David Shaywitz

The worlds of technology and entrepreneurship are captivated by recent advances in generative AI and large language models (LLMs). 

The arrival of ChatGPT, developed by OpenAI (a startup partnered with Microsoft), caused Google to declare a “Code Red,” akin to “pulling the fire alarm,” the New York Times explained. The latest class of startups at Y Combinator are reportedly flocking to AI. Students in the Harvard MS-MBA biotechnology program I advise tell me they are using ChatGPT constantly in section, and say the page is always open. I’ve also heard many medical trainees are routinely using it.

But if the future has arrived, as science fiction writer William Gibson famously observed, it’s not evenly distributed.

“People in tech are freaking out about LLMs, but not in science yet,” writes University of Rochester chemist and AI expert Andrew White.

Andrew White

“AI hype may be too high,” White observes, “but we’re missing hype on smart people + AI…. this will be the biggest transformation in science since the internet. Universities must be completely rethinking education and research.”

White’s onto something. There is an absolute frenzy of activity in tech, and a rash of startups seeking to apply AI to some aspect of biopharma, often drug discovery. 

But the view from within established biopharma companies tends to be more reserved. 

Far, far more reserved. 

Curiosity, wariness, weariness, and concern

As best I can determine, the view of incumbent biopharmas reflects a combination of curiosity, wariness, weariness, and concern. 

There’s authentic curiosity around ChatGPT and AI more generally, wariness about the newest new thing, weariness about successive cycles of technology hype and disappointment, and concern about the many unknowns.

No one in the pharma C-suite wants to miss the AI train. Then again, no CEO wants to explain to the media how confidential company information was inadvertently leaked – as recently happened at Samsung. Biopharma also must be serious about patient confidentiality and strict adherence to regulatory process. Not surprisingly, the industry is loath to engage in anything that could put these vital priorities at risk.

Consequently, many biopharmas are taking a cautious approach, considering generative AI the way they routinely evaluate other emerging technologies. Rigorous guidelines for use are established, highly specified pilot projects are proposed, reviewed and subject to tiers of approvals. Once clear guardrails are established, the new technology is gently prodded and poked. The potential is gingerly explored.

It’s not just senior executives who are risk averse. Most of my colleagues in biopharma are skeptical. 

After successive cycles of hope and disappointment, many seasoned drug developers just aren’t smitten by what seems like the latest shiny tech object. (I was probably in this camp as well before I started speaking with grounded AI experts like Zak Kohane and Peter Lee, who had early access to GPT-4, and began to appreciate over time that the technology represents something profoundly different.)

Many are still trying to understand what this technology can do. For instance, a colleague this week told me he tried GPT-4 for search and concluded “Meh. I’m still going to use Google.”

Playing around (or not)

What’s abundantly clear to me from Kohane, Lee, and others who have invested time in understanding GPT-4 is that:

1. To really understand what GPT-4 can do, you need to spend a lot of time just playing with the technology, trying out all sorts of things, particularly in your area of expertise (like medicine).
2. The more time you spend with GPT-4, the more it starts to feel like you’re developing a relationship with an extraordinary alien intelligence – powerful, imperfect, mystifying, intriguing.

Ideally – as one expert I spoke with this week suggested – the best way, in theory, for pharma colleagues to fully leverage GPT-4 would be to play around with it. This exploration would help determine the extent to which the technology could enable everyday tasks, and eventually contribute more profound insights. 

Given the concerns about risk, I don’t see this happening in biopharma. While data scientists like Cloudera co-founder Jeff Hammerbacher famously aspire to “party on the data,” this unfettered, playful approach tends not to be the dominant mindset of large biopharmas, given their abiding concerns around potential downside consequences.

One way GPT-4 is likely to arrive in pharma is through consultant service companies like Tata Consulting Services, Accenture, and Cognizant, to use the three examples highlighted by Chamath Palihapitiya on the “All In” podcast. As Palihapitiya notes, these companies do coding-for-hire work at scale, and are thus likely to be the first to operationalize tools like GPT-4 (which can assist and enable coders) so these firms can accomplish more with fewer people. 

GPT-4 will also come to pharma through applications, ideally from established, trusted vendors.  Familiar Microsoft Office products supercharged by GPT-4 will be an early example. For many emerging tools, generative AI will operate under the hood, largely invisible. Users, understandably, are likely to focus on the capabilities an application is providing — the job to be done — rather than on the underlying technology making this possible.

Culture Contrast

The contrasting attitudes of established companies and startups towards emerging technology — including, but hardly limited to, GPT-4 — reflects radically different goals and relationships to perceived risk.

Large established companies are defined by their ability to execute reliably at scale. In biopharma R&D, this means being able to manage a portfolio of complex programs globally. Included: everything from the consistent manufacturing of a range of products to the safe, responsible, and efficient conduct of clinical trials to navigating different regulatory procedures across the world. 

