Jeffrey Zang: The AI Wrapper Boom Is Missing the Real Problem

The founder of Proletic argues that the next wave of AI products will be won by teams that build restrained, useful software people actually want to keep using.

The AI market has become crowded with tools that promise to handle one more narrow task. Another meeting note-taker. Another writing helper. Another app built around a small workflow that may not have needed a company around it. Jeffrey Zang, the 19-year-old founder of Proletic, sees the pattern clearly: too many AI products are being built because large language models made them easy to build, not because users were waiting for them.

That frustration sits behind much of his work. Zang is building in the AI-agent space, but his view of the industry is sharper than simple enthusiasm. He believes the market is full of products that mistake access to a powerful model for a meaningful product.

“A lot of AI products feel like they were made because someone realized they could make them quickly,” Zang said. “That is not the same thing as solving a problem people actually care about.”

His argument lands at a strange moment for artificial intelligence. AI agents are one of the dominant topics in the field, especially computer-use agents that can click, type, operate apps, and move through a computer more like a person would. At the same time, the barrier to launching AI software has dropped. With APIs, frameworks, and new coding tools, small teams can build products faster than ever.

Zang sees both sides of that shift. He is not dismissing the opportunity. He is warning that speed can make weak ideas look stronger than they are.

Proletic grew from the same frustration Zang sees across the AI market: too much attention on what the technology can do, and not enough on whether the product deserves a place in a user’s workflow. For him, the test is not whether an AI feature can be demonstrated. The test is whether someone will keep using it after the novelty wears off.

“The market rewards speed right now, but users reward usefulness over time,” Zang said. “Those are not always the same thing.”

That belief gives his critique a practical edge. Zang is not arguing for slower building or safer thinking. His own record points in the opposite direction. He believes in fast shipping, but not in using speed as a substitute for judgment. A product still needs a reason to exist beyond the fact that the underlying technology is impressive.

“AI does not save a weak product,” he said. “Sometimes it makes the weakness easier to see.”

The problem, as Zang sees it, is that the model has become the center of too many product stories. Teams talk about what model they use, how advanced it is, and how impressive the output looks in a demo. Those things may matter, but they do not answer the harder question of whether the software fits into real behavior.A user does not return to a product because a founder used the right model.

A user returns because the product made something easier, faster, clearer, or less annoying.

“People over-index on the model,” Zang said. “They talk about capability when they should be talking about behavior. What does the user do before your product, what do they do after, and why would they come back?”

That question has shaped Zang’s own building habits. He has learned to treat usefulness as something that must be tested, not assumed. Opus, his computer-use agent, reached 500+ beta users after a three-day launch and drew more than 200,000 views. OSX-Query, his later agent harness, pushed the same question further: how can computer-use agents become lighter, faster, and practical enough to run outside expensive demos?

“A demo can impress people for a day,” Zang said. “A product has to earn repeat behavior.”

The early AI wave has rewarded attention, and attention can be earned with a clever launch, a viral clip, or a narrow tool that looks useful in a controlled moment. Zang is interested in what happens after that moment passes.

Do people keep using it? Does it reduce a real burden? Does it become part of the work without demanding more attention than it saves?

Those are not flashy questions, but they are the questions that decide whether software survives.

“Everyone wants the big reveal,” Zang said. “But the real product is what happens on day 30, when the user is not excited anymore and just wants their work to be easier.”

His perspective has been shaped by working close to users. As a founding engineer at 10x, Zang helped build an AI app that reached 2,000+ users and profitability with under 5% monthly churn. At Nokia, he built an LLM-powered, RAG-based internal assistant adopted by 200+ employees and helped reduce compute costs by about $1,000 per month. Those experiences gave him a view of AI that is less about spectacle and more about whether the tool continues to work when people are no longer impressed by the category itself.

"Users do not care that you used AI,” he said. “They care that something annoying became easier, faster, or less distracting.”

That is why Zang is skeptical of what he calls the flood of thin AI products. The issue is not that small tools are useless. Some are genuinely valuable. The issue is that many are built around a task so narrow, or a workflow so shallow, that the product depends on AI excitement rather than durable need.

For a while, that can look like momentum. People try the tool. Investors take meetings. Social posts travel. But Zang believes that kind of attention is fragile.

“Novelty can get someone to open the product once,” he said. “It cannot make them build a habit.”

His answer is product discipline. Build around a real problem. Remove more than you add. Watch for bloat before it becomes the product. Avoid the temptation to turn every possible capability into a feature. The best software, in his view, is often defined by restraint.

That restraint is not minimalism for its own sake. It is a way of protecting the core idea.

“If you keep adding features to prove the product is powerful, you can make it harder to understand why it matters,” Zang said. “The hard part is knowing what to leave out.”

Control matters to Zang for the same reason. A tool that feels powerful but intrusive will not become part of daily work for long. Especially as AI moves closer to personal workflows, files, apps, and communication, users will need to trust that the product helps without making them feel watched or overruled.

Trust, in that sense, is not separate from usefulness. It is part of whether the product lasts.

That is where Zang sees the industry heading after the first rush of AI excitement cools. The market may still celebrate speed, technical ambition, and ambitious demos, but users will become more selective. The fact that a product uses AI will no longer be enough to make it interesting.

The winners, he believes, will be the teams that understand behavior as deeply as they understand models.

“The best AI products will not make people think about AI all day,” Zang said. “They will make people wonder why the old way felt so slow.”

His warning is simple: AI novelty will not protect weak products forever. Once the excitement fades, users will keep the tools that solve a real problem and abandon the ones that only prove the technology works.