A lot of AI startups still look like this:
Take a large language model. Wrap it in a clean UI. Add “copilot” somewhere in the homepage copy. Raise money. Hope distribution magically appears.
That strategy worked for a brief window because the technology itself was novel. But novelty decays fast. By 2026, “uses AI” means almost nothing. It’s like saying your startup “uses the internet.”
The harder question now is this:
What kind of AI business still has room to matter once the foundation models become cheap, fast, and everywhere?
If I were starting from scratch today, I wouldn’t chase the flashiest demos. I’d look for friction. Boring, expensive, repetitive friction hiding inside industries that still run on PDFs, spreadsheets, and human memory.
Many of the best opportunities still come from solving boring, specific problems, which is why Why Solving Boring Problems Can Be a Competitive Advantage.
That’s where AI becomes economically dangerous.
The First Mistake I’d Avoid: Building a Wrapper With No Leverage
There’s a reason so many AI products feel interchangeable.
Most are thin layers on top of the same underlying models.
That creates three problems immediately:
- low differentiation
- weak margins
- fragile defensibility
If your entire product advantage disappears when another company changes its API pricing, you don’t really own much.
This doesn’t mean wrappers are worthless. Sometimes distribution alone wins markets. But beginners underestimate how quickly infrastructure advantages disappear in AI.
The real value usually accumulates somewhere else:
- proprietary workflows
- customer relationships
- internal data
- operational integration
- trust
- distribution
Not the chatbot itself.
I’d Start With a Workflow, Not a Model
Most successful AI companies in the next few years probably won’t feel like “AI companies.”
They’ll feel like:
- accounting software
- logistics platforms
- recruiting systems
- legal operations tools
- healthcare admin products
AI is becoming infrastructure.
The opportunity is embedding intelligence into painful workflows people already pay to solve.
A Better Question Than “What Can AI Do?”
Instead of asking:
“What can AI generate?”
I’d ask:
“Where are humans spending time on repetitive cognitive labor?”
That shift changes the kinds of businesses you notice.
Interesting targets include:
- insurance claims processing
- compliance documentation
- procurement workflows
- sales qualification
- customer support escalation
- contract review
- internal knowledge retrieval
Not glamorous. Very valuable.
The Best AI Markets Are Usually Operationally Ugly
This is one of the least appreciated dynamics in AI right now.
Many founders chase creative AI because it looks impressive in demos:
- image generators
- video tools
- AI companions
- presentation makers
But operational software often has stronger economics because the ROI is measurable.
If AI saves a company:
- 20 hours per employee
- compliance penalties
- hiring costs
- support headcount
- processing delays
then buyers care less about hype and more about outcomes.
That’s a healthier business.
Ironically, some of the best AI companies in 2026 may have the least exciting demos.
I Would Obsess Over Distribution Earlier Than Most Founders
A dangerous misconception in AI startups is believing superior models automatically win.
They don’t.
Distribution wins constantly.
The graveyard of tech history is full of technically brilliant products nobody adopted.
The Harsh Reality About AI Products
Most AI tools are easier to copy than founders want to admit.
What’s harder to copy:
- audience trust
- industry expertise
- embedded workflows
- partnerships
- communities
- existing customer pipelines
If I were starting today, I’d probably choose one of these approaches:
1. Audience-First
Build an audience before the product.
Examples:
- niche newsletters
- YouTube channels
- operator communities
- educational content
- developer tooling ecosystems
This lowers distribution risk dramatically.
A surprising number of AI startups are really media companies wearing software clothing.
2. Vertical SaaS With AI Embedded
Pick one industry. Go painfully deep. Own the workflow.
Horizontal AI tools are crowded. Vertical tools still have room because domain expertise matters more than people expected.
An AI tool for dentists, freight brokers, or construction estimators may outperform generic assistants simply because it understands context better.
The barrier to building AI products has collapsed, but usefulness remains scarce. That's the central theme of Building an AI SaaS Product Is Easier Than Ever. Building a Useful One Is Still Hard..
3. AI Infrastructure Picks-and-Shovels
Everyone wants to build the glamorous application layer.
But infrastructure businesses often survive longer:
- evaluation tools
- observability
- security
- AI governance
- data pipelines
- synthetic data generation
- inference optimization
Not sexy. Historically profitable.
I Would Be Skeptical of “Fully Autonomous” AI Products
This is where I’d probably sound more conservative than the average AI founder.
The market currently rewards autonomy theater.
Every startup pitch claims:
- autonomous agents
- self-operating workflows
- human replacement
- end-to-end automation
Some of this is real. A lot isn’t.
What Actually Breaks in Practice
Autonomous systems fail in surprisingly ordinary ways:
- edge cases
- unclear instructions
- missing context
- bad integrations
- changing environments
- human unpredictability
The more expensive the mistake, the less companies want full autonomy.
That’s why many successful enterprise AI systems today are “human-in-the-loop” rather than fully automated.
And honestly, that may remain true longer than the hype cycle expects.
Zooming out, this is one reason I believe AI Will Create More Small Businesses Than Unicorns.
The Real Moat Might Be Data Exhaust
One thing many beginners misunderstand about AI businesses:
The product often improves after customers start using it.
Every interaction generates operational data:
- user behavior
- workflow patterns
- edge cases
- corrections
- approvals
- failures
Over time, this creates proprietary insight competitors don’t have.
That’s potentially powerful.
Why This Matters
The long-term winners in AI may not be the companies with the smartest models.
They may be the companies sitting on the most useful workflow data.
This is already happening quietly across industries.
And it explains why incumbents are simultaneously more vulnerable and more advantaged than people think.
I’d Keep the Team Smaller Than Previous Startup Eras
AI changes company structure.
A strong technical founder with AI tooling can now:
- prototype faster
- automate support
- generate content
- accelerate research
- write internal tooling
- reduce operational overhead
This doesn’t eliminate the need for talent. It changes leverage.
Small teams can suddenly compete in markets that previously required much larger organizations.
That shift is easy to underestimate.
But There’s a Catch
AI also lowers barriers for competitors.
So speed matters more. Iteration matters more. Distribution matters more.
The market gets noisier as creation becomes cheaper.
What I Wouldn’t Build
There are a few categories I’d personally avoid unless I had an unusually strong angle.
Generic AI Writing Tools
This market became crowded astonishingly fast.
Most products differ cosmetically while relying on similar underlying capabilities.
Without unique distribution or workflow integration, it’s difficult to sustain differentiation.
“AI for Everyone” Platforms
Broad positioning sounds appealing but often creates weak product focus.
The strongest startups usually solve painful problems for very specific users first.
Narrow markets compound.
Vague markets drift.
Products That Depend Entirely on One Model Provider
This is an underrated strategic risk.
If your margins, reliability, and feature roadmap depend entirely on another company’s API decisions, you inherit their volatility.
Abstraction layers help. Proprietary workflows help more.
The Most Important Question Isn’t Technical
By 2026, the technical barrier to building AI products will keep dropping.
That changes the game.
The advantage shifts toward:
- problem selection
- customer understanding
- operational execution
- trust
- distribution
- speed of iteration
In other words, the hard part increasingly becomes business itself.
Not AI.
That’s probably the biggest misconception in the industry right now. People still talk as if better models automatically create better companies.
Usually, they just create faster-moving competition.
And the founders who survive that environment probably won’t be the ones shouting “AI” the loudest.
They’ll be the ones quietly solving expensive problems people desperately want removed from their workdays.
