Power markets look relatively simple next to divining the next chapter in the Strait of Hormuz, but after a couple of days at EVOLVE2026, what I am left with is how little is clear.
Consider power and energy transition, where I spend most of my time. Project economics are now hyper-location specific. Load, land, interconnection, deliverability, fuel, policy, saturation, buyer behavior. Get the combination right and the asset prints. Miss one variable and the site goes nowhere.
The harder problem is the rate of change, and you would be forgiven if it leaves you feeling a little off balance. Environmental attributes are trading more like derivatives of the political environment than the output of supply and demand. IPP valuations have rerated in recent months on the appetite of hyperscalers to sign PPAs against a subset of their assets: see our take on NRG and CEG. Data centers are moving behind the meter, but for how long? The pieces are too interconnected, excuse the pun. Power markets now need scenario-based models that clear energy, capacity, attributes and project economics together.
Zoom out and the old pattern is visible. Value accrues to whatever is scarce, deliverable and contractable. Everything abundant gets discounted to the floor.
Which brings me to the AI sessions, and the perspective that reasoning itself is becoming abundant. Every company will soon have access to capable models trained on public data, which means the model will not be the moat. One speaker pushed it further: AI is an amplifier. It does not change what you are, it amplifies what