The position that's hard to see and hard to copy is the general store.
Specific product. Specific problem. Specific person who needs it solved every month. No funding. No permission. No dev team.
You don't need to build the next foundation model.
You need to be the person who shows a plumber how to never miss a booking again and gets paid monthly while it runs without you.
That's the shovel. That's the ladder. That's the window.
You need to be the person who shows a plumber how to never miss a booking again and gets paid monthly while it runs without you.
That's the shovel. That's the ladder. That's the window.
SF-001 just dropped. Full thesis here:
Same pattern is playing out right now.
Some people are a step ahead, building audiences and turning AI into income. That is real money.
It is also rented land.
The smaller group, the one that's easy to miss, is building on the tool layer itself.
@KanikaBK This seems kind of asinine. I don't think I truly agree with what's being said here because it's saying that the user doesn't understand the output that it's getting. You're still reviewing and proofing and iterating to make sure that it is an accurate statement.
๐จBREAKING: The workers losing their jobs to AI are not the ones who use AI.
Stanford's 2026 AI Index confirms it. Unemployment is rising faster among workers least exposed to AI than workers most exposed.
The threat was never having a job AI can do. It is having a job AI cannot reach.
For two years the entire conversation has been built on one assumption. AI takes the high-exposure jobs first. The lawyers. The analysts. The programmers. Every layoff headline reinforced the same story. Stanford just inverted it.
The Stanford Institute for Human-Centered AI tracked unemployment changes across occupations sorted by AI exposure. They expected to find workers most exposed to AI losing jobs faster than workers least exposed. They found the opposite. Workers in low-exposure occupations are losing jobs faster than workers in high-exposure ones.
The mechanism makes sense once you see it. Companies are not firing AI-exposed workers and replacing them with AI. They are using AI to make those workers more productive. Then they cut elsewhere. The cuts come from departments where AI is not yet a tool. The AI-exposed workers are not the casualties. They are the leverage that justifies cuts everywhere else.
The numbers make this concrete. Software developers aged 22 to 25 saw employment fall nearly 20% from 2022 to 2025. But experienced developers in the same field grew their headcount. The cuts within tech are concentrated in entry level. The cuts outside tech are concentrated in jobs where AI never arrived.
The pattern is consistent across every dataset Stanford pulled from. The marketing team using AI gets to keep its 12 people. The procurement team that is not using AI loses 4 of its 10. The accounting team using AI gets to keep its workflow. The facilities team that is not using AI absorbs the layoff. The savings have to come from somewhere. Stanford documented exactly where.
McKinsey's 2025 executive survey confirmed where the next round is heading. A third of organizations expect AI to shrink their workforce in the next year. The cuts they named were not in the high-exposure categories. They were in service operations and supply chain. Departments that have to absorb the cost savings the AI-exposed teams are generating elsewhere.
The detail most coverage missed: productivity gains from AI are not appearing in tasks requiring more judgment. The 14% gain in customer service and 26% gain in software development are real. In tasks requiring legal reasoning, financial judgment, or strategic decision-making, productivity gains are weak or negative. The work AI is actually doing is concentrated in narrow task categories. The work AI is supposed to do, that justifies the layoffs, is not showing measurable productivity at all.
If you spent the last two years assuming your job was safe because AI could not do it, the Stanford 2026 AI Index just confirmed your job is exactly where the cuts are landing.
Source: Stanford Institute for Human-Centered AI, 2026 AI Index Report
PDF: https://t.co/O5zXh2oXsQ
ClickFunnels hustlers. Teachable creators. Shopify dropshippers. Crypto builders on Telegram.
They weren't the smartest. They just recognized the infrastructure layer before everyone else and moved. By the time the crowd caught up, the window was closed.
In the gym this morning, just thinking about how behind I feel as I take action and execute on the things that I've been trying to execute on for years but now with AI it's made it even more possible for me than ever.
The gap between "I have an idea" and "I shipped it" is not talent. It is not money. It is the willingness to sit with a bad first version long enough to make it a good one. Most people bail before the ugly stage ends.
In the Gold Rush, the miners got famous. The people selling picks, shovels, denim, and provisions got rich. Consistently. Regardless of whether anyone struck gold. That pattern is playing out again right now with AI. Pay attention to who is building the general store.