Yariv Adan, Basic Companion, ellipsis enterprise
There has by no means been a greater time to be an AI engineer. If you happen to mix technical chops with a way of product design and a eager eye for automation, you might need even constructed a extremely helpful app over a weekend hackathon. So, is it time to pitch VCs? Widespread knowledge says that if yow will discover a market hole, ship actual worth, and ship shortly, you’ve gotten the recipe for a venture-backed startup. You’re possible watching numerous friends do precisely that. However earlier than you be a part of the hunt for a billion-dollar unicorn, you need to ask your self: would you be higher off herding donkeys?
and startups are altering. Not incrementally, however essentially. Over the previous yr, we’ve met workforce after workforce doing all the things proper: shifting quick, constructing helpful merchandise, focusing on actual buyer ache, delivering actual worth. And but, we handed on a lot of them. Not as a result of the groups have been weak, however as a result of the moats that may defend their worth have essentially eroded.
Probably the most fundamental rule of enterprise hasn’t modified: an organization wants differentiation and defensible moats to maintain high-margin success at scale. However what counts as a defensible moat has shifted dramatically, with the bar rising to a a lot increased degree. If your corporation lacks a real moat, whether or not proprietary knowledge or distinctive experience that may stand up to a military of highly-skilled AI brokers, it can inevitably face disruption inside the commoditization kill zone.
Two years in the past, we coined the time period Commoditized Magic to explain the long run we noticed AI portray. Expertise and merchandise have gotten actually magical, unlocking beforehand not possible capabilities but they’re virtually fully commoditized by frontier fashions. We stay optimistic concerning the “magic” half: it introduces a large financial alternative by unlocking worth that was beforehand inaccessible. However the commoditization threat is actual and disruptive, making whole areas uninvestable.
On this piece, we need to unpack that commoditization dynamic: why the unicorn is even tougher to hunt within the present panorama. However we additionally need to counsel {that a} new creature, or reasonably, a really acquainted one, is about to emerge: herds of donkeys.
Commoditization from Each Path
AI is consuming software program and providers, however on the identical time, the unit economics of making worth are drastically altering. The price, experience, time, and total sources required to carry a product to market are spiraling down. That modifications all the things, and commoditization is dashing in from all sides.
The person as builder. There’s a new class of apps changing beforehand bought software program: the ephemeral app. Whether or not it’s a easy immediate that creates an artifact, a Claude Code session, or some mixture of expertise, instruments, and plugins customers can now construct any app they will think about. Any skilled engineer is aware of that constructing even probably the most advanced module for a single, one-time person is trivial; the standard complexity and experience kick in solely when making it modular, generic, scalable, and maintainable. A single user-builder is a formidable competitor to a whole SaaS firm in the case of constructing precisely the app she wants at a given second. This scales to groups as effectively, and thru organizational reminiscence, past that.
The explosion of opponents. As coding brokers enhance and attain the extent {of professional} human engineers at a lot decrease value and complexity of administration the entry barrier to turning into a SaaS firm drops dramatically, resulting in orders of magnitude extra opponents. The result’s crowding at each degree, and we already see it in our dealflow. Each use case now has quite a few startups attacking it, every ranging from a small beachhead the place they’ve some unfair benefit, hoping to broaden and win the market. However after they increase their heads, they see beachheads throughout them, with no clear differentiation. These firms could ship actual worth, some could even be worthwhile however they don’t make sense as venture-backed companies.
Enterprise and startups have all the time been a numbers sport of hits and misses. However when the ratios shift by orders of magnitude, with much more firms, solo founders, and tiny groups all enabled by the identical instruments, the outdated guidelines break down. You find yourself with many extra misses than hits, to the purpose the place the VC mannequin itself stops working.
“It’s All About Distribution” Or Is It?
An argument we frequently hear is that in a world the place software program is a commodity, it’s all about distribution: transfer quick, seize these first clients, and also you win. Sadly, commoditization and AI are rewriting the foundations of go-to-market and distribution as effectively.
First, there’s the crowding downside. If you happen to can transfer shortly, quickly prototype an MVP, and signal a pilot, all in 4 weeks with two folks, so can your many opponents.
