I attended three industry conferences (Bio-IT World, Rev4, and BIO) over the last four weeks. This post shares my big-picture takeaway from each conference, as well as a bit about how they stitch together.
I think that I may be the only person who attended all three of these shows. If I’m wrong about that – please reach out! I’m curious to connect and share notes. Even if you only attended two, one, or even none at all – I would still appreciate hearing your thoughts on the themes of biology, technology, AI, and the biotech industry writ large.
My core conclusion is this: Tools change but purpose endures. More on that at the end of the post.
The Bio-IT World conference in Boston has been something of a touchstone in my professional life for more than two decades. My recent post, The More Things Change was informed, among other things, by a trip down memory lane where I scrolled through presentations and agendas from prior years.
It’s not entirely hyperbole to say that half the industry is busy refining large language models (LLMs) to accelerate the creation of manuscripts and regulatory filings, while the other half is building knowledge graphs, domain specific ontologies, and using natural language parsers (NLP) to cope with an already unmanageable tide of manuscripts and filings. Half of us are building machines to automate the smashing of data down into text and figures while the other half are trying to figure out how to automate the un-smashing process.
What this means is that we are still, as an industry, organized along pre-digital lines. We still assume legacy roles and processes that require humans to write notes to each other across certain organizational lines. Some larger and more forward looking organizations (including several pharmaceutical companies) are overcoming this with internal efforts to de-silo and go digital – but the handoffs between organizations, especially with regulators and the public, still assume human authors and human readers.
I had no idea what I was in for at Rev4. I figured that attending an AI conference in Manhattan really couldn’t be wrong – especially when the keynote roster included Neil DeGrasse Tyson, Steven Pinker, Steven Levy, Karim Lakhani, and Cassie Kozyrkov.
My big takeaway was that nobody has all the answers, there is a lot of “hustle and hope” going on, and there is going to be no shortage of interesting, important work for those of us who can keep our eyes on the prize and focus on KPIs and ROIs rather than on some particular tool or other.
Kozyrkov’s frankly brilliant and quotable keynote made a distinction between “thinking” and something she termed “thunking,” likening it to “the sound made by a brick, dropped from shoulder height, or perhaps a lovely evening of manual data entry.” She urged the audience to focus on thinking and leave the thunking to the machines.
She also drew a delightful parallel between “prompt engineering” for LLMs and any sort of management communication. Code is more precise but less expressive than language, so we should expect that it will take a couple of iterations to sufficiently specify the context and intent – no matter whether the audience is an LLM or a human engineer (or a human engineer using an LLM). “Don’t give instructions like a jerk,” she said.
Many of the conference themes were tautologies. How do AI leaders lead? Why they lead with AI of course. Who is going to “win” with AI? Well it’s the people who are “all in” on AI.
As the meme says: “Why tho?“
The hype reminds me of cloud discourse circa 2013. Back then, no matter your enthusiasm and excitement, it was challenging to articulate potential drawbacks or challenges of a “cloud transformation.” Technologists risked being painted as a luddite server-hugging dinosaurs for bringing up uncomfortable facts like “renting is always more expensive than owning,” and “okay, we won’t have the data center engineers anymore, but now we need cloud engineers.” It took about ten years for the industry to reach a sensible equilibrium on cloud. I expect a similar sort of timeline on AI.
I had never been to BIO before. It was by far the biggest of the three shows – with perhaps 15,000 people descending on Boston and taking over the entire seaport / convention district for several days. My takeaway is that it takes a village to get a drug over the finish line. There is a vast world of details, skillsets, teams, companies, and people who neither work directly on the science nor on the technology side of things. Without them all of our clever code and compounds would remain academic.
I also got a peek at how serious business development people approach a conference, and it is not at all the casual “wander the floor and see what’s up” random walk that I have historically taken. I tagged along to various networking events with friends in legal services, real estate, development, resales, and marketing. In doing so, I got a glimpse of the structure, discipline, tenacity and skill that it takes to succeed at what they do – which is helping us to succeed at we do.
Anybody who claims that they’ve got it all figured out, that it’s just this one simple trick, and that we won’t need most of the people in this industry is going to be sorely mistaken – just like all the other hopeful hustlers who preceded them.
I said at the top that “tools change but purpose endures:” This showed up most clearly at Rev4, but it was certainly present as a theme at Bio-IT and at BIO. Organizations who get distracted by the new shiny, who lose sight of the ‘why’ in favor of the ‘how,’ are going to encounter AI as friction. We are replacing a coal engine with an electric engine – but getting where we want to go remains the point of it all.
This connects back to the data smashing / re-hydration challenge I saw at Bio-IT. We continue to act as though manuscripts and filings are important in their own right. The real goal, for me at least, is to remove technology as a barrier to safer, more effective therapies and longer, healthier lives.
As I’ve said before, I’m still optimistic, and still all in.