If you trace the timeline of how LLMs went from a technologist's dream to early text-generation toys, to the world-shifting launch of ChatGPT, and finally to the daily drivers of modern programming (Sonnet, Opus), it has taken less than a decade. It’s a thrilling, almost unbelievable tale.

Let's look at how we got here, and the wall the industry is currently hitting.

- The Dream Phase (2010-2016). By the dawn of the last decade (2011), an interesting thing was happening. The two platforms, Wikipedia and Stack Overflow, had started gaining tremendous traction, folks were collaborating on these platforms to openly exchange knowledge. Looking back, this feels like a more ideal, community-driven path for humanity — one we abandoned for the centralized architecture we have today.

- The Disruption Phase (2016-2021). A perfect storm of unrelated events paved the way for AI. By 2017, new programmers were growing deeply frustrated by Stack Overflow's rigid policies, subjective question rejections, and senior coder pedantry. In retrospect, those strict moderators carved the first stones of what would later become Copilot and ChatGPT. If the community won't answer a beginner's question without downvoting it, a private LLM gladly will.

Add to this Google's landmark 2017 paper "Attention Is All You Need" which unlocked the Transformer architecture, and the forced isolation of COVID-19 in 2020. The ground was suddenly fertile for virtual assistants that could act as isolated developers' programming partners.

- The Hook Phase (2023-2025). The launch of ChatGPT left no doubt about how easy the "hook" would be. For non-technical folks, it was pure magic. It didn't take long for specialized LLMs like Copilot, Claude and Deepseek to become an indispensable part of the programmer's toolbox. Meanwhile, OpenAI was still advertising its "non-profit" roots, and the consensus was that this was purely about empowering humanity.

- The Endgame Phase (2025-present/future). AI companies had miscalculated a lot of things by this time. They were optimizing for the "long-term" but as John Maynard Keynes rightly said many years ago, "In the long-term, we are all dead". The VCs are losing patience today because while the technology itself has gained massive ubiquity and appreciation, the revenues aren't coming as fast. The hook had sort of worked but failed to fully work.

Most frontier models like Sonnet, Opus and GPT 5.5 are still running on 'subsidized mode'. The amount of monthly subscription they charge users (USD 10/20/30 per month) is a pittance compared to all the compute and RAM needed to run those "thinking..." and "pondering..." tokens. In order to truly show profits in the books and come out of subsidized mode, they must charge on the scaling of input/output tokens and that appears to be difficult. Very few companies might be able to sustain such unlimited budget for unpredictable hardware scaling, the recent Uber story shows exactly what happens when they try doing this.

The frontier models are trying to replace something which could never be successfully delegated or automated in entire human history - the highest cognitive skills of human brain like reasoning, deduction and logic. Yet, the efforts are on and the goals are long term. The conundrum is that if they stop subsidizing, the hook phase may be undone - there is a strong possibility of folks reverting back to older ways of Wikipedia/Stack Overflow or pivot entirely to open source dry/academic models like Llama and Qwen which can run locally on their own hardware. And yet, they also can't keep subsidizing and draining the funds indefinitely.

What happens when the subsidy mirror cracks?

The AI Conundrum: We are living in highly subsidized, interesting times