Everything suggests that the impact of Artificial Intelligence will grow exponentially.
With the emergence of autonomous agents that I have been covering in recent days, a rupture is occurring in the software world, a blow to the SaaS model, and the entire business ecosystem promises to transform at great speed.
Unless... that exponential curve crashes into a wall of silicon, energy, and above all, time.
While everyone is watching the software, very few are paying attention to the physical infrastructure. And the data we are analyzing for the 2026-2030 period shows an imminent structural crisis.
Until today, AI consumption depended on us. A human wrote a prompt, waited, read, and wrote again. It was growing traffic but at a "human pace."
But the demand paradigm has just been inverted. The transition toward "Autonomous Agents" --systems that plan, decide, act on their own and coordinate armies of sub-agents-- changes the rules of the game. It is no longer a human typing; it is machines talking to machines at "machine pace" and 24/7.
This subjects global networks to unprecedented and growing strain. It is projected that by 2027, inference (daily AI usage) will surpass model training, representing 75% of all computing needs by 2030.
What is the first bottleneck? It is not just the processors. It is the memory.
High Bandwidth Memory (HBM), critical for AI accelerators, faces a brutal physical barrier: manufacturing a single HBM wafer consumes 3 times the production capacity of a traditional DRAM memory wafer. Major manufacturers are reassigning more than 40% of their capacity exclusively to HBM.
The result? A supply chain collapse. We are facing the worst memory deficit in 15 years. Costs are skyrocketing (up to a 75% increase in a single month) and delivery times, which used to be 8 weeks, now exceed 20 weeks.
This is where AI's projection is put in check. You can deploy a thousand autonomous agents with a click in one second, but building the physical infrastructure to sustain them takes years.
The industry is colliding with the laws of physics. By 2030, data centers in the U.S. alone will exceed the electrical demand of the entire state of California. And energy is not downloaded from the cloud: wait times for a new data center to connect to the power grid in primary markets (like Northern Virginia) already exceed 4 years.
The same applies to the manufacturing of fundamental components. Mass production of the next generation of processors (like TSMC's 2-nanometer node) will take years of development and will only begin in 2026. At the same time, new architectures demand massive consumption: AI racks reach extreme densities of up to 150 kW, making liquid cooling mandatory.
Meanwhile, the 5 major "Hyperscalers" (Microsoft, Alphabet, Amazon, Meta, Oracle) are playing Russian roulette with economic risk, spending nearly 700 billion dollars in Capex (infrastructure) in 2026 alone. By diverting all investment toward this, "traditional" clouds are left unattended, and analysts are already projecting massive outages lasting several days.
Fascinated by the power of vibe coding (all of Scalabl's systems and automations were built by me without programming knowledge), I discovered yesterday that the cost of v0 (the wonderful tool I use to make everything possible) had increased dramatically. This morning, I spent 90 USD in 2 hours of work.
Technology is a multiplier, but without a business strategy, without a solid business model and a clear operating model behind it, automating and delegating to AI only accelerates failure.
Pressured by hardware limitations and pressured by the limitations of their business models and revenue (OpenAI is now experimenting with advertising), I suppose AI's free tier will be under strong pressure to be limited or disappear.
As entrepreneurs, this is a moment of enormous risks and infinite possibilities for reinvention. But it requires strategic focus, the ability to stay continuously informed, flexibility, and the capacity to make the best decisions using innovation methodology.
