The next AI revolution may not be bigger models, but persistence, systems with memory, recursive reasoning, and long-horizon continuity that think continuously.
https://t.co/NNHMTHBbfT
I used Claude, ChatGPT, Stitch, Replit, and customer feedback loops to build an enterprise-grade SaaS platform in weeks instead of quarters. Here’s the full breakdown.
https://t.co/aASr1ivbNA
Executives often expect certainty from inherently uncertain problems. Product leaders now balance AI, strategy, engineering tradeoffs, and customer pressure in real time. “It depends” is usually the honest answer.
https://t.co/2r2RorBati
Agent-based AI is driving real productivity gains. I see it firsthand. But automating coding doesn’t mean all knowledge work follows. Execution scales. Judgment, context, and trust don’t.
https://t.co/COSCdG94n3
“Human-in-the-loop” sounds safe. But when AI does the thinking and humans just approve, is it really oversight?
There's an illusion of accountability in AI systems, we need to design for real judgment, not confirmation.
https://t.co/k2VZim6oB1
AI may not trigger mass unemployment overnight, but the signals are clear. Even incremental disruption is enough to rethink UBI, Social Security, and how we fund basic living costs over the next 15 years.
https://t.co/NiwLM7NFt6
Scientific progress gave us the confidence to say “I don’t know.” This article shows why AI needs that. Training LLMs is the easy part, the real challenge is teams willing to build systems that admit uncertainty. That’s where trust starts.
https://t.co/dsxSpbbxrj
Most teams use AI to move faster. The real advantage is using it to think better. I use LLMs to simulate debate, challenge assumptions, and pressure-test decisions before execution.
A simple framework for better decisions under ambiguity.
https://t.co/ux4LTFrccO
Another Ezra podcast, TLDR: AI = power concentration, labor disruption will be uneven, govt isn’t ready, job loss isn’t just economic, it’s identity. This isn’t just tech. It’s a system level shift in power and purpose.
https://t.co/0DKdKn8CSE
Reasoning is now an API call. We’ve removed the constraint on generating answers and created a new one: judging them. Market fit isn’t the model. It’s the system that turns outputs into decisions. In a world where thinking is cheap, judgment is everything
https://t.co/wRPyWJxlEO
Busy 16 months. Updated my portfolio with work across AI, data platforms, and product leadership. If you’re tackling product strategy, scaling AI, or tying roadmaps to outcomes, take a look. Open to VP/CPO roles and select advisory work. DM me.
https://t.co/vZkmrBhufd
Most AI efforts fail for one reason: bad systems. Legacy workflows aren’t built for agents. Teams optimize without understanding economics. Competitors redesign while others pilot. AI doesn’t fix inefficiency—it exposes it!
https://t.co/VHa5yTxV33
Sad to see Sora go. My family had many LOL moments with it over the holidays. With my product hat on thought, it's a clear example of weak market fit and shaky economics. Novelty is not enough, if it doesn’t drive sustained value, it doesn’t survive.
https://t.co/Me6kMyifhZ
Software dev isn’t just accelerating, it’s being redefined. SDLC was built for humans, not machines. At scale, the constraint is no longer code, it’s control. We’re shifting from writing software to governing it.
https://t.co/Dpkfn9Xb84
Companies are laying off workers because of AI. But in many cases, the automation hasn’t actually arrived yet. After 16 months building AI platforms, one thing is clear: AI improves tasks, not entire jobs. The real transformation is workflow redesign.
https://t.co/hwASwWqci6
AI doesn’t reduce work, it intensifies it. Product teams can prototype faster than ever, but speed has side effects: ambient work, cognitive overload, and hidden review burdens. The real challenge for leaders is managing the speed of progress.
https://t.co/E3Ryy7CZOZ
We keep calling it “autonomy,” but so much of it is structured human labor behind the curtain. When he hide what really makes it work, we tend to overestimate machine intelligence and underestimate the system actually doing the work. So familiar.
https://t.co/CNJnrjhb5T
Over 14 months I’ve helped startups and Fortune 500 teams rethink AI delivery. The shift isn’t that AI can code, it’s that it can ship MVPs fast. I break down Replit, Cursor, Claude, and Antigravity and how I’d operationalize them as a CPTO in 2026.
https://t.co/fbUq5Su459
AI coding is powerful but overhyped. Perceived speed gains often vanish under measurement. Great for narrow tasks, weak in complex systems, and can add technical debt. Use it with discipline and intent.
https://t.co/hH0KKrfxUA
Indecision is the most expensive bug in the system.
Engineers want clarity, not control. Over-planning beats re-coding. Mistakes are fine. Denial isn’t.
If engineering is confused, ladership failed upstream.
https://t.co/7rQllNrKXP