The Cost of Reasoning
Every technological revolution makes a once-scarce resource abundant. Steam reduced the cost of muscle, computers the cost of calculation, and the internet the cost of communication. AI may be doing the same for practical reasoning: as the computational cost of machine reasoning falls, the organizational cost of human reasoning may fall with it.
How to Get Rich with AI (Fast)
Search for AI business ideas and you’ll drown in the same recycled suggestions everyone else gets. That’s not where fortunes are made. The real opportunity isn’t finding another application for AI. It’s discovering problems that nobody even realized were AI problems in the first place.
“AI does not create value”
On the surface, this statement appears to be indefensible. Around the world, companies are injecting billions of dollars into AI precisely because they expect to see value creation—increased efficiency, smarter decisions, innovative products. Yet, the claim holds an important truth. AI does not create value merely by existing.
If AI Isn’t Intelligent, Are We?
Dismissing AI as non-intelligent may feel safe, but it carries a hidden implication: much of what humans do might not qualify either. The debate exposes a deeper tension—not about machines, but about how fragile, flexible, and perhaps self‑serving our definition of intelligence really is in practice, especially when it threatens our sense of uniqueness.
Do We Still Need Software Architects?
If AI can generate working software in minutes, why does software architecture still matter? As prototyping becomes cheap and iteration nearly instant, traditional upfront design starts to look increasingly obsolete. Yet this shift raises a critical tension: while speed and experimentation improve, we may be trading away long-term structure, maintainability, and scalability in the process.
Picks, Shovels, and Silicon: Who Wins the AI Gold Rush?
What happens when anyone can build software instantly? As AI drives marginal costs to near zero, the industry begins to resemble a gold rush—except the winners may not be the builders. Companies like OpenAI are selling the “picks and shovels,” capturing value while developers face intensifying competition and shrinking margins.
Why Taxing AI Misses the Point
An algorithm tax may seem like a natural response to the rise of AI, but it misunderstands how digital markets work. As software becomes abundant and cheap to produce, competition drives prices and profits toward zero. Instead of generating broad taxable value, AI concentrates economic gains in a few dominant platforms, making taxation far more complex than it appears.
Why Stopping ChatGPT From Lying Could Make It Useless
OpenAI thinks it’s found the root of AI “hallucinations” — and a way to fix them. The idea? Teach models to admit when they don’t know instead of bluffing. But here’s the catch: a more cautious ChatGPT might refuse answers so often that users lose patience, and costs could soar. The solution could strip away the bold confidence that made ChatGPT irresistible in the first place.
Generation Laid Off: AI’s First Casualties in the Job Market
AI isn’t just changing the workplace—it’s reshaping who even gets to enter it. A groundbreaking Stanford study reveals that the very workers once seen as the future—22 to 25-year-olds in tech and customer service—are now its first casualties. Entry-level jobs in AI-exposed roles have plunged by double digits since late 2022, while older workers hold steady.
What you can ask from ChatGPT: A Cheat Sheet
This blog post presents a cheat sheet for working with ChatGPT, a large language model developed by OpenAI. The cheat sheet includes a range of useful commands and tips for fine-tuning the model, generating text and controlling the level of context and specificity in the model's responses. At least ChatGPT thinks so.
