AI is an extension of strategy

This feels a bit business-school-y to say ... but broadly speaking, new technology can be used in one of a few ways:

1. Strategy enhancement -- the technology doubles-down on existing moats and rights to win

2. Create new strengths -- expand the business in new directions

3. Operational efficiency -- make admin tasks quicker

4. Required technology adoption -- must-have technology just to keep up the times

I've been spending more and more time with different ideas and strategies on how to adopt AI well at our investment shop. We've had the chance to talk with a few other investment firms (as well as dive into other folks' LinkedIn posts, hear start-up pitches, hear vendor pitches, etc.) 

From what I've seen, I believe that the most successful AI adopters will fill up bucket #1 -- AI will extend an existing (winning) strategy even further. This would be a useless post without a few examples:

1. One AI-forward venture shop's core strategy is early-early-stage AI start-ups, targeting companies before they're necessarily even formed. They built tools (with AI) that allows them to (a) read, digest, and map the latest computer science papers and (b) track and rank people (e.g. X person left Y company, another person's stock is likely fully vested on Z months after being acquired, etc.). It feels like they are a step ahead of the masses (and a small step ahead of tools like Harmonic), allowing them to get a narrow lead in sourcing.

2. I've also built a few tools (using AI) for myself that doubles down on our core strategy at Connecticut Innovations. Our biggest constraint is that companies have a presence in the state, so I found a source (CT Business Registry) that gives us a good jumping off point. Layer in a few other AI assistance, and this has become a renewed source of sourcing alpha.

3. One much more contentious area is investment memo writing (of which I've written about before). I've seen venture shops with great exited companies have lengthy, almost unreadably-long memos generated with a heavy hand of AI. I'm sure there are also great, principled memos written about losing start-ups. 

Through the above lens, though, the question changes from "should we use AI to write memos" to "is writing an investment memo a core part of our investment strategy." I think for some places (like the Yale Endowment), to write is to think (a sentiment that I espouse). But, perhaps, the difference is one of strategy, not capability -- they intend to be some of the most patient capital, really hoping to partner over the next 20+ years. If it takes an extra 6-12 months to do diligence (and to write a memo and grapple with minutiae and diction and brevity), then so be it. 

Venture, especially now, may be different. In some AI deals, there is no time to think or do deep deep diligence; you must see and believe the uber-high-growth story. Perhaps here AI memos are good. Or perhaps for a single GP whose core strength is their network and business building, memos are a necessary evil, an artifact produced out of necessity (but not part of the alpha). Through this lens, memos are operational, not a strategy. 

I, of course, disagree, especially where I am today. I'd like to be a good investor (not just a good venture investor), and so the painstaking writing is worth it. 

Digital therapeutics are challenging for VC

 Recently did some digging into a digital therapeutic company (non-telehealth). I've heard the sector is very challenging, but after digging, good to put some concrete thoughts behind it:

• Digital therapeutics require all the same things that bio therapeutics require -- regulatory approval (albeit fewer trials than bio) and solid clinical evidence, but then also payer adoption and provider prescribing. 
• Payer adoption is hard (and where players like Akhili failed) -- you need to show clinical proof of medical necessity for payers to want to cover the digital therapeutic. This feels like valley of death number 1.
• Even if payers cover it, you need to get providers to prescribe it. Which means a sales/marketing team and a fleet of account managers, similar to pharmaceuticals but with a more untested care modality (digital vs. drug). Can be expensive and slow. 
• Provider un-enthusiasm. A more speculative observation that heads into behavioral aspects of healthcare, but I believe that (a) doctors understand how drugs work and are generally not hesitant to prescribe them if the benefit is clear, but (b) doctors don't really believe that digital therapeutics work. I think in general, many providers are slower to change prescribing practices, but especially slow when confronted with a totally new modality of care (digital vs pharma). It might be the sort of thing that's a generational change (like getting comfortable typing notes into a computer instead of handwriting), not a multiyear change.
• Digital therapeutic adoption/compliance. Most pharmaceuticals you take a few times a week, then forget about it -- it takes less than a minute to take a pill (and still compliance rates are sub-par). However, many digital therapeutics have a longer duration -- e.g. "watch X thing for 1 hour a day, 5 days/week". To adopt it well, patients need to change behaviors and habits, which can be difficult. (This thought needs a little more workshopping, but prescribing self-directed therapies -- like many digital therapeutics, like prescribing exercise -- feels harder. Would love for some data to refute/support this claim.)

AI will never have all the data

Some comments you hear after a pitch include things like "the founder was kind of annoying" and "man, that was hard to sit through, couldn't imagine being on that board." Strongly worded, yes, but honest (and often directionally accurate). No investor ever would want to put those words into any internal database.

Similarly: few firms require that you write a memo for investments you didn't make. (I've been trying to do this for a few that I've dug deep on but ultimately passed on -- but it takes a lot of time!) This valuable data is not captured anywhere -- how can you try to build an "investment analysis" AI if you don't have these "negative" data points? Will there not some weird bias inherent in the system?

Short of a neural tap (of which I hope I am not alive for), this information will never get recorded, and machines can only learn on things that ... exist somewhere. Transient but critically important thoughts will struggle to find their way to recorded data points, for better and for worse. 

