So What Should You Build With AI?
Do you?
I constantly feel like I’m falling behind with AI but recently I wondered if that’s really true.
If we look closely at the people who appear furthest ahead, the ones spending the most time with the technology… you’ll find something surprising: they have very little idea what to actually do with it.
Five Ways People FAIL to use AI
Using AI to slack off
Picture someone with a 40-hour job that includes ~35 hours of real work and ~5 hours of coffee and Slack.
They’re now using ChatGPT and Claude and are left with 20 hours of real work and 20 hours of browsing Facebook.
This person has owned themselves in three ways simultaneously.
1/ They’ve demonstrated that a third of their job can be automated, which means there’s less of a need for the role.
2/ Other people are using the same tools to do better work, not just less work, so the automator’s unchanged output is actually falling behind relative to peers.
3/ They’re atrophying their brain. The premium on actually thinking, on engaged cognitive work, has gone up, not down. Every hour you outsource to AI without redirecting that time toward harder problems has a cumulative negative effect.
The more extreme version of this are the over-employment crowd, people secretly working four or five jobs in parallel. In a nihilistic world where everyone’s income is going to 0 in 2 years maybe they are nailing it but they are failing to raise their career floor in every other scenario.
At least these people are doing their jobs. The next group has turned AI exploration itself into the product.
LARPing
The AI influencer economy runs on a very specific loop: try the next model, post about it, declare it changes everything, repeat. Every new release, from 4.6 to 5.3 is the one that changes the game. They said it about every single model.
Between model drops, these creators fill time with flashy build projects. “I just built this in 5 hours.” “I just shipped this in 10 hours.” The problem is their entire audience consists of people who also want to build things in 10 hours. Nobody sticks around to use what was built.
What you’re watching is effectively an outsourced marketing arm of OpenAI and Anthropic. These creators are great at getting people to try the tools. But they monetize attention through courses, communities, subscriptions – all focused on telling other people to use AI. None of it is about doing anything with AI.
The mythical person who is quietly running away to build a $100M business is probably not posting about it.
The creators will tell you to “just start playing with the tools and you’ll figure it out.” But they’ve spent all this time playing with the tools, and they haven’t figured it out either.
Lazy solopreneurship
The third failure mode is the person who was always too lazy to build something and now uses AI to skip it entirely. X reply automators (with useless followers), Polymarket bot builders (unprofitable) and ebook generators (with 3 sales) are all in this category.
These people never did the deep work required to succeed at the thing they’re now automating.
If they had actually taken the time to write a book or post intelligently or specialize in some aspect of prediction markets, they might have been successful or at least learned some transferable skills. They hadn’t, so they reached for automation instead.
AI doesn’t fix the underlying deficit. Whatever revenue they generate temporarily will get commoditized fast, because the work they do now doesn’t make future work easier or better. There are no durable loops here.
Just vibe coding
The over-eager non-technical builder sees AI coding tools and thinks: finally, I can build an app. So they build a fitness tracker, a to-do list, a goal-setting app or a content generation tool.
Typically things they themselves would use, which makes them intellectually honest but constrained by a narrow view of what useful software looks like.
The fundamental mistake is believing that coding was the bottleneck. For most apps, the actual challenge is the growth loop. By removing the ability to produce the software, you haven’t removed the distribution challenge. You’ve just built a working product that nobody knows about.
“Gen AI” investments
Then there is the enterprise executive who read a McKinsey report on AI and decided to fully automate their call center. They signed a hard-to-reverse investment, hired a third-party contractor who promised to handle it, removed 500 people, and deployed AI agents.
Profit?
Nope.
All five groups share the SAME mistakes
What these five groups have in common is that they are all risk-averse and short-term oriented.
Which sounds counterintuitive, because some of these moves look bold. But automating your job, producing demo content, generating e-books, vibe-coding apps, and ripping out call centers are all attempts to capture value right now with minimal uncertainty. They’re low-risk, low-time-horizon plays.
Content creators will falsely tell you they have a view of the future: “you should be trying these tools out.” But they don’t have a thesis as to why. Their actual promise is that you’ll spend time with the tools and eventually figure out what to do. They haven’t figured it out either though, and they’ve spent way more time than you will.
This should be encouraging.
AI is not running away from you as fast as it seems. It’s a capability that is genuinely complicated to navigate, and it requires dimensions beyond technical proficiency to apply well.
Take RISKS, play LONG TERM GAMES
Spend a couple of weeks familiarizing yourself with the latest generation of tools. But don’t make it 50% of your week. You can rely on the content creators to surface what’s new and then intelligently switch over when a new great tool appears.
Once you have a sense of what’s possible, pick a narrow vertical and bet on it. Don’t start a YouTube channel of AI-generated Muppet sketches. Instead, take an almost science-fiction level North Star for where your vertical ends up in two years.
Assume models will be dramatically better. Then spend the next two years thinking about what’s possible in that world, building the components that are possible now.
You will spend more time in that future than anyone else. You’re learning things nobody else is learning. And that compounds into genuine domain advantage, the kind that can’t be replicated by someone who just picked up the tools last week.
There are better ideas out there
The lesson here is that you should use personal conviction but if you don’t have it, here are some business models I’m intrigued by.
Distribution-first companies
If the ability to respond to feature requests is now extremely fast, then the feature matrix is no longer an advantage. Being two years early to understanding that a customer needs a feature doesn’t matter if anyone can build it in a day. The real domain of competition becomes marketing and distribution. Companies that use the excitement around AI itself as a distribution mechanism, and maintain enough taste and speed to respond to customer needs, are well-positioned.
BridgeMind is the perfect example here: the content value of the journey is more valuable than the direct revenue from the apps.
Services
80% of the US economy is services. The things that make service companies great: business development, high-trust relationships, the “we’ll do anything for you” approach, fundamentally weren’t scalable. But now they are. And many service-oriented companies aren’t technical enough to exploit it.
So what does the service become? Do you automate the back-end and maintain the trust with the same number of people, doing 10x more per client? Or do you automate the whole thing and turn it into a product, competing with SaaS by having a broader, more fluid feature scope where clients can request modifications to the software itself?
Services aren’t glamorous, which is exactly why this category is exciting for the right teams.
Data-media hybrids
You can’t automate journalism or opinion writing. And pure data businesses, the ones that are essentially a bunch of SQL queries and scripts, are now easier to replicate than ever.
These two categories are the opposite of a media spectrum and are going to blend in ways they haven’t before.
One of the first verticals where AI can write genuinely useful content is where it can procure deterministic data that lives entirely online. A fully automated crypto data newsletter, for instance, is far more doable than most people realize.
You would compete with data companies by doing what they do more cheaply, incorporating data sources that are hard for humans to mine, and then leveraging the data to create media-driven distribution.
Honest exploration as a business model
A subtle counter-example to my thesis is Every. They are a good example of how to build an AI-native business while taking very little risk.
On the surface, they look like AI creators: exploring tools, telling you to use them. But they’re intellectually honest about not knowing what the tools are for yet, and they actually build and launch their experiments. They do enterprise coaching, instead of $100 courses.
Build stuff anyways
Notice that none of these categories are focused on what can be done cheaper with AI.
So what do you do if you don’t want to build a company?
One of the best things I ever did at a job was create a product. The less you think of your role as receiving inputs and delivering outputs, and the more you think of it as being an internal CEO of a new project, the more successful you’ll be.
Favor creative, value-driven projects where the upside is building something new and nobody else is looking (yet).