“Meta, Amazon, Microsoft, Google, and Tesla are expected to have spent some $560 billion on AI development by the beginning of next year, their collective revenue from AI comes in at a paltry $35 billion.
This revenue gap is the key crisis facing the tech industry and the broader economy in their quest to build an AI future. This is leading to the very real possibility of a rupture forming in the broader economy, where companies induce rounds of layoffs to satisfy executives, without anything to show for it in the bottom line.
In other words, we’re barreling toward a future where unemployment could rise while productivity takes a nosedive, dramatically slowing the economy as a result.”
The cause isn’t mysterious. We put all our eggs, $10T of them into one approach that brute-forces its way to “intelligence” through scale, with structural flaws baked in. Scale buys fluency, not adaptation, the non-negotiable requirement for real intelligence. Demos sing on stage, then stall in production when the real world gets messy. Worse, every new user and query lifts marginal cost, making sane unit economics impossible.
Structural problems:
1) Non-adaptive by design: Frozen weights, no real-time learning; every “fix” (RAG, longer context, fine-tunes) is a band-aid, not knowledge.
2) No world model: Surface pattern-matching without grounded causality.
3) Unreliable & ungovernable
4) Resource consumption: Insatiable compute, power, and data; latency and energy scale the wrong way.
5) Economics: Marginal costs rise with usage; retrains and depreciation vaporize ROI.
So, why aren’t we investing in alternate approaches? If today’s systems fail in production, aren’t reliable/explainable/auditable/repeatable and marginal cost rises with use, what exactly are we waiting for?
There is a way out: Cognitive AI, that isn't trained like any other AI, rather raised like a child through a carefully orchestrated developmental arc. Its fully integrated neuro-symbolic architecture supports a trajectory akin to human learning: starting with perception, basic concepts and concrete thought forming a world model and gradually advancing toward abstract reasoning, symbolic manipulation, and self-reflection. It learns like humans, and scales like software.
Each stage brings it closer to the intellectual maturity of a human mind, charting a course from early childhood learning to eventually engaging with the world at the level of a PhD. It learns incrementally, adapts autonomously and updated its own model in real-time continuously. An integrated neuro-symbolic stack that learns through experience delivers repeatable outcomes, predictable costs, and governance you can sign, and requires less than 1% of the funds being directed towards GenAI/ LLMs.
Back Cognitive AI: intelligence compounds, investments compound, ROI compounds, and metrics follow.