There has never been a greater time to be an AI engineer. In the event you mix technical chops with a way of product design and a keen eye for automation, you would possibly have even built a highly useful app over a weekend hackathon. So, is it time to pitch VCs? Common wisdom says that in case you can discover a market gap, deliver real value, and ship quickly, you’ve the recipe for a venture-backed startup. You’re likely watching countless peers do exactly that. But before you join the hunt for a billion-dollar unicorn, you’ve to ask yourself: would you be higher off herding donkeys?
and startups are changing. Not incrementally, but fundamentally. Over the past yr, we’ve met team after team doing all the pieces right: moving fast, constructing useful products, targeting real customer pain, delivering real value. And yet, we passed on a lot of them. Not since the teams were weak, but since the moats that may protect their value have fundamentally eroded.
Probably the most basic rule of enterprise hasn’t modified: an organization needs differentiation and defensible moats to sustain high-margin success at scale. But what counts as a defensible moat has shifted dramatically, with the bar rising to a much higher level. If what you are promoting lacks a real moat, whether proprietary data or unique expertise that may withstand a military of highly-skilled AI agents, it’s going to inevitably face disruption throughout the commoditization kill zone.
Two years ago, we coined the term to explain the long run we saw AI painting. Technology and products have gotten truly magical, unlocking previously inconceivable capabilities yet they’re almost completely commoditized by frontier models. We remain optimistic in regards to the “magic” part: it introduces an enormous economic opportunity by unlocking value that was previously inaccessible. However the commoditization risk is real and disruptive, making entire areas uninvestable.
On this piece, we would like to unpack that commoditization dynamic: why the unicorn is even harder to hunt in the present landscape. But we also need to suggest that a brand new creature, or quite, a really familiar one, is about to emerge: herds of donkeys.
Commoditization from Every Direction
AI is eating software and services, but at the identical time, the unit economics of making value are drastically changing. The price, expertise, time, and overall resources required to bring a product to market are spiraling down. That changes all the pieces, and commoditization is rushing in from all sides.
The user as builder. There may be a brand new class of apps replacing previously purchased software: the ephemeral app. Whether it’s an easy prompt that creates an artifact, a Claude Code session, or some combination of skills, tools, and plugins users can now construct any app they’ll imagine. Any experienced engineer knows that constructing even probably the most complex module for a single, one-time user is trivial; the standard complexity and expertise kick in just when making it modular, generic, scalable, and maintainable. A single user-builder is a formidable competitor to a complete SaaS company with regards to constructing precisely the app she needs at a given moment. This scales to groups as well, and thru organizational memory, beyond that.
The explosion of competitors. As coding agents improve and reach the extent of skilled human engineers at much lower cost and complexity of management the entry barrier to becoming a SaaS company drops dramatically, resulting in orders of magnitude more competitors. The result’s crowding at every level, and we already see it in our dealflow. Every use case now has quite a few startups attacking it, each ranging from a small beachhead where they’ve some unfair advantage, hoping to expand and win the market. But after they raise their heads, they see beachheads throughout them, with no clear differentiation. These firms may deliver real value, some may even be profitable but they don’t make sense as venture-backed businesses.
Enterprise and startups have at all times been a numbers game of hits and misses. But when the ratios shift by orders of magnitude, with way more firms, solo founders, and tiny teams all enabled by the identical tools, the old rules break down. You find yourself with many more misses than hits, to the purpose where the VC model itself stops working.
“It’s All About Distribution” Or Is It?
An argument we regularly hear is that in a world where software is a commodity, it’s all about distribution: move fast, capture those first customers, and also you win. Unfortunately, commoditization and AI are rewriting the foundations of go-to-market and distribution as well.
First, there may be the crowding problem. In the event you can move quickly, rapidly prototype an MVP, and sign a pilot, all in 4 weeks with two people, so can your many competitors.
Second, not only does AI unlock ephemeral, hyperpersonalized apps, but integrating traditional software has also develop into much easier, quicker, and cheaper. Traditional SaaS products arrive generic and require complex, expensive integration projects, a significant source of stickiness and first-mover advantage. In the brand new world, where these integrations might be automated or regenerated on the fly, those moats are rapidly disappearing. As lock-in effects weaken and the client not needs to fret as much about future support and compatibility, they’ll deal with what they need now, and who does it best, especially in highly commoditized and competitive markets.
Consequently, we expect software procurement AI agents to emerge that replace old, human-led methods. These agents could bid and test in real time for required capabilities, threatening to render brand, distribution, and first-mover advantage largely irrelevant. The economics are clear: when switching costs approach zero, loyalty follows.
Finally, Big Tech is moving up the stack and across verticals. Consider how frontier model providers and platform owners, think email, chat, and docs within the enterprise, or mobile, search, and social for consumers, can now construct vertical use cases themselves, faster and higher than ever. Google adding AI capabilities directly into Workspace, Microsoft embedding Copilot across Office, Apple integrating intelligence into iOS. These giants are moving into territory that when belonged to startups, leveraging distribution benefits that startups simply cannot match. The flexibility to develop at much higher velocity applies to Big Tech as much because it does to a two-person startup, and Big Tech starts with a billion users.
That is the brand new reality within the software and services market, as useful intelligence becomes a commodity.
Donkeys, Not Unicorns
Is that this the tip of entrepreneurship, is there no path forward for strong small teams who can deliver quick value to underserved markets? Removed from it.
There may be clearly an enormous opportunity for brand spanking new unicorns, just with a better bar. That’s the chance we’re focused on as a VC. But we also imagine that the superpowers and speed 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 agents and the low price of development to automate and scale the creation of value-generating businesses? Can a solo founder construct a herd of passive-income-generating donkeys at scale?
Take into consideration what that appears like in practice. You automate ideation and market research to generate, prioritize, and prune a pipeline of ideas. You automate user research and interviews, customer outreach, hypothesis generation, prototyping, experimentation, and evaluation. You bootstrap these businesses, run them in parallel, kill the losers, double down on the winners, and adapt as needed.
Imagine a founder running fifteen micro-businesses concurrently, each serving a narrow area of interest targeting an underserved market segment they’ve access to: one automating compliance reports for small European fintech firms, one other generating custom training materials for logistics firms, a 3rd managing invoicing workflows for freelance consultants. Likely even with geographical focus. None of those is a billion-dollar market. None of them will land on a TechCrunch headline. But each generates regular, sustainable revenue, and together they compound into something meaningful. The founder isn’t managing fifteen teams; AI agents handle the construct, the iteration, the client support. The founder’s job is portfolio management: which donkeys to feed, which to retire, which niches to enter next.
That is the inverse of the enterprise model. As an alternative of concentrating risk into one massive bet, you distribute it across many smaller ones. As an alternative of needing a 100x return on a single company, you construct a portfolio where the combination consequence is what matters. The mathematics is different, the danger profile is different, and critically, it doesn’t require outside capital, which implies the founder retains full ownership and control.
We recommend this path to groups we meet who’re doing excellent work but operating in spaces where the moat simply isn’t deep enough for a venture-scale consequence. Often very small and efficient, these teams are perfectly positioned to bootstrap quite than raise. The donkey path isn’t a consolation prize. For a lot of founders, it often is the smarter play.
This isn’t a venture-scale play, and that’s precisely the purpose. It’s a brand new avenue for entrepreneurs willing to trade the dream of 1 massive consequence for a portfolio of smaller, sustainable ones, and to make use of AI to make that portfolio manageable at a scale that was previously inconceivable.
We imagine there may be an actual opportunity here, and we’ve began exploring the tools to make it work. Stay tuned.
