AI Is Just Infrastructure

I've spent the last two years building with AI tools: Cursor, Claude, Replit, Windsurf, and more. The tooling changes every few months. Models get cheaper. New frameworks appear and disappear.

But one thing keeps getting clearer to me: AI is not a product. It's infrastructure.

We keep talking about AI like it's a chatbot, a SaaS feature, or a clever tool. That framing misses what's actually happening. AI is becoming more like electricity. More like the internet.

Power goes in, intelligence comes out.

This framing changed how I think about what to work on.

The Stack

You can think about AI as a simple stack:

• Energy
• Chips
• Infrastructure
• Models
• Applications

Each layer enables the next. Energy powers everything. Chips turn electricity into computation. Infrastructure connects thousands of chips into massive AI factories. Models sit on top of that.

And finally, we get applications or in pop jargon, wrappers, where the value actually gets created.

I wrote in my 2024 life lessons that "if you're building a startup today, your real moats are in the Three D's: Data, Distribution, and Design."

That's still true. But now I'd add a fourth: understanding where you sit in this stack.

Most Builders Are Fighting the Wrong Battle

When AI exploded, many founders immediately started trying to build models, AI infrastructure, or their own frameworks. I was guilty of this too. I tried building Pynions, an open-source AI agent framework in Python.

It was a fun learning experience, but I quickly realized I was competing with companies investing tens or hundreds of billions into the lower layers.

OpenAI, Google, Anthropic, NVIDIA, you're not going to outbuild them and you don't need to.

Electricity created power plants, but the real economic explosion came from appliances, factories, machines, tools that people actually used. AI may follow the same pattern.

The cheaper your company is to operate, the harder it is to kill. Paul Graham, Co-founder of Y Combinator

The opportunity isn't in the infrastructure. It's in what you build on top of it.

Intelligence Is Getting Cheaper

One thing is becoming very clear: the cost of intelligence is falling fast.

The cost of frontier-model intelligence has dropped ~10–20x in roughly 3 years, while budget models now offer basic AI capabilities at 200x less than GPT-4's launch price.

Models improve. Reasoning improves. Open models spread everywhere. I've watched the price of API calls drop dramatically just in the past year. What cost me hundreds of dollars in 2024 now costs a fraction.

This means the model itself won't be your moat. If you're building a business that depends on having access to a better model than everyone else, that advantage has a short shelf life.

Your advantage has to come from somewhere else.

Distribution Still Wins

I keep coming back to this. AI models are quickly becoming interchangeable. Your distribution is not.

The companies that win usually control attention.

Search traffic, newsletters, communities, platforms. Ross Simmonds hammered this in Create Once, Distribute Forever, and it's more true now than when I read it.

At Rewardful, I see this every day. The AI and SaaS companies growing fastest aren't the ones with the best models.

They're the ones with the best distribution:

• Affiliate programs
• Content engines
• Partnerships

If you control distribution, you can plug in better intelligence every year or even every month. Without it, even great products struggle.

Context Is the Real Moat

AI becomes dramatically more powerful when it has context. Not generic internet knowledge, real context. Projects, goals, people, history, preferences.

This is why I'm excited about personal AI assistants and why tools like Claude Code feel so powerful. Claude doesn't just generate code. It understands my project, my files, my patterns. That context makes it 10x more useful than a generic chatbot.

The real advantage isn't the model. It's the memory. Context compounds over time, and whoever accumulates the most relevant context wins.

Ninety percent of success can be boiled down to consistently doing the obvious thing for an uncommonly long period of time without convincing yourself that you're smarter than you are. Shane Parrish

Context works the same way. Feed it consistently, and it compounds.

Workflow Over Intelligence

The best AI products don't sell intelligence. They sell outcomes.

Cursor sells faster coding. Perplexity sells better answers. Legal AI tools sell faster research. Users rarely care about which model is running under the hood. They care about the result.

I learned this the hard way with my own projects. When I was vibe coding and building tools like Bordful and Draftpen, the breakthroughs never came from switching to a better model.

They came from designing a better workflow, clearer prompts, structured files, smarter iteration loops.

Design the workflow first. Add AI second.

AI-Native Companies

Something bigger is starting to happen. We're seeing the emergence of AI-native companies, businesses designed from the beginning to run with automation, agents, and minimal staff.

In the past, scaling meant hiring more people. More writers, more analysts, more operators. Now a small team, sometimes even a single founder, can run what used to require an entire company. AI research, content production, distribution, analytics monitoring.

I wrote about this shift in my 2025 life lessons: "You must understand 100% of your business to get the full advantage and be able to move fast. If you can control it from bottom to top, you can't be stopped."

That's the promise of AI-native. Full-stack understanding, full-stack leverage.

General ambition will give you anxiety. Specific ambition will give you direction. Anu

Where I'm Focused

Looking at the stack, the strategic choice becomes obvious for me.

I'm not interested in building models. I'm not interested in running GPU clusters. I'm interested in applications, turning intelligence into outcomes.

At Craftled, that means systems that combine AI reasoning, structured workflows, and real distribution. Media systems, automation tools, agents that monitor operations and surface opportunities.

The models will keep improving. That's not my job.

My job is designing the systems around them, and making sure I have the distribution to reach people who need them.

We're Still Early

Despite all the hype, we're still early. Hundreds of billions have already been invested in AI infrastructure, and trillions will likely follow. New chip factories, new data centers, new models.

But the application layer is still wide open. Most industries have barely started integrating AI into real workflows.

Paul Copplestone at Supabase shared data showing a hockey-stick growth in weekly sign-ups driven by AI coding tools. Jan Curn at Apify reported similar organic traffic explosions.

The market for builders is expanding fast. The opportunity isn't building the next model. It's building companies that run on intelligence by default, not as a feature, but as the operating system.

You waste years by not being able to waste hours. Amos Tversky

Start building. The infrastructure is ready. The leverage is there. Now it's about what you do with it.