AI

The Illusion of "Data-Driven" In 2026, we are drowning in information but starving for synthesis. Most organisations claim to be "data-driven". They have dashboards that glow with real-time metrics and research repositories overflowing with colourful PDFs. Yet despite this abundance of evidence, they continue to build products nobody wants and launch disconnected campaigns that nobody hears or understands. The problem isn't a lack of insight. It’s an insight-horading problem.

Ditch the 100-page Brand Bible. Learn why fast-moving SaaS companies need a "Liquid Brand"—a testable, agile operating system built for the AI era.

You probably think I’m doubting my warnings about "wrappers" now that the headlines are full of billion-dollar exits. I’m not. In fact, I’m doubling down. As we head toward 2026, the great AI sugar rush of the last twelve months is finally wearing off. The hype hasn’t disappeared; it has simply met the cold, hard floor of strategic reality. We are moving away from a year of Invention (building things because we can) toward a year of Reinvention (building things that actual

The "Loneliness Economy" is projected to be one of the defining market opportunities of the next decade. However, the current wave of hardware solutions—epitomised by the device "Friend"—represents a fundamental failure in business design. This forensic audit argues that "Friend" is not a product failure, but a business model failure. In this article, I outline why intimacy cannot be sold as a one-off hardware transaction.

$1.8M for a domain name. $0 for common sense. 💸 The "Friend" AI launch is a masterclass in vanity over value. It forgets your name and acts like a bad roommate. I break down how a simple Fake Door test could have saved investors $10 million and spared its founder viral humiliation.

Choosing an LLM (GPT, Llama, etc.) is a financial bet, not a tech spec. There's a Three-Layer Framework you can use: Architecture (RAG vs. Wrapper), Engine (Moat vs. Speed), and Moat. Learn how to choose responsibly, avoid exploding costs, and ensure your AI provides a true, defensible advantage in this article.

The biggest risk in AI venture building right now isn't technical failure, it's strategic fragility. Most new products are built on a house of cards: high token burn, easily replicated features, and zero defensible IP. You can build something fast, but can you build something that lasts? Before you invest another hour, cent, or ounce of emotional energy, you should ask yourself these five critical questions!

The tech scene loves a good shortcut. Right now, that shortcut is the "AI Wrapper Business"—a glossy user interface layered over a powerful, off-the-shelf Large Language Model (LLM). It feels like genius: outsource the hard part (building the model) and focus on the wrapper (distribution and design). Low overhead, high potential. What could possibly go wrong? If you want to build a business that provides enduring value and doesn't rely on continuously hiking the price, read t

The excitement around launching an AI-powered product is infectious. The marketing promise—a powerful brain, low maintenance, and negligible operational cost—sells itself. You look at the API pricing for the latest GPT-4o mini or a similar model, see the rate for a million tokens, and think: "We've got margin for days." But that's an illusion. Let me explain.