📝 TL;DR After two years of AI hype, investors are no longer rewarding every company that says the word “AI.” Markets are shifting into a “show me” phase where only businesses proving real, measurable AI revenue and profits are being taken seriously. 🧠 Overview A new analysis of AI focused stocks says the easy phase of the AI trade is over. The first wave was simple, buy anything connected to chips, cloud, or models and ride the boom. Now, with talk of an AI bubble growing louder, investors are demanding proof that AI is driving real sales, margins, and product stickiness, not just headlines. OpenAI’s success has set a new bar, everyone else has to explain how they will actually make money from AI at scale. 📜 The Announcement The report looks across the current landscape of AI related stocks and argues that markets are entering a tougher, more selective stage. Chip makers and cloud giants that clearly profit from AI infrastructure are still in focus, but second tier “AI story” names are under pressure to justify their valuations. At the same time, OpenAI’s rapid revenue growth and product adoption highlight a key frustration for public investors, some of the biggest AI winners are still private, so the question becomes which listed companies will actually capture that value in the years ahead. ⚙️ How It Works • The first AI wave was broad - In 2023 and 2024, many investors bought almost anything labeled AI, from chip makers to obscure software names, which pushed valuations up fast. • The “show me” phase is about earnings - Today, investors want to see clear evidence of AI driven revenue, higher margins, or lower costs, not just promises in PowerPoint decks. • Leaders versus followers - Core infrastructure players that sell GPUs, cloud capacity, and AI platforms still have strong narratives, while companies that only sprinkle AI on existing products face harder questions. • OpenAI sets the benchmark - OpenAI’s rapid growth shows what a real AI business can look like, which makes it easier for investors to compare other companies and ask, where is your version of that.

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When organizations talk about AI transformation, the language often sounds ambitious. Roadmaps, enterprise strategies, and long-term visions dominate the conversation. While these plans are well intentioned, they frequently stall. Not because AI is not powerful, but because momentum is fragile. In practice, small, tangible AI wins create more progress than the most sophisticated strategy deck ever will. ------------ Context: The Weight of Big AI Ambitions ------------ Large AI initiatives often begin with high expectations. Leaders want to get it right. They want alignment, governance, and measurable impact. The result is months of planning before anyone meaningfully uses the technology. During this time, confidence erodes. Teams hear about AI constantly but rarely experience it helping them day to day. Skepticism grows. When AI finally arrives, it feels heavy and overdesigned. This dynamic is especially risky with fast-moving tools. By the time a grand strategy is approved, the landscape has shifted. What felt comprehensive now feels outdated. People disengage not because they dislike AI, but because it never becomes real. Small wins avoid this trap. They trade certainty for momentum. ------------ Why Big Strategies Struggle in Practice ------------ AI is not a static capability. It evolves weekly. Strategies built too early often optimize for assumptions that no longer hold. There is also a psychological cost to big plans. When expectations are high, experimentation feels risky. People hesitate to try imperfect uses because failure feels visible and consequential. Big strategies also tend to abstract value. They promise future efficiency rather than delivering immediate relief. Without felt benefit, adoption becomes performative rather than practical. None of this means strategy is unnecessary. It means strategy should follow evidence, not precede it. ------------ Small Wins Create Belief ------------ Belief is the missing ingredient in most AI initiatives. Not belief in the technology, but belief in ourselves. Do we trust that we can use AI meaningfully in our own work?

Hello my name is Matthew Blumhardt and I am hoping to remake myself and my future with the help of AI and a support group.