2025 marks three years of mainstream AI- less a jobs apocalypse, more a slow, uneven shock we’re still not prepared to handle.
Three years after ChatGPT’s public debut, the narrative around large language models (LLMs) feels very different. The story investors told themselves about capex ($350 billion in 2025!), enterprise adoption (actually falling), and Artificial General Intelligence (AGI) (nowhere in sight) is proving problematic. At the same time, concerns about job-evaporation have been somewhat mitigated, and the promise of generative and agentic AI as a useful tool in vertical markets, as well as general-purpose horizontal areas, continues to unfold.
With three years of rapid innovation behind us, it’s clear that AI’s practical gains are arriving unevenly. Meanwhile, the mass media and industry leaders’ narratives around jobs, infrastructure spending, and AGI are drifting farther from reality.
Jobs: Slower Shift, but with Tail Risk
While early reports show no immediate disruption to the job market- macro conditions, rather than AI and robots, are currently affecting white-collar jobs- anecdotal evidence suggests the shift is undeniably underway. Geoffrey Hinton, the “Godfather of AI,” recently warned that leaders haven’t yet considered the consequences of rapidly improving AI replacing cognitive labor in a demand-constrained economy.
For now, predictions about the speed of job displacement have proven wrong, but the probability remains high, with severity yet to be determined. AI looks more like a slow-burning general-purpose technology than a sudden labor apocalypse. Waiting for clear indicators, like rising unemployment, may leave us unprepared for a difficult challenge. It’s crucial to monitor early signs of employment slowdowns as AI’s influence on the workforce will unfold unpredictably, and unevenly, and maybe exponentially with major consequences for industry and society.
Capex-Revenue: Growing Disconnect
In 2025, Microsoft, Amazon, Alphabet, and Meta will have invested $344 billion in AI infrastructure, up from $228 billion in 2024. However, the revenue side looks stark. Annual AI revenue, not “someday” AI revenue, needs to hit $320-480 billion to support the capex build-out. This is a steep challenge, particularly as the vast majority of AI use is either through bundled offerings or is free to end users. OpenAI, for example, recently declared a “Code Red” due to competition from Gemini 3.
Despite the lack of ROI, adoption is surging. McKinsey research reveals that 88% of organizations use AI, but only 39% report EBIT impact, with most saying AI contributes under 5% to profits. Scaling beyond pilot phases remains tricky — a pattern Gartner’s Hype Cycle captured in 1995.
Adding to the uncertainty is rising algorithmic risk. DeepSeek’s release of version 3.2, which is 25–30 times cheaper than GPT-5 and Gemini, raises questions about the cost-effectiveness of today’s leading models. Yann LeCun, a deep learning pioneer and former head of AI for Meta, has critiqued LLMs as “not a path to human-level intelligence” and is now launching a new company focused on alternative AI architectures.
As respected experts pivot to new directions, there’s growing concern that a significant portion of today’s AI investments is misdirected. While giants like Microsoft and Google will likely weather the storm, others will struggle. Overbuilding, slow monetization, and rising uncertainty about AI’s future architecture suggest that a re-pricing of the sector-aka a bubble burst- is coming.
LLMs: Powerful but Not AGI
Gary Marcus, a prominent critic of LLMs, recently published a retrospective. He argues that AGI should no longer be the primary goal of AI research. Instead, he advocates for more targeted, verifiable tools and architectures that combine learning with explicit reasoning. This view is gaining traction. Yann LeCun’s critique of LLMs and push for world-model-based architectures aligns with Marcus, and even OpenAI co-founder Ilya Sutskever has acknowledged that the “just add GPUs” approach is over.
LLMs are growing and remain economically important. In enterprises, the future will likely center on small/medium models (SLMs) tailored to domain-specific needs. These models are more cost-effective, secure, and suited for demanding environments where 80-90% accuracy is a non-starter.
LLMs and SLMs remain increasingly powerful general-purpose technologies- but they are more akin to relational databases than AGI. This should temper expectations for AGI-driven explosive growth over the next 3–5 years.
2026: The Road Ahead
Taken together, these three hard truths point to a simple conclusion: AI will continue to dominate rhetoric and pilots, but will be under immense pressure to demonstrate economic value and to withstand sharper market scrutiny of revenue expectations. For those of us in the ring with this evolving technology, organizational agility and informed investments will continue to guide our way through 2026.
Will Poole is Co-Founder & Managing Partner, Capria Ventures, a Global South-specialist Venture Capital firm.
