Capital Efficient #1
Our inaugural weekly round-up.
I’m Pat McGovern and welcome to the first edition of Capital Efficient. We are still experimenting with formats here at CEHQ so expect the unexpected over the next few weeks as we find our groove. Without further ado, our inaugural post:
Weekly Radar
Vertical AI -> Custom Enterprise Software
The opportunity AI presents to automate large swaths of back-office and middle-office tasks in corporate settings is undoubtedly real. But exactly how AI’s promise and abilities will be delivered to buyers is still being worked through. Many of today’s best vertical AI founders are building module-based platforms with an eye toward ultimately becoming the default platform for their given industry to automate repetitive, formulaic workflows.
However, in the very largest organizations, there is a certain scale and style of business where vertical AI platforms - which are inherently “one-size-fits-all” - may not be enough. Large companies often have complex and dated internal storage systems, mountains of technical debt, and highly specific internal processes they don’t want to deviate from. It is very difficult to build an industry platform that is applicable across the top end of any given market; and those that succeed often do so by offloading customization to entire ecosystems of SI’s and consultants.
To date, the needs for automation and internal tooling within the F500 have been filled by “digital transformation” consultancies and we have seen the Accentures and Deloittes of the world make hundreds of millions in top line by serving as custom dev shops building internal AI tools for the world’s largest orgs. My belief is much of this consulting revenue will be eaten by productized versions of the services these firms provide, and these products will be built by best-in-class technologists who can drive faster implementations and better outcomes.While I love the traditional vertical AI business model, I am also increasingly excited about platforms that can enable rapid deployment of automation solutions and internal apps within the F500. With many predicting that the cost of code is heading towards zero, companies will demand infinite customization and will need a trusted technical party to enable this shift.
The work these custom enterprise software platforms do may start off looking like consulting, but they will evolve to have some kind of technical asset at their core (think Palantir’s Gotham or Metropolis) that can be re-used across engagements and each engagement should serve to make the core asset faster, more effective, and give it a broader range of capabilities. These businesses will rely on FDEs to meet customer needs and will need to command high ACVs to justify their human capital and deployment costs. A handful of companies (Distyl, Braintrust, etc.) are already working in this space but it’s a massive market, with tons of white space, and is one I believe will have multiple winners targeting different markets and different size enterprises.
If you are building this kind of software, I would love to chat. Get in touch by replying to this email.
Early AI Winners & Large But Under The Radar Verticals
Some of the recent growth-stage fundraising announcements I’ve been seeing have me re-evaluating the vertical AI opportunity set. Many of the more obvious verticals have seen multiple, extremely well-funded AI-native challengers break out in the past 18-24 months and new entrants looking to compete are being crowded out and having trouble raising. Elad Gil had a great piece last week which surveyed how verticals like law and medicine are shaking out and seeing early winners emerging. With many “obvious” verticals seeing the early AI winners become semi-entrenched, what are the second order use cases that can scale to an IPO-level exit?
Some areas that may fit this description where I am spending time: equipment dealerships, franchisors, building supply distributors, home healthcare agencies, insurance agencies/carriers, pharmacies, and medical device distributors.
News Roundup
Money Can’t Buy You Class
Kylie Robison from Wired broke the story this week that Meta has been offering Thinking Machines Labs employees comp packages in the $100MM-$1B range to jump ship. And even more shocking - most of these TML employees have turned Meta down. This is either a big vote of confidence in the value of TML’s yet-to-be released product or a sign of how toxic the Meta brand is in the eyes of some engineers. Escalating AI engineer pay raises an interesting question - how can vertical AI companies attract top AI talent given the riches on offer at Google/Meta and the foundation model companies for this skill set?
Toma Brings Voice AI To The Dealership
Toma announced a $17MM Series A from a16z to build a voice-first OS for car dealerships. This is a good early example of startups using Voice AI as a wedge to break into what has been a notoriously tough space to crack. As the ceiling on what software is capable of doing rises with each new foundation model release, expect to see more big vertical funding rounds. It will be interesting to see if Toma can eventually displace Reynolds, CDK, etc. From my perspective, Toma fits the new “default” building model for vertical AI founders who are selling to mid-market and enterprise companies, where the founders need to fully embed with their early customers. Toma’s story also highlights the growing importance of design partners to serve as an AI sandbox as you tinker with early product.
Anthropic’s War Chest Grows
Anthropic is reportedly raising $5B at a $170B valuation - this is up 3x from their last raise six months ago. It’s a two horse race at the moment between OpenAI + Anthropic in the lead; this dynamic may also explain Zuck’s eye-watering willingness to pay up for their alumni in an effort to draw even.
Figma’s IPO Is The Hottest Ticket In Town
Figma’s $1.2B IPO is reportedly 40x oversubscribed and the expected IPO listing market cap has grown from ~$13B to $20B over the last week. Hold on for a serious first-day pop. And get ready for even more startups to call themselves the Figma for X (or is it the Cursor for Y?). Does this mean the IPO window is open again?
What I Am Reading
“Drones & The Future of Warfare” (New Yorker) - the legend Dexter Filkins reports from Ukraine on how drone warfare has changed the battlefield and what it means for national security. Must read for any defense-tech investor.
“The Boring AI Questions That Actually Matter” (Margins by Ranjan Roy & Can Duruk) - good check-in on where we are in the AI platform shift and what chapters remain to be written.
“The Website at the End of the Internet” (NY Mag) - John Herman chronicles the rise of Reddit, its breakthrough into the mainstream, and the platform's relationship with AI.
“Mary Had Schizophrenia, Until She Didn’t” (New Yorker) - fascinating article about the link between mental illness and autoimmune diseases, and how autoimmune treatments might have a huge “off-label” use case hiding in plain sight.