Why Most Startups Are Doomed

FUTURE

Clarifying the Future of Software

After months of thinking about it and dozens of articles, papers, and keynotes, I think I finally understand the future of software. And my conclusion is that I am fully convinced that most AI and software startups are doomed.

In fact, almost everyone, including AI startups building the so-called “agents for x,” is doing it wrong. And it’s not just me who’s saying this; OpenAI and Anthropic have also expressed the same sentiment.

The funny thing? I was doing it wrong too!

By the end of today’s piece, I guarantee you will be able to evaluate any software product and determine whether it will survive AI.

This is an important read for:

Business owners using or planning to use AI software

Investors in software (public and private) or Big Tech and index funds in general

Business Leaders using software as part of their daily lives and those of their employees because it will help them understand how to deal with crappy salesmen and make better business decisions.

Here we go!

What Agents Actually Are

We’ve discussed agents extensively in the past, even giving a tech-aware definition that goes further than most people will.

However, my definition lacked one crucial piece, which an Anthropic researcher helped me identify.

An End-to-End Goal Achiever

For most people, agents are AI models that interact with an environment and have a goal they must achieve under a set of constraints. A great example is AlphaGo, an AI agent that played the board game Go to a superhuman level.

In that case, we had the following variables:

Environment: The board game

State: the state of the game being played (in reality, this environment is fully observable, but most environments aren’t. Therefore, normally, we would call this not a measure of a state but an observation of the state, a partial representation.

Goal: Win.

Constraints: The rules of Go.

The AI model then learns an action policy that maximizes the likelihood of achieving the goal under the environment and set constraints. This is a definition most people, even experts, will be okay with.

But it’s missing a crucial piece: engineering mindset. With agents, how the AI achieves the goal is equally important as achieving the goal… and I totally missed that part.

Crucially, while everyone also misses that part, it’s precisely the one that will define your software product's survival probability.

Today, we are fixing the definition for good (and actually visualizing it). But what do I mean by this?

Workflow vs Agentic

Cutting to the chase, most software thought to be agentic is, in fact, not. I realized this while seeing how the Head of Applied AI at Startup Ramp used agents internally.

Ramp is a company that helps other companies, mainly startups, by offering corporate charge cards, expense management, and bill-payment software.

In his keynote at an agent’s conference, he defined three ways of building software:

Over-engineered code, where engineers look for fully deterministic and, often, hardcoded and rigid…

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