at the end of February, uses machine learning models to “instantly generate medicines,” according to its website.
What these companies are doing, Arianpour said, is demonstrating to funders that their clinical decision-making is supported by advanced, data-driven technologies—and it’s hitting the spot. “Investors increasingly look for organizations that pair scientific excellence with the data foundations needed for AI to deliver meaningful, repeatable insights.”
Not Just Pitch-Deck Fodder
The application of AI in biotech is diverse and nuanced. “To be clear, this isn’t ChatGPT or Claude running drug analysis,” Lam told BioSpace.
Instead, biotechs are developing and using their own bespoke platforms that fulfill specific purposes, such as simulating human biology or digging through various non-obvious drug interactions. Life science companies can, for example, use machine learning models to analyze and synthesize massive biological and clinical datasets, Arianpour said. This could include spotting genomic patterns or looking through large volumes of imaging or real-world data to identify novel therapeutic targets or biomarkers.
More importantly for investors, Arianpour continued, this “signals a systematic and scalable approach to discovery, rather than relying on traditional trial-and-error method.”
Biotechs could also leverage AI models to predict the right patient population for testing, refining their inclusion criteria and even forecasting trial outcomes, he said. “AI enables smarter, more efficient trials,” he added, which again, could help boost investors’ confidence in a budding company.
For Lam, this is where AI truly shines, at least from an investment standpoint: Not just to optimize drug design and accelerate clinical development, but also as a tool to communicate risk to potential funders.
Lam advises that biotechs start “moving from storytelling to evidence architecture.” That is, they should be leveraging AI to build their case to a potential investor and provide a “structured, defensible view of how the asset is likely to behave across its development lifecycle.”
The objective for young companies, “isn’t to bolt an ‘AI-powered’ label onto their pitch decks” but rather make it a central part of their businesses. “The startups that build infrastructure early, before they need it for a roadshow, will be in a materially better position when it counts,” Lam said.
Generate Opens the Gate
Of all the IPOs this year so far, the experts that BioSpace spoke to were most enthusiastic about Generate:Biomedicines.
Backed by life sciences VC firm Flagship Pioneering, Generate entered the scene in 2020 after spending two years in stealth. The biotech’s business revolves around an AI-heavy platform that draws “generalizable principles of biology” from high volumes of data and applying these principles to drug development, resulting in novel therapeutic proteins, according to its website.
Generate’s platform also has the capacity to learn from itself, looking at “critical molecular characteristics and functions” to inform future development.
This engine has produced a pipeline of investigational therapies, led by the late-stage GB-0895, an antibody that targets the pro-inflammatory cytokine TSLP that works in the airways. The bulk of Generate’s $425 million IPO will go toward this asset, helping bankroll two late-stage studies in severe asthma and one Phase 1b trial in chronic obstructive pulmonary disease.
Generate’s IPO stands out for Igor Pejic, a tech strategist who has published several books—including an upcoming one called Tech Money—geared toward investors.
Generate “is not just a slide-deck story,” he told BioSpace in an email interview. The biotech, he contended, “could turn out to be the first step, the first real moment of fusion between biotech and AI,” marking one of the first “AI-native drug-discovery platforms to come public with a late-stage asset and multiple clinical programs.”
The record-breaking raise has implications for the broader biotech startup ecosystem as well, Pejic added, noting that “big-ticket investors are once again willing to underwrite clinical-stage risk, as long as there’s a scalable platform story with a solid technology engine.”
Of course, much of Generate’s story is still up in the air. The biotech will have to prove its mettle in the public market, with a lot riding on GB-0895. Many will look to the asset’s late-stage performance to gauge whether AI is ready for pharma primetime, or if the tech is still too unreliable and unpredictable for drug development.
If GB-0895 and Generate do succeed, however, it would be pivotal for the industry, Pejic said. “It will be read as a vote of confidence not just in one drug but in the idea that AI and biotech have finally clicked and that AI engineered proteins can reliably feed a multi-asset pipeline.”
