If 2025 felt like the year AI almost became magic and then immediately turned into plumbing, congratulations. You were paying attention.

For a brief and unhinged stretch, models felt AGI-adjacent. Demos were jaw-dropping. Twitter became unreadable. Every startup pitch sounded like a spiritual awakening with a deck. And then something deeply inconvenient happened.

Reality arrived. With a clipboard.

Ten years ago, software companies sold tools.
In the next ten, they’ll sell results.
And if that sounds like semantics, it’s because most people haven’t realized that AI just killed “Software-as-a-Service” and replaced it with something bigger — what Foundation Capital calls “Service-as-Software”.

The Shift No One Saw Coming

In the SaaS era, customers paid for access — a login, a dashboard, an API key.
The responsibility for performance sat with the user.

Here’s the uncomfortable truth: we’ve been measuring AI’s impact with the wrong instruments. Adoption metrics, GDP blips, and “AI usage” dashboards are rear view mirrors. If you want to see where the economy is actually headed, look at what models can do on real work today. That’s what GDPval measures, and the results should make every exec, policymaker, and operator sit up. (GDPval paper)

Quick roadmap:

• A contrarian take on AI’s “productivity paradox”
• What GDPval is and why it matters more than hype cycles
• The jaw droppers: speed, cost, and quality where models already rival humans
• Where models still fail and how simple scaffolding fixes move the needle
• The playbook for teams to capture value now without breaking things
• Why this reframes the future of work and your next budget cycle

The Bold Claim: We’re Misjudging AI’s Economic Impact

Measuring AI by “adoption rate” is like measuring electricity by the number of lightbulbs sold in 1900. Economic impact from general purpose tech lags because organizations need new processes, controls, and culture. GDPval sidesteps that lag by evaluating frontier models on actual, economically valuable tasks across the top nine U.S. GDP sectors, with 1,320 deliverables covering 44 occupations.

The headlines hit like a cold shower: “MIT Report: 95% of generative AI pilots at companies are failing.” LinkedIn feeds exploded with doom-and-gloom takes. “The AI bubble is bursting,” they declared. “We told you it was all hype.”

But here’s the thing about viral panic stories: everyone focuses on the wrong part.

While the internet obsessed over the 95% failure rate, they completely missed the real story. What did that elite 5% of companies do differently? What separates the winners from the $30-40 billion in wasted enterprise spending?