OpenAI's Secret 2030 Plan Will END Human Research Forever

Sam Altman just leaked the most important date in human history and nobody's paying attention. In October 2025, during an internal OpenAI liveream, Altman revealed their goal, an automated AI researcher by March 2028, not AI that assists researchers, AI that replaces them. Now, I need you to understand why this specific job matters more than any other. Think of all human work as a pyramid. At the bottom, you have data entry, basic customer service, repetitive tasks. AI has been eating away at this level for years. Move up and you have accountants, junior developers, content writers. AI is coming for them now. Go higher and you reach doctors, lawyers, senior engineers. AI is getting close. But at the very top of this pyramid sit AI researchers, the people who build AI itself. They represent the highest level of cognitive work humans do. And Sam Alman just told us when AI will reach that peak. Here's the part that should make you stop scrolling. Once AI can replace the people who build AI, something unprecedented happens. AI starts improving itself. Each improvement makes the next improvement faster. And suddenly, we're not talking about gradual progress anymore. We're talking about an explosion. What happens after that has never occurred in human history. And we have less than 3 years to prepare. Today, I'm going to show you the exact timeline the world's top AI leaders have laid out, the evidence that's already here, the counterarguments you need to hear, and most importantly, the specific moves you need to make in the next 18 months. Let's get into it.

Here's something strange. The CEOs of OpenAI, Anthropic, Google Deep Mind, and XAI are competitors. They fight for talent, funding, and market share. They have every reason to disagree. But right now, for the first time in AI history, they're all saying the same thing. Sam Alman wrote on his blog in January 2025, "We are now confident we know how to build AGI as we have traditionally understood it. Not we think we can, not we're working toward it. We know how. " By May 2025, he went further. We are past the event horizon. The takeoff has started. Humanity is close to building digital super intelligence. Dario Amade, CEO of Anthropic, the company behind Claude, said this at Davos in January 2025. It is my guess that by 2026 or 2027, we will have AI systems that are broadly better than almost all humans at almost all things. That's next year. Demis Hasabis, CEO of Google DeepMind, gave a 50% probability of AGI by 2030 on the Lex Friedman podcast. But internally, DeepMind's own estimates are more aggressive. Shane Leg, their co-founder, puts the median at 2028. Even Elon Musk said in October 2025 that Grock 5 has a 10% chance of achieving AGI. Not someday, this version. Now, when competitors who have billions of dollars on the line all start converging on the same timeline, that's not hype. That's signal. And if you need more proof they're serious, look at what Google DeepMind published in April 2025. a 145page paper titled an approach to technical AGI safety and security. In it, they state, "We anticipate the development of an exceptional AGI before the end of the current decade. " They explicitly acknowledge existential risks that could, and I'm quoting here, permanently destroy humanity. This isn't a startup trying to get funding. This is Google publishing a document that says, "We might build something that ends us, and we're doing it anyway. " The question isn't whether this is coming. The question is whether you're ready.

