koba (@kobaHUB)

Jack Ma told Elon Musk humans will always be smarter than machines. Musk’s reaction says everything. “Computers may be very clever, but human beings are much smarter. We invented the computer. I've never seen a computer invent a human being.” Ma's argument was simple. Computers are tools. They calculate faster, remember more, but humans still hold something machines don't: experience, judgment, wisdom. Musk clearly didn't buy it. “What would be an example of something that humans are better than a computer at?” Watching this exchange now feels completely different than it did then. AI agents aren't just calculating anymore. They're writing code, analyzing documents, planning tasks, deciding what to do next on their own. METR found that the length of software tasks frontier agents can complete autonomously has historically doubled roughly every seven months, with signs of even faster progress through parts of 2024. That's where Ma's argument starts to buckle. For decades, technology automated what humans did. AI is starting to automate what we assumed required a human to think. And companies were already learning to create enormous value with almost no people before this wave even arrived. Instagram sold for $1 billion with 13 employees. WhatsApp sold for $19 billion with 55. Now picture what happens when a small team can direct hundreds of machines that never sleep, never take a salary, never need an office. The question was never just which jobs disappear. It's what happens to the money once fewer humans are needed to create it in the first place. That's the question behind 2028: The Economy Without Humans. Who gets the money when machines do the work?

You can be right 70% of the time - and still go broke. A strategy that wins 7 out of 10 trades sounds unbeatable. But say the winner pays +1 and the loser costs −3. Expected value: 0.70×1 − 0.30×3 = −0.20. A system that's right most of the time is bleeding money on every trade. Now flip it. A strategy that wins only 4 out of 10 - but the winner pays +2 and the loser costs just −1. Expected value: +0.20. It loses more often. It's also the only one making money. The market doesn't pay you for guessing direction correctly. It pays you for the full payoff - how much you make when you're right, how much you lose when you're wrong. It gets worse. Even with a genuine edge, six losses in a row isn't a red flag - at a 40% loss rate, it's a routine occurrence. And a 50% drawdown doesn't need a 50% recovery to break even. It needs your capital to double. So the real question isn't "how do I win more often." It's "what survives being wrong, repeatedly, in the wrong order." That's not confidence. That's math.

My AI wrote perfect code on the first try. Then it destroyed the entire project over the next two hours. That's what working with Claude Code looked like for me: fix one bug, break two more. Patch the UI, watch the layout fall apart. A hundred hotfixes later, my codebase was a dead Frankenstein of duct-taped patches - and the feature still didn't work. Turns out the model isn't "dumb." It's an intern who starts typing before you've finished the sentence. The fix was almost embarrassingly simple: ban the AI from touching code until there's an approved plan. First - clarifying questions. Then - 2-3 architecture options with trade-offs. A design doc. Only after that - 2-5 minute micro-tasks, tests before implementation, root-cause debugging instead of guessing. The moment I forced it to think before typing, the chaos ended that same day. The lesson: AI isn't bad at coding. It's bad at solving giant problems in one shot. Give it structure, and you get an architect - not an intern running on caffeine and vibes.

Everyone's still prompting ChatGPT like it's 2023. Most people are still stuck in the old way of using AI: write a prompt, check the result, fix it, write another one. Repeat all day, every day. Every small mistake compounds, and you end up being the bottleneck in your own workflow instead of the one reviewing finished work. Some people don't do this anymore. They set a goal once - and let the AI run the entire process by itself. It plans the next step, executes it, honestly scores its own output against clear criteria, and fixes whatever's still weak. No manual back-and-forth required. This is called an AI Loop. Not a longer prompt — an actual cycle: plan → do → verify → decide. It only stops once the result genuinely meets the bar, or it hits a set limit. Recorded what this looks like in action Prompt is in the article.

Prompt engineering is already dead. You just haven't gotten the memo yet. Two years ago, "talking" to ChatGPT was a superpower. Today it's a manual switch on an automated factory. Welcome to Software 3.0 - where developers stop being typists and become managers of autonomous agents. The shift: you write a spec, not code. The agent builds, tests, reads its own errors, and rewrites itself until tests pass. You just hit Deploy. What makes this actually work isn't magic words - it's engineering: agentic transactions that roll back failures instead of crashing, durable memory that kills stale context before it causes hallucinations, and dynamic routing that sends easy tasks to cheap models and hard ones to deep reasoning loops. The programmers who survive this aren't the best "prompters." They're the ones who design the guardrails. Are you building the scaffolding - or still tweaking prompts?

This knowledge costs him $13,000/year. You're getting it free. Most people use Claude as a chatbot. The top 1% build agent systems with it. Here are the 7 types - and what each one actually does: 1. Basic Agent Connects to your calendar, Gmail, contacts. Autonomously plans and executes tasks. Handles the boring stuff so you don't have to. 2. MCP Agent Plugs into Notion, Atlassian, any database. Becomes the central hub of your entire data structure. This is where Claude stops being a tool and starts being infrastructure. 3. Sequential Agents Works like an assembly line. Agent 1 reads your contacts → passes to Agent 2 → Agent 2 sends the email. Clean separation of tasks. Zero manual handoffs. 4. Parallel Agents Multiple agents running simultaneously on different tasks. Results get merged at the end. Same work. Fraction of the time. 5. Router Agents Acts as an intelligent traffic controller. Reads the input → decides which workflow handles it. Adaptive. Flexible. Scales with complexity. 6. Human-in-the-Loop AI does the work. You approve before execution. Non-negotiable for high-stakes tasks where errors aren't an option. 7. Dynamic Subagents The most powerful type. A master agent that spawns specialized subagents on demand. This is how you handle complexity at scale. Master all 7 and you can build systems that solve real problems in 2026.