It’s November 2025, nearly three years after ChatGPT became publicly available.1 Three years of hype, three years after the record-breaking user growth2, three years of promises that AI would transform everything, and three years of that transformation always being just around the corner.
I’m generally pro-LLM. At my last two companies, I ran user groups to bring people together — technical and non-technical — to educate, connect, and evangelize around the responsible use of AI. I’ve led product teams building models to improve customer experience and home security, seeing measurable impact on satisfaction and adoption.
Often, these successes came despite headwinds: misunderstanding, fear, and leadership unfamiliarity with AI. We had to educate executives on what AI was, what it wasn’t, and where it could help. We pushed to let data scientists do the data science, rather than forcing them into traditional software development models.
The Gold Rush Hits
Then ChatGPT arrived, and it felt like everything we’d built — metrics, prioritization, careful problem selection — could suddenly be replaced by simply ‘throwing an LLM at it.’ Promises flew: search is dead, coding is dead, thinking is dead. AGI is just around the corner.
Businesses rushed to stake their claims, building wrappers around LLMs. One API call to solve everything. CoPilots for every task. Flashy demos everywhere. Executives saw dollar signs from revenue gains and headcount reductions.
Projects worldwide were paused, shelved, or converted into LLM initiatives. Funding poured in, often for initiatives that hadn’t even existed weeks earlier. The goal shifted: from solving important business problems to showcasing generative AI quickly.
The Barons and the Tools
The “barons” who built the models and hardware were rewarded with massive investments, copyright protection, and enormous data access. Vendors selling platforms and tools gained huge funding and an endless supply of prospectors eager to mine their land.
And like every gold rush, there were always “better” tools on the horizon. A new API promising 10x productivity. A new model promising “real” multimodality. A new agent framework that would “finally” automate everything. The land just over the ridge was always more fertile than the land you were currently standing on. And teams spent real money and real time chasing it — sure, this time the promise would finally pay.
The promise of “grab a shovel and get your gold” was marketing, not reality. Easy-to-get gold runs out; mining becomes technical, requiring skill and know-how. The dream of instant wealth fades. Too often, it’s fool’s gold — investments in tools and access are never recouped.
Reality Hits
Suddenly, hallucinations become a board-level word. Reliability matters. “Just call the LLM” is no longer enough.
Hallucinations, integration friction, and workflow complexity appear. Legal briefs with fabricated citations, inconsistent customer support responses, and hallucinated business documents turn reliability into a top concern. A model that works in a demo may fail in production, exposing operational, financial, and reputational risks.
The illusion of ease, the desire for speed, and the dream of instant ROI never materialized. Rapidly built demos often worked only on the surface. Quick prototypes, bolt-on integrations, and low-discipline AI-generated code created massive technical debt3 — problems no LLM could solve alone. Many early adopters found fast paths to value required extensive rework, refactoring, and governance. Projects stalled or never reached production.
These failures weren’t a surprise — they echoed the same issues we’d faced when hype outran preparation.
Mining Real Value
Three years in, many companies still haven’t figured it out. They’re digging for gold, chasing demos, hoping for a lucky strike. A few got lucky and saw big value — but most only saw modest gains, if any. Articles and studies show the promised ROI often didn’t materialize. The dream of instant impact remains elusive.
In that scramble, businesses and their customers often suffer. The barons still own the land, controlling the most valuable resources. Vendors who sold the tools have already moved on to the next rush. The cycle repeats.
The hope is that we finally learn the lesson: generative AI doesn’t deliver value through hype, demos, or shortcuts. True success comes from patience, discipline, and relentless focus on real value — careful engineering, thoughtful product design, high-quality data, and robust workflows. These principles aren’t just for today’s LLM hype; they matter for whatever technology or “next rush” comes next.
Shiny demos grab attention, but only foundational work separates the companies that thrive from those still chasing fool’s gold.