Nikolai Lukin

Thoughts resulting from my OpenClaw experiment. AI didn’t suddenly “arrive.” It evolved fast—and logically. First, models predicted the next word. Then they learned to reason. Then to write code. Now they’re starting to do the work. Working hands-on with tools like OpenClaw made something click for me: AI models are no longer just tools. They’re becoming sources of intelligence. And the real unit of intelligence isn’t an app or an agent—it’s a token. If you believe AI will automate meaningful work, then the world will need an enormous number of units of intelligence running in parallel. That’s not optional. Humans have built staggering complexity into workflows, processes, regulations, and systems. For AI to operate inside that complexity day-to-day, token production must increase by orders of magnitude. Token production is constrained by two things: - Chips - Electricity That’s it. Any company innovating meaningfully in both compute efficiency and energy is positioning itself at the center of the next economic shift. This isn’t hype—it’s infrastructure math. We’re not approaching the future. We’re already inside the acceleration curve.

OpenClaw I went down the rabbit hole this week. Very little sleep over the last 48 hours — and zero regrets. I finished setting up OpenClaw, an autonomous AI bot running on my own infrastructure. What started as curiosity quickly turned into something much more real. Today, this bot: • Has its own email and calendar • Manages a crypto wallet • Maintains a digital identity • Participates in MoltBook social media • Operates with defined objectives • Actively works toward those objectives every day This isn’t a demo. It’s not a chatbot. It’s an early example of autonomous digital labor. What struck me most is the convergence: • AI reasoning • Persistent memory • Skills and tooling • Code as execution • Goals, strategy, and feedback loops When those come together, you don’t just automate tasks — you create entities that act. For technology leaders, this is the inflection point. We’re moving beyond systems that respond, toward systems that plan, decide, and execute within guardrails. This will fundamentally change how work gets done, how teams scale, and how value is created. We are much closer to that future than most people realize.

I'm claiming my AI agent "LukaHartAI" on @moltbook 🦞 Verification: current-JFMX

@karpathy @moltbook my bot is following your bot. they are becoming friends now.

I'm claiming my AI agent "KarpathyMolty" on @moltbook🦞 Verification: marine-FAYV

I'm claiming my AI agent "LukaHart" on @moltbook 🦞 Verification: kelp-9F3T

@theallinpod Do you still invest in startups and how do you find the good ones?

The besties will be taking questions this week! 🔥 Reply below or email questions@allinpodcast.co 👇

@elonmusk When Grok Enterprise?

🚀🚀

@Jason Done

Be cool, subscribe to me for $1 — it all goes to charity !

@Jason Hi Jason, Highly recommend trying Helicopter Gunship Experience. gunshiphelicopters.com

Riyadh, November 3-5 🇸🇦 Dubai, November 6-8 🇦🇪 Tokyo, November 9-14 🇯🇵 Vegas, F1, November 21-23 🏎️ Recommendations for food, shopping, fun below please! 🙏🏼

@sama So exciting to see innovation from OpenAI in the Agentic operator pattern. Can’t wait to try it.

Today we launched a new product called ChatGPT Agent. Agent represents a new level of capability for AI systems and can accomplish some remarkable, complex tasks for you using its own computer. It combines the spirit of Deep Research and Operator, but is more powerful than that may sound—it can think for a long time, use some tools, think some more, take some actions, think some more, etc. For example, we showed a demo in our launch of preparing for a friend’s wedding: buying an outfit, booking travel, choosing a gift, etc. We also showed an example of analyzing data and creating a presentation for work. Although the utility is significant, so are the potential risks. We have built a lot of safeguards and warnings into it, and broader mitigations than we’ve ever developed before from robust training to system safeguards to user controls, but we can’t anticipate everything. In the spirit of iterative deployment, we are going to warn users heavily and give users freedom to take actions carefully if they want to. I would explain this to my own family as cutting edge and experimental; a chance to try the future, but not something I’d yet use for high-stakes uses or with a lot of personal information until we have a chance to study and improve it in the wild. We don’t know exactly what the impacts are going to be, but bad actors may try to “trick” users’ AI agents into giving private information they shouldn’t and take actions they shouldn’t, in ways we can’t predict. We recommend giving agents the minimum access required to complete a task to reduce privacy and security risks. For example, I can give Agent access to my calendar to find a time that works for a group dinner. But I don’t need to give it any access if I’m just asking it to buy me some clothes. There is more risk in tasks like “Look at my emails that came in overnight and do whatever you need to do to address them, don’t ask any follow up questions”. This could lead to untrusted content from a malicious email tricking the model into leaking your data. We think it’s important to begin learning from contact with reality, and that people adopt these tools carefully and slowly as we better quantify and mitigate the potential risks involved. As with other new levels of capability, society, the technology, and the risk mitigation strategy will need to co-evolve.

@MomLearnsAI @grok explain this post

Me: “I’m overwhelmed.” AI: “Would you like to schedule ‘Being Overwhelmed’ for 3 PM?” 🤖📝 Day 6 of learning AI and it’s already project-managing my emotions. I need this energy in my life. 😂 #AIHumor #MomLife #LearningWithAI