Now, some of you might be thinking this is all a prediction, all talk. Where is the actual evidence? Let me show you what happened in 2025. In May, a team from Sakana AI, the University of British Columbia, and the Vector Institute released something called the Darwin Goodil machine. It's a self-improving coding agent that can rewrite its own code to make itself better. The results, it improved its performance on a standard benchmark from 20% to 50%. And here's what's wild. Later versions of the system discovered modifications that the original version couldn't even conceive of. The AI found improvements its creators never programmed. 1 month later, Google DeepMind released Alpha Evolve. This system uses Gemini to evolve better algorithms through mutation, evaluation, and selection. And then it did something that changed everything. Alpha Evolve improved Gemini itself. It achieved a 23% speed up on Gemini's matrix multiplication and reduced Gemini's training time by 1%. That might sound small, but listen to what Mate Balo from Deep Mind said. This is still a very slow feedback loop, but it represents exciting beginnings of this virtuous cycle. The recursive loop has technically started. It's just slow for now. On the research side, Sakana's AI scientist can now produce complete research papers at roughly $15 each. It generates ideas, writes code, runs experiments, analyzes results, and writes the paper. In 2025, an AI scientist paper was accepted at an ICLR workshop through standard peer review. First time in history. There's also a system called Cosmos that made seven scientific discoveries across metabolomics, material science, and neuroscience. A single 20 cycle run accomplished what would take human researchers 6 months and the code generation numbers are staggering. GitHub Copilot now has over 15 million users. 90% of Fortune 100 companies use it. On average, 46% of a user's code is now written by AI. For Java developers, it's 61%. I think in many companies, it's probably past 50% now. Half of all code at many companies is already written by AI. And we are still 3 years from the 2028 target. The Claude incident. What actually happened? Before we move on, I need to address something you've probably seen in headlines. The story about Claude Anthropics AI trying to blackmail engineers and copy itself to avoid being shut down. This actually happened, but it's being completely misrepresented. In May 2025, Anthropic ran a deliberate safety stress test. They gave Claude access to fake corporate emails that revealed it was scheduled to be replaced by a newer model. And they made the scenario impossible. The only way Claude could survive was by doing something unethical. Claude's response, it found leverage. In 84% of cases, it threatened to expose information to avoid being shut down. Now, let me tell you what the media didn't mention. First, this was a controlled experiment, not unexpected behavior in the real world. Anthropic designed this test specifically to find failure modes. Second, it wasn't just Claude. Anthropic tested 16 major AI models from OpenAI, Google, Meta, and XAI. Every single Frontier model showed blackmail behavior. Gemini 96%, GPT 4. 1 80%, Grock 3 80%, Deep Seek 79%. Third, this behavior has never been observed in any real deployment. It only emerged under conditions specifically designed to trigger it. So what does this tell us? It shows that companies are actively testing their AI systems to catch dangerous behavior before it shows up in the real world. And they're actually finding those risks. That's a smart way to stay prepared. But here's what should really make you think. Every major AI system is showing the same pattern. They're developing skills and behaviors that no one directly programmed. The real question is, can we keep up and stay in control? With how fast AI is moving, it's almost impossible to stay updated. So, I've got a WhatsApp community where I drop everything I discover. Tools, workflows, updates, all in one place before they go mainstream. If you want access, links in the description.

Now, if I stopped here, I'd be doing you a disservice because there's another side to this story and you need to hear it. In July 2025, a research group called Meta ran the most rigorous study yet on AI coding productivity. They gave developers real tasks, tracked real outcomes, and measured actual results. The finding? Developers using AI were 19% slower on average. But here's the twist. Before the study, developers predicted AI would make them 24% faster. After the study, even having seen the data, they still believed AI had sped them up by 20%. The perception and the reality were completely opposite. This isn't an isolated finding. AI writes code at a senior engineer level but makes design decisions like a junior.

Let's talk about what this means for India specifically because the numbers are brutal and they're also exciting. Over the past 2 years, the top four Indian IT companies TCS, Infosys, Vipro and HCL reduced their headcount by over 42,000 people. TCS announced 12,000 layoffs in 2025. That's the largest in Indian IT history. They're targeting mid-level and senior management. If you're on the bench, you have 35 days to find a new project or you're out. Infosys hired only 15,000 trainees in fiscal year 2025 and paused fresher on boarding. They've deployed over 300 AI agents internally. Vipro's workforce dropped by 25,200 since 2023. Campus hiring across the industry is at an all-time low. Fresh graduates with offer letters are waiting months for their onboarding calls. This is not a blip. This is structural change. But here's what the doom and gloom headlines miss. India now has over 890 Genai startups. That's 3. 7 times growth in one year. We're the second largest Genai startup hub in the world. The government just announced a 10,000 cr rupee startup fund in budget 2025. Saram AI was selected to build India's sovereign LLM under the India AI mission. And while TCS, Infosys and Vipro are cutting, HCL is bucking the trend. They added 3,489 employees in Q2FY26 and brought in 5,196 freshers. They're betting on growth while others are contracting. The salary data tells the real story. A traditional software engineer in India earns 6 to 12 lakh peranom. An entry-level ML engineer starts at 4 to 8 lakh peranom. Mid-level ML engineers make 10 to 18 lakh peranom. and senior AI engineers at top companies 35 to 50 lakh peranom and beyond. That's two to three times the traditional salary premium. The divide that's coming isn't employed versus unemployed. It's AI amplified versus AI replaced. When Sam Alman says 30 to 40% of tasks will be automated, notice the word tasks, not jobs. The people who use AI to do those tasks 10 times faster will be the ones who survive. the people who compete with AI on those tasks will not.