Who's done the reading?

Back in high school, one of the core challenges a teacher needed to figure out was: who has done the reading? I remember one of my English teachers doing a pop quiz, that asked a simple question about a short story that tested: did you actually do the assigned reading? It's easy to fake it (or just be quiet) in class, and that was a good (or at least, memorable) solution. 

I'm reminded of this in two cases: (1) Reddit posts (and other similar comment-driven forums like Twitter) - I'd guess a slim majority actually read the article, and most read the comments, and (2) LLM content - I suspect most people are content with the LLM output and seldom read the underlying content in full. (And for #2: I think this is okay in many circumstances -- most LLM use cases don't demand information perfection.)

But with the age of AI comes a similar question to my high school days: who is doing the underlying readings, who is doing the hard work to try to understand and really dive in, who is using their brain as the primary processing engine (and AI primarily as a data gatherer)? Perhaps no surprise, I think I straddle this line well, but I believe others don't. For example, I see others using AI to read through a deck to help generate diligence questions, and others throwing large prompts into AI to generate content that they then edit down. Is it inherently bad? No, but I think it can erode the ability (and urge) to struggle with thinking. 

If we assume the above premise is correct (that some people misuse AI as a shortcut to thinking), the big question becomes: how can you tell who is who? And does it matter? "Surface readers" will provide equal value in many scenarios, and in some disciplines (like pre-seed venture), it may not even matter -- being on top of the current trends may matter more than, say, digging into the unit economics. But, if we assume it is a problem AND that it matters, then what questions or conversations do you have to figure out who did the readings, who's asking good questions? No good answers yet, but something that kicks around in the back of my mind as I think through what a good, modern, AI-infused investor looks like. 

AI to write investment memos?

 The more I've spent time with AI tools and investment memos, the more I believe that AI should never be the primary author of investment memos. Some thoughts I've had in my head:

• Having "AI write the first draft" turns the role of the investor from primary author to editor. For anyone who's edited papers or done code review, these are two drastically different roles. An analyst's job is to take a bunch of noise/data and capture the essence of them in as few words as possible. An editor's job is to gut check another person's work -- tone, diction, facts, etc. -- but seldom to digest the ideas completely. Editing is very hard work (arguably harder than being a primary author!), AND being an editor dilutes your brain with another person's key ideas. Your ideas become responses, not original ideas.
• Through this lens: one counterpoint I've heard is "have AI write the first draft then just remove what you don't need." Just as in editing, removing AI text is actually really hard! All of the stuff is usually valid, but how can you remove something that is useful but not important? I've seen this in memos recently -- 50+ pages, but missing some fundamentals.

KPIs for state-backed venture in tech

If you are a place-based venture firm, how do you measure success? I think this is actually one of the trickiest conundrums out there. As the saying goes (and I may be butchering it), you are what you measure. 

The challenge in tech is that (a) tech workers are well-paid but generally used to living wherever they want, (b) tech companies generally are more human capital light than other industries, and (c) many tech companies fail. So: if you measure by "how many jobs did we create/retain in the state": there is an incentive not to back the high-risk, high-reward moonshots (because if they fail, you lose jobs!), but instead to back good businesses with capped upsides. (There is also some incentive to fund start-ups that maybe shouldn't be funded!) If you measure by "how many start-ups did we create," you run into similar issues -- you can have many start-ups, but most/all fizzle out. Perhaps you measure by leverage -- how many external VC dollars went into our companies -- but then that incentivizes competing in the latest stage companies. 

Ultimately, the question is "what does a tech start-up ecosystem look like, and how do you know it's doing well." This factors in a lot more than just VC money alone can provide -- e.g. the city itself has to be a place people want to move to -- and so the KPIs are challenging. 

The human as the tastemaker

 In my view of AI: (a) the AI is able to collect a near infinite amount of data on almost any topic imaginable, but (b) the human is still the filter that determines what is relevant. Yes, AI can and will get better and better at this, but "taste" will always be a human characteristic. 

My current excitement is around making the market map building process better. Some of this is rote (e.g. finding all the M&A transactions, finding all the companies in the market), but some of it requires a little finesse (e.g. which companies actually get included in the set? how is the market segmented? who are the incumbents?) A lot of this can be automated by LLMs ... but I do think there's still a surprising amount of judgement involved. 

One example: M&A analysts get paid big bucks to determine what companies should go into a comp set. Another example from the software world: a lot of coding is getting automated, but things like database architecture require a deeper level of thinking that AI can't do alone. This is because building a database (and many other human tasks) have no perfect solution and requires weighing trade-offs -- so they inherently cannot be completely AI'ed away. It's a little like saying that AI can, with 100% certainty, pick what you should have for dinner tonight or pick what your child's name should be. (That being said, some decisions that might've felt consequential a few years ago might now just be automated/abstracted away e.g. by using an all-in-one provider like Supabase.) 

Overall, though, it's becoming clearer that all the AI efforts now will eventually lead to a larger emphasis on humans as the tastemakers. They are the ones who will make sense of all the inputs, who will give a lens on how to interpret it all, and who people will ultimately trust.