Second, not solely does AI unlock ephemeral, hyperpersonalized apps, however integrating conventional software program has additionally change into a lot simpler, faster, and cheaper. Conventional SaaS merchandise arrive generic and require advanced, costly integration tasks, a serious supply of stickiness and first-mover benefit. Within the new world, the place these integrations will be automated or regenerated on the fly, these moats are quickly disappearing. As lock-in results weaken and the shopper now not wants to fret as a lot about future help and compatibility, they will concentrate on what they want now, and who does it greatest, particularly in extremely commoditized and aggressive markets.
Consequently, we count on software program procurement AI brokers to emerge that substitute outdated, human-led strategies. These brokers may bid and take a look at in actual time for required capabilities, threatening to render model, distribution, and first-mover benefit largely irrelevant. The economics are clear: when switching prices method zero, loyalty follows.
Lastly, Large Tech is shifting up the stack and throughout verticals. Contemplate how frontier mannequin suppliers and platform homeowners, suppose e-mail, chat, and docs within the enterprise, or cellular, search, and social for shoppers, can now construct vertical use circumstances themselves, sooner and higher than ever. Google including AI capabilities straight into Workspace, Microsoft embedding Copilot throughout Workplace, Apple integrating intelligence into iOS. These giants are shifting into territory that after belonged to startups, leveraging distribution benefits that startups merely can’t match. The flexibility to develop at a lot increased velocity applies to Large Tech as a lot because it does to a two-person startup, and Large Tech begins with a billion customers.
That is the brand new actuality within the software program and providers market, as helpful intelligence turns into a commodity.
Donkeys, Not Unicorns
Is that this the tip of entrepreneurship, is there no path ahead for sturdy small groups who can ship fast worth to underserved markets? Removed from it.
There’s clearly a large alternative for brand spanking new unicorns, simply with the next bar. That’s the chance we’re centered on as a VC. However we additionally consider that the superpowers and pace of AI have unlocked one other avenue for entrepreneurs, one which doesn’t require enterprise capital in any respect.
What if, as an alternative of chasing a single elusive unicorn, you used brokers and the low value of improvement to automate and scale the creation of value-generating companies? Can a solo founder construct a herd of passive-income-generating donkeys at scale?
Take into consideration what that appears like in observe. You automate ideation and market analysis to generate, prioritize, and prune a pipeline of concepts. You automate person analysis and interviews, buyer outreach, speculation era, prototyping, experimentation, and evaluation. You bootstrap these companies, run them in parallel, kill the losers, double down on the winners, and adapt as wanted.
Think about a founder working fifteen micro-businesses concurrently, every serving a slender area of interest focusing on an underserved market phase they’ve entry to: one automating compliance experiences for small European fintech corporations, one other producing customized coaching supplies for logistics firms, a 3rd managing invoicing workflows for freelance consultants. Most likely even with geographical focus. None of those is a billion-dollar market. None of them will land on a TechCrunch headline. However every generates regular, sustainable income, and collectively they compound into one thing significant. The founder isn’t managing fifteen groups; AI brokers deal with the construct, the iteration, the shopper help. The founder’s job is portfolio administration: which donkeys to feed, which to retire, which niches to enter subsequent.
That is the inverse of the enterprise mannequin. As a substitute of concentrating threat into one huge guess, you distribute it throughout many smaller ones. As a substitute of needing a 100x return on a single firm, you construct a portfolio the place the mixture end result is what issues. The mathematics is totally different, the danger profile is totally different, and critically, it doesn’t require outdoors capital, which suggests the founder retains full possession and management.
We suggest this path to groups we meet who’re doing wonderful work however working in areas the place the moat merely isn’t deep sufficient for a venture-scale end result. Typically very small and environment friendly, these groups are completely positioned to bootstrap reasonably than increase. The donkey path isn’t a comfort prize. For a lot of founders, it might be the smarter play.
This isn’t a venture-scale play, and that’s exactly the purpose. It’s a brand new avenue for entrepreneurs prepared to commerce the dream of 1 huge end result for a portfolio of smaller, sustainable ones, and to make use of AI to make that portfolio manageable at a scale that was beforehand not possible.
We consider there’s a actual alternative right here, and we’ve began exploring the instruments to make it work. Keep tuned.
