Speed, cost, quality and efficiency of tokens are driving the whole narrative recently. We have been asking the wrong question. In the mad rush, we have started pivoting to harnessing and prioritising more energy, water, land and chip economy for AI than humans.
For the last two years, the corporate world has been obsessed with one metric: AI speed. How fast can the model generate? How many agents can we deploy? How quickly can we automate this process?
But in our race to make machines faster, we forgot a fundamental law of physics: Human biology doesn’t scale and is not speeding up to match AI speed.
There is a moment of clarity when you realise that the most expensive resource in the AI economy isn’t the chip; it is the 5 seconds it takes a human brain to context-switch. We have been so focused on removing the compute bottleneck that we have accidentally created a much bigger one: The Attention Bottleneck.
I recently came across a sentiment that perfectly encapsulates this shift: “Compute scales infinitely. Human cognition doesn’t” and “AI’s biggest bottleneck isn’t silicon. It’s synapses.”
AI handled tasks that lasted a few minutes from 2020 to 2025. Now, AI agents run for hours. Tomorrow, they will operate entire “pyramids” of workflows simultaneously, generating a tidal wave of output that no individual or team can realistically process.
We are already seeing the symptoms. First, there is the Context-Switching Trap; dozens of AI-generated summaries competing for our focus, pulling us away from the deep thinking required to build a business. Then, there is the Review Bottleneck; where AI operates at the speed of silicon, but our eyes still read at the speed they are capable of.
However, there is a silver lining within this chaos. As this bottleneck tightens, the cost of raw intelligence is collapsing. Token costs are predicted to drop roughly 2X every month on average.
Let that sink in. If intelligence becomes nearly free, the value of “generating more stuff” becomes zero. The winners in this thriving startup ecosystem won’t be those with the most agents in their Slack channels. They will be those who design systems that prevent human overload.
We are moving into an era where Generic Outputs are commodities. But Deeply Reasoned Outputs built on proprietary data, domain expertise, and unique context become priceless.
For startups, this is a massive opportunity. Don’t build a business around access to AI; everyone will have that. Build your business around proprietary data or specialised workflows that a generic AI LLM cannot replicate. Build trust. Build distribution.
The future isn’t about autonomous agents running wild. It is about orchestration. It is about creating the perfect handshake between the brute-force calculation of a machine and the nuanced, intuitive judgment of a human mind.
Is your organisation building “Focus, Wisdom and Signal” into its AI strategy? Or are you just adding noise? Are you already feeling the fatigue of reviewing AI-generated content?
That is the question CXOs and AI leaders need to ask.
Disclaimer
Views expressed above are the author’s own.