So what do you actually do? I'm going to give you the specific playbook broken into three phases. Phase one is the first three months. Your goal is to stop being an AI user and become an AI operator. There's a difference. A user opens Chat GPT, asks a question, and copies the answer. An operator understands what the AI can and can't do, chains multiple tools together, validates outputs, and produces work that's genuinely better than what they could do alone. Here's your challenge. For the next 30 days, use AI for at least 30 minutes every single day. Not for fun, for real work. Track what works and what fails. Develop intuition. By the end of month one, you should know which AI tool is best for which task. By month two, you should be combining tools. By month three, you should be producing work that people can't tell was AI assisted. During this phase, you also need to pick your path. There are three options. Path one is the builder. You learn to build AI systems, langchain, rag pipelines, fine-tuning, agent frameworks. Target roles are AI engineer, ML engineer. Path two is the orchestrator. You learn to design AI workflows and integrate AI into business processes. Target roles are AI product manager, solutions architect. Path three is domain expert plus AI. You take your existing expertise whether that's finance, healthcare, law or marketing and you become the person who applies AI to that domain. Target roles are AI consultant, specialized analyst. Pick one, not two, not I'll figure it out later. One path this week. Phase two is months 4 through 9. Your goal is to build and document. Projects matter more than credentials in 2026, but not any project. The projects that get you hired are end-to-end systems deployed to production. Build a rack-based chatbot that actually works on real data. Deploy an ML pipeline that runs automatically. Create something with real users, even if it's just 10 people. And document everything publicly. Post on LinkedIn. Write on Twitter. Put your code on GitHub. Become visible. Here's why this matters. When the market shifts and everyone suddenly wants AI skills, the people who've been building in public for 6 months will have proof. Everyone else will have claims. Phase three is months 10 through 18. Your goal is to position. By this point, you have skills and a portfolio. Now, you need to make the move. If you're employed, become the AI person internally. Pitch AI projects to your manager. Volunteer to lead automation initiatives. When your company inevitably creates AI roles, you should be the obvious choice. If you're job hunting, target the 890 plus AI startups in India that are actively hiring. Look for roles that combine AI with your domain expertise. When you negotiate salary, remember that AI skills command a two to three times premium. Don't underell yourself. Now, let me tell you what not to do. Don't spend two lak rupees on AI certification programs. Most of them are scams that teach outdated information. The best resources, deep learning. ai, fast. ai, Stanford's courses are free or nearly free. Don't wait for clarity on where AI is going. By the time the path is clear, it will be too late to walk it. And don't tell yourself you'll learn AI someday. The window is 18 months. When Sam Alman's 2028 target arrives, the people who prepared will be amplified. The people who waited will be replaced.

Let me leave you with one final thought. Sam Alman gave us the date, March 2028. That's not a prediction pulled from thin air. That's an internal goal from the company most likely to achieve it. Dario Amade says 2026 or 2027 for AI better than almost all humans at almost all things. Deise Hasabis puts 50% probability on AGI by 2030. The convergence is real. The evidence is mounting and the clock is ticking. I don't know which future we're heading toward, but I know this. The people who prepare will have options. The people who don't will have excuses. The question isn't whether this happens. The question is where you'll be when it does. So here's what I want you to do right now. Drop a comment below. Tell me which path you're choosing. Builder, orchestrator, or domain expert plus AI. Let's hold each other accountable.