It’s a huge challenge to orchestrate these activities in parallel. The work requires a deliberate focus on establishing and optimizing repeatable processes, and making careful, deliberate choices – often involving multiple stakeholders – to ensure any risks to the established business are identified and hopefully mitigated.

Startups typically can’t, and don’t, work this way. Wall Street Journal technology columnist Chris Mims recently highlighted the prismatic example of Noam Bardin, a startup CEO who was absorbed into Google after his company, Waze, was acquired.

Noam Bardin

“What seems natural at a corporation,” Bardin said, “multiple approvers and meetings for each decision—is completely alien in the startup environment: make quick decisions, change them quickly if you are wrong.”

Bardin also described to Mims the differences in incentives he noticed before and after the acquisition.  “Before the sale,” Mims reports, “everyone’s financial interest was aligned with the performance of the company’s products. Once Waze was a subsidiary, getting ahead was all about getting promoted.”

As I’ve discussed (see here, here) a nearly identical description is found in Safi Bahcall’s Loonshots. The author, a former biotech executive, explains how disruptive innovation can be stifled by corporate processes dominated by risk-aversion and enlightened careerism. 

Suffocating processes designed to reduce risks are reportedly what caused Google to fall behind OpenAI and Microsoft in generative AI, according to the WSJ. “Now Google, the company that helped pioneer the modern era of artificial intelligence, finds its cautious approach to that very technology being tested by one of its oldest rivals [Microsoft],” the Journal reports (also covered nicely in this WSJ-associated podcast).

The risk aversion of large, established companies creates an opportunity for risk arbitrage. Startups can naturally shoulder more risk because they have less to lose and potentially more to gain. As I’ve discussed, this is reportedly how PayPal was able to defeat eBay’s attempt to develop a rival online payment product with Wells Fargo (called Billpoint). It is almost certainly why the most important generative AI applications for biopharma are likely to be developed by someone else. 

Bottom Line

While many technology companies view generative AI as essential technology that must be adopted as an existential, urgent imperative, established biopharma companies are taking a more cautious approach, curious about the opportunities, but wary of the many uncertainties and risks. 

Additional Astounding columns on generative AI:

• Generative AI: No Humbug
• Welcome to the AI Irruption
• New Book Contemplates How Generative AI Will Serve Man

David Shaywitz

In 1935, Astounding Science — the premier science fiction magazine of the day, and perhaps ever — published “Proxima Centauri.” It was a short story envisioning an encounter between a voyaging earth spacecraft and a ship from a nearby star system.

Written by Murray Leinster (William Fitzgerald Jenkins), the tale features the original use of the phrase “first contact” in science fiction. Think Close Encounters of the Third Kind, or perhaps more appropriately the “To Serve Man” episode of The Twilight Zone, since Leinster’s aliens turn out to be carnivorous plants who quickly develop (as alien plants often do) a taste for human flesh.

“First contact” is also the phrase Microsoft’s head of research Peter Lee selects to describe his initial encounter with GPT-4, in The AI Revolution in Medicine: GPT-4 and Beyond, the engaging and timely new book he’s co-authored with Harvard physician-scientist and

AI researcher Zak Kohane and journalist Carey Goldberg, documenting the trio’s early experience with this emerging, intriguing technology.

Lee’s phrase seems apt. When I asked GPT-4 to provide the first paragraph of a blog post on the subject, written in the style of The New Yorker, it responded:

The phrase “first contact” conjures up images of flying saucers and green men, of awe and wonder, of fear and loathing. It is the ultimate fantasy—or nightmare—of humanity’s encounter with the alien Other. But what if the Other is not from another planet, but from another machine? What if the first contact we are about to make is not with a distant civilization, but with a new form of artificial intelligence that surpasses our own in linguistic prowess and creativity? This is the prospect that looms over us as we await the arrival of GPT-4, the latest and most advanced iteration of a technology that can generate coherent and convincing texts on any topic, given only a few words or sentences as input….

GPT-4 even provides a cogent opinion on the popularity of “first contact” stories in science fiction, suggesting it may reflect the interplay of curiosity, fear, hope, and imagination.

Lee and Kohane have worked on AI their entire careers – yet neither seemed prepared for their encounter with GPT-4. Both described losing sleep as they contemplated the implications of what they describe as an “alien intelligence” with whom they each formed a “relationship.” Both are struck by its capabilities,yet acknowledge its limitations. 

Lee describes GPT-4 as “at once both smarter and dumber than any person you’ve ever met.”  He is particularly captivated by its ability to “imagine emotions and perhaps even empathize with people.”

