Every boardroom in the world is having the same conversation right now. A vendor pitches an AI product. Someone on the executive team then asks the question that has become the most disruptive five words in enterprise software: “Can’t we just build it?”
Increasingly, the answer appears to be yes. The barrier to generating a working prototype has collapsed. With the right prompt and an afternoon with plenty of coffee, a modest technical team can produce something that looks convincingly like the product they were just quoted six figures to buy.
And so, the logic follows: why pay for what you can build yourself?
Daniel Zahavi thinks this instinct represents the peak of the current AI hype cycle and that the correction will be painful for the companies that followed it. Born in Kermanshah, Iran, in 1985, he immigrated to Israel at the age of 15, studied electrical engineering at the Technion, and earned a doctorate in Information Theory, the mathematical field underpinning modern large language models.
During IDF service, he held one of the highest security clearances in the military, working on projects touching the Prime Minister’s Office and the Intelligence Corps, before going on to develop drone interception systems and offensive cyber capabilities. He has now co-founded Arito, an AI analytics platform for finance and revenue teams, which raised $6 million in seed funding last month.
When Zahavi talks about commercial survival, there is biographical weight behind it. His defense technology business was blocked from export by Israel’s own Defense Ministry: a working product that couldn’t reach its market. He knows what it costs to build something that turns out not to be deployable.
That experience sharpens his read on the ‘Build vs. Buy’ trap now playing out in enterprise AI.
“Right now we are at the very top of that hype cycle that everyone believes that they can build whatever they need themselves easily,” he told me. “The amount of people that know exactly what they need and what they want is not very high. The portion that knows exactly how to describe that in very high resolution so that you can actually get what you need is even lower.”
Building anything genuinely useful with AI requires a clear understanding of the actual problem, and the ability to specify it with enough precision so that a model can act on it reliably. Most organizations have neither. They have a vague sense of the pain and a vocabulary borrowed from demos. But that produces impressive prototypes and disappointing production systems.
But the main point is what happens after launch. “Writing the code is only the first part,” he added. “Maintenance is a way, way bigger part of creating it the first time. I’m not even talking about security and privacy. A lot of the actual challenge continues afterwards.”
This is the consideration in the ‘Build vs. Buy’ debate that goes ignored. The prototype is cheap, but maintenance is not. And unlike a purchased product, where maintenance, iteration, and accountability belong to the vendor, the self-built version belongs to whoever built it, permanently.
Finally, Zahavi frames this as the difference between tools that produce what he calls “one-off artifacts” and tools that compound value over time. Asking an LLM a question and getting an answer is a one-off artifact, easy to replicate, easy to replace. But to build a system that learns how a specific finance team defines its metrics, tracks how those definitions evolve across fiscal years, and surfaces anomalies against that institutional context in real time is something much harder to build in a weekend or ‘vibe-code’.
“The only question that they need to ask themselves is: ‘Are they creating continuous long-term value for their customers and not just a one-off thing that can be solved easily?’ Because if it’s a one-off thing, then the chances of them being replaced by an AI prompt [are] very, very high.”
The hype cycle will correct. For Zahavi, who has spent a career building things in environments that were actively trying to stop him, like war zones, military bureaucracy, or the Defense Ministry that blocked his exports, the question of what survives hostile conditions is not theoretical.
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