Wrappers and Models

You need both

Every few years someone believes they’ve found a shortcut. Every few years, scale reminds them shortcuts don’t last.It’s been true since the 1980s. It’s still true now.

What has changed is access.

Today the same API serves a solo developer in Bangalore and a team at Google. Claude doesn’t know who you are. Your tokens aren’t special. They’re processed exactly like everyone else’s.

That’s new. And it quietly changes everything.

Not long ago, the thinking was simple: a few large labs would dominate AI. Three or four winners would own the models, and everyone else would build around them.

That story unraveled faster than expected.

Reasoning models changed the math. Mixture of experts lowered costs. DeepSeek trained R1 for a fraction of what anyone thought possible. You watch Claude Code pull ahead, then Codex scrambles the rankings. Every time the leaderboard stabilizes, something disrupts it.

OpenAI and Anthropic feel like early Google right now. Not Google today, the one with fourteen messaging apps nobody asked for. Google in 1999, when being close to that energy meant something. The companies building near them tend to do well. Not all of them. But enough.

Founders often ask what the playbook is.

But in moments like this, playbooks don’t exist yet. They’re written later by the people who shipped early, then sold to everyone else as history lessons.

Prompt skills became common quickly. Anyone with a weekend can learn them. So the advantage isn’t prompting. It isn’t even the model by itself. The real advantage sits between the two.

You need the wrapper and the model together.

Most teams end up choosing only one. Some focus on building beautiful products layered on borrowed intelligence. Others invest deeply in training models with no real product surface. Neither approach holds on its own. Wrappers without models are easy to copy. Models without wrappers struggle to reach users.

Durability begins when the two grow side by side.

A wrapper isn’t just interface design. It’s how users think through problems. It’s how workflows get shaped. Most importantly, it’s how data gets created.

A model isn’t just an engine in the background. It’s behavior refinement. Domain learning. The piece that converts signals from users into improved outcomes.

When these connect well, a feedback loop forms. Users shape the product. The product produces new data. The data improves the model. The model improves the experience.

The loop tightens over time.

Many products never build this loop. They remain pipes. Input, API, output. Nothing accumulates. Nothing compounds.

Copying has never been easier than it is now. Someone can see your product, grasp its logic, and rebuild its core over a weekend. Being first helps, but only briefly.

What compounds is something else: turning confusion into clarity.

AI feels chaotic today. New models appearing constantly. Pricing shifting overnight. Benchmarks disagreeing with each other. GPU clusters overbooked everywhere. Most users just want things to work. They want someone to absorb the complexity for them.

The teams that take messy ecosystems and present simple, trusted workflows build something defensible. Everyone else produces features that get copied by Tuesday.

Narrow focus helps. Not “AI for sales.” AI for one workflow, in one corner of one industry, where copying requires more than technical skill. It requires caring about the problem as much as you do.

Enterprise buying hasn’t changed much. The chief AI officer still needs a story for the board. Something defensible. But there’s a quiet question underneath every deal now: could we build this ourselves with three engineers and an API key?

Products that only offer surface functionality struggle here. Products where rebuilding the loop would take more than a sprint still justify buying.

The hard truth is that not every good product becomes a good business. Many things people love never scale profitably.

But this is a golden age for builders. Not hype. Pattern recognition. Cloud had a window like this. Mobile had one. AI has one now.

Satya has resources. Marc has reach. They also have committees and roadmaps and quarterly reviews. What they’ll ship in six months, a small team can ship Friday.

That gap doesn’t last forever.

But it exists right now.