Lee and his colleagues also present a series of specific, formalized challenges to GPT-4, examining its common-sense understanding, its moral judgement, and even its ability to navigate “theory of the mind” tests, exploring GPT-4’s ability to intuit what others might be thinking. The performance of the technology was extraordinary, Lee reports. 

Responding to one question involving two roommates who shared a cat that was moved from one place to another when one roommate left the apartment, GPT-4 not only correctly anticipated the likely reaction of each roommate, but even offered an opinion on what the cat in the scenario might be thinking – namely that the two roommates were “very rude and confusing because they keep moving it from one place to another without asking.”

As discussed in my last column, Kohane was especially impressed by the GPT-4’s clinical reasoning skills, and by the way it approached complex medical cases and suggested reasonable next steps. On the other hand, GPT-4 would also make some fairly basic mistakes (especially involving math), and would often confidently assert it was correct even when it demonstrably was not.

Having established GPT-4’s bona fides, Lee, Kohane, and Goldberg devote the balance of their text to a question that even the technology itself might struggle with: how do you solve a problem like GPT-4?  Each of the three authors seems focused on slightly different aspects of the challenge.

Lee appears especially preoccupied with the underlying existential question of what GPT-4 is, and whether at some level it can “understand” and “empathize.” He seems consumed by GPT-4’s capabilities, particularly since so many of these, “were not programmed by humans. Instead, they emerged into existence – sometimes unexpectedly – as its neural network grew.”

He continues, “how on earth can it be that the next-word prediction can possibly do… all the things that we’ve seen…unfortunately, we simply do not know the answer to this question.”

Kohane seems hopeful the technology can be used as a partner or co-pilot for healthcare providers, reducing burnout, administrative burden, and medical error, while allowing providers to practice at the top of their license. He delights in the possibility of physicians able to focus once more on the complex reasoning and human relationships that had first attracted so many of us to the profession.

Carey Goldberg

Projecting forward, Kohane imagines a next-generation large language model, fancifully called “Dr. One-With-Everything,” encompassing “protein structure, other basic biological databases (like gene regulation and human genetic variation), preclinical studies, and the design and conduct of clinical trials.”

This model, Kohane suggests, “will be likely the central intellectual tool for biomedical research by the mid-2030’s.”

Goldberg, meanwhile, offers the most pragmatic and in some sense relatable perspective on how GPT-4 will be used. She recognizes that “because GPT-4 appears to be such an extraordinary tool for mining humanity’s store of medical information, there’s no question members of the public will want to use it that way – a lot.” 

Already, Goldberg writes:

“health-related web searches are second only to porn searches, by some counts. Surveys find roughly three-quarters of American adults look for health information online. It’s not hard to predict a massive migration from WebMD and old-style search to new large language models that let patients have a back-and forth for as long as they want with an AI that can analyze personal medical information and seems almost medically omniscient.”

Moreover, she adds, patients want this capability now. While awaiting access to GPT-4, she writes, “My ‘everypatient’ emotion has tended to be impatience. Sure, I get the risks of hallucinations and other little-understood distortion, but I’ve still felt mainly the AI-delay frustration of people on the waiting list for chatGPT: ‘I get it – it’s not perfect. I’ll manage – just give me access!’”

The promise of democratizing this powerful technology is easy to appreciate.  As Microsoft AI expert Karmel Allison tells Goldberg, “If the internet and the mobile age were about putting information in the hands of everyone across the planet, the AI age is about putting intelligence into the hands of everyone across the planet.”

Karmel Allison

At the same time, the co-authors all recognize that there are clearly risks involved in deploying this powerful technology. Kohane, for example, asserts “GPT-4 cannot be used in medical settings without direct human supervision.”

Kohane and Goldberg also suggest that this is the “moment for broad, thoughtful consideration of how to ensure maximal safety and also maximal access,” and emphasize the need for “guardrails to keep patients as safe as possible.” But they also recognize it’s a “tricky balance,” adding:

“those safety measures must not mean that the great advantages that we document in this book end up unavailable to many who could benefit from them. One of the most exciting aspects of this moment is that the new AI could accelerate healthcare in a direction that is better for patients, all patients, and for providers as well — if they have access.”

None of the co-authors seem to seriously contemplate pausing ongoing work in generative AI – as Elon Musk and others have recently proposed.

Kevin Scott

Collectively, the authors’ perspective seems well-summarized by Microsoft Chief Technology Officer Kevin Scott, who tells Goldberg, “The technology will exist. It will have an enormous amount of possibility. I think it will be incredibly useful and powerful. And then society has to choose how it’s going to use it.”

It’s a thrilling future to envision, to anticipate, and (as many of us hope) to help create.

Unless, of course, the technology breaks away, and chooses how it’s going to use us. 

How To Serve Man? It was a cookbook.