Startup ARR is less secure than ever, new research shows

AI has ushered in a lot of never-happened-before moments, but one of the most transformative is its impact on enterprise IT. Companies that have historically been cautious and committed long-term to what they buy are on pace to spend $4.25 trillion on technology in 2026, market researcher IDC predicts. It’s almost all driven by AI.

New research from venture capital firm Madrona shows that 74% of 150 enterprise IT professionals it surveyed plan to expand their AI budgets in the next 12 months, and the rest plan to hold spending steady. Yet these same enterprises say that fewer than half of their AI pilots ever make it into full production.

That’s actually an improvement. Last year, MIT famously reported that 95% of enterprise AI projects had failed in terms of ROI. Fewer than half succeeding is a pretty low bar, but it’s better than a 5% success rate.

But the most telling finding from Madrona’s report is that, even when an enterprise does roll out the AI tech, it doesn’t commit to it long term.

Some 77% of enterprises re-evaluate their AI vendors every six months or even on a rolling basis. “This creates a ‘fast in, fast out’ dynamic that is fundamentally different from traditional enterprise SaaS, where multi-year contracts provided a moat of inertia,” Madrona writes in the report. “In enterprise AI, switching costs are lower and the re-evaluation cadence is relentless.”

This has widespread implications for all those fast-growing annual recurring revenue (ARR) numbers startups report. Enterprise trial budgets are what fueled the initial AI boom of 2025. This year was supposed to be the year these big customers settled in and started committing long term to AI startups. Enterprise contracts are what allow so many AI startups to claim astronomically fast revenue growth — think the phenomenon of startups going from $0-$10 million in three months.

Yet, for the first time ever, enterprise revenue remains insecure, even after a startup’s AI product graduates out of a pilot phase and gets adopted by a company.

Part of the issue is that many AI startups haven’t fully landed on a good way to price their AI wares for enterprises. New research from VC firm Andreessen Horowitz that surveyed 50 technical AI buyers, found that more than half of them want AI fees tied to the work produced or other outcomes, rather than to usage like the number of tokens consumed.

Charging for usage like tokens is basically a SaaS-era business model. Once an enterprise knows it needs email, or HR software, or cloud storage, it’s merely a matter of how many employees or how much data it must pay for.

For AI, pricing “around the recognizable work” is what helps the startup prove its worth to the customer. When the fees revolve around, say, how many reports are processed, or tickets closed, or leads generated, this makes the product “economically valuable to both sides,” writes a16z partners Tugce Erten and Sarah Wang.

All of this means that AI has potentially ushered in a new era of enterprise experimentation. That opens doors to startups — enterprises are more willing to try their tech — but it also means an enterprise contract no longer secures long-term revenue. When or if enterprises will revert to their long-term buying habits remains to be seen.