When we published the open invitation that seeded this paper, we were careful to frame it as a set of questions rather than a thesis. Six long conversations later, I am more convinced than ever that this framing was the right one. What follows is not a verdict on AI-assisted software development. It is a snapshot of a profession reasoning its way through a structural change in real time.

If 2025 was the year organizations realized something was wrong, 2026 will be the year they are forced to fix it.
The conversation around AI is shifting decisively. Hype is giving way to scrutiny. Experimentation is giving way to expectations. And organizations are being challenged to prove — not promise — that their AI systems are safe, reliable, and worth the investment.

Across the market, ambition outpaced execution. AI initiatives multiplied, budgets were approved at a rapid pace, and expectations were sky-high. But by the end of the year, a more sober reality emerged: many AI projects failed to deliver meaningful business value, not because the models were weak, but because the foundations were.