AI in 2026: When the Demos Stop Carrying the Story
From wow moments to economic reality
Disclaimer: This publication and its authors are not licensed investment professionals. Nothing posted on this blog should be construed as investment advice. Do your own research.
Happy new year everyone!
For the last few years, AI has mostly been judged by how impressive it looks in public. First it was OpenAI and ChatGPT suddenly sounding smarter than anyone expected, then Sora arrived and made it feel like video production itself was about to be rewritten. Capital (and especially the NVIDIA stock) reacted immediately. Big tech spent hundreds of billions on GPUs, data centers, long-term energy contracts, and custom chips, all on the assumption that intelligence would naturally translate into revenue at scale.
From where I sit, that assumption has always felt fragile. As a software engineer working with these systems daily, and as someone who has built startups and runs an agency that ships production software for paying clients, I’ve learned that technical capability and economic leverage rarely move in sync. AI looks magical in isolation, but once it lands inside real environments with SLAs, compliance constraints, edge cases, and accountability, the story becomes far less clean.
So far, the numbers reflect that. AI revenue remains small relative to the balance sheets now supporting it. Even compared to companies like Apple or Alphabet, AI is strategically important but still not a standalone cash engine. And almost none of the infrastructure spend accrues to the average AI startup. Most live one layer above the models, packaging the same intelligence in slightly different forms and hoping focus or UX creates leverage.
Adoption looks big until it meets real workflows
This is why 2026 feels like a turning point. Not because the models stop improving (they won’t), but because attention is shifting from demos to deployment. From the outside, adoption looks everywhere. Engineers rely on AI daily, marketers treat it as default tooling, students grow up with it, and many people quietly get more done because of it.
Inside organizations, progress is slower and messier. Formal adoption is uneven, pilots are common, and many fade out without drama, sometimes because it might reflect bad on employees to speak loud about AI failures. I see this constantly in agency work. Teams build a proof of concept, show early promise (especially by euphorized team members), then hit harder questions than expected. Who signs off on outputs that are sometimes wrong. How failures are handled when automation breaks something subtle. How probabilistic systems fit into organizations built around certainty and blame.
AI often gets close enough to be useful without ever fully owning the outcome. Responsibility does not transfer along with the output. Legal risk, on-call duty, customer fallout, and reputational damage still sit with humans. That gap between assistance and ownership is where most AI ROI disappears, and it is also where many AI startups quietly stall.
Even focused vertical tools like Harvey AI (AI for lawyers) or Sierra (AI for customer service) run into this reality. Their success depends less on model quality and more on whether organizations are willing to change workflows, incentives, and responsibility boundaries. That kind of change is slow, political, and consistently underestimated.
AI wrappers: Startups that don’t own their tech & what it means for Big Tech
There is a seductive belief that AI finally restores the compounding effect to software. Smarter tools, faster iteration and more leverage per person. From my experience and in practice, AI dramatically lowers the cost of building while simultaneously shortening the half-life of advantage for anyone who does not control their economics.
This is why AI wrapper economics should feel familiar to anyone who lived through previous platform shifts. It resembles businesses built entirely on APIs whose fate depended on someone else’s roadmap. Many of those AI wrapper companies were well executed. Some became case studies.
All of this matters because AI is no longer a side theme in markets. It is a core assumption. By late 2025, AI-related companies made up a massive share of the S&P 500, and current valuations assume that adoption accelerates and margins expand.
A large share of what gets labeled an AI company today is effectively a wrapper. The core intelligence comes from OpenAI, Anthropic, or a hyperscaler. The startup builds a thin product layer on top. From the outside, it looks like SaaS. Under the hood, it behaves like a variable-cost service.
When these systems run at scale, the tension becomes obvious. Usage does not follow neat pricing assumptions. Customers expect more volume for less money. Model behavior changes without warning and margins compress in ways pitch decks never modeled. In 2026, more investors will realize that many AI startups are not failing because adoption is slow, but because the economics were fragile from the start.
This puts a lot of pressure on the big AI providers. To live up to the expectations, they need to push into real production. According to a MIT research from July 2025, 95% of of businesses’ AI pilots failed to generate a return at all. That means Big Tech needs to act fast.
Might agents be the solution to a stalling adoption curve?
To push past the adoption friction, the industry is leaning into agents. Systems that take tasks and execute them end to end. From a buyer’s perspective, outcomes are easier to understand than prompts. From a founder’s perspective, results are easier to sell than assistance. It’s as simple as replacing humans, not augmenting their capabilities.
From an operator’s perspective, agents surface the hardest questions immediately. Responsibility, failure modes, and substitution stop being abstract. Startups like Artisan framing agents as replacements for human beings rather than tools may gain attention, but they also turn technical products into cultural statements.
What 2026 actually sorts out
By the end of 2026, it will not be clear whether AI “won,” but it will be clear which advantages survived contact with a reset. Some companies will have restructured workflows deeply enough to make AI stick. Some startups will prove they actually control their cost curves. Many others will turn out to be compounding inside someone else’s system, on borrowed time.
From my perspective, AI does not need to fail to disappoint. It only needs to scale more slowly than expected, cost more than hoped, and concentrate value higher up the stack than most narratives assume.
That is why 2026 feels less like a breakthrough year and more like a sorting year. Not between AI and non-AI companies, but between businesses that can survive repeated resets and those that quietly plateau while the story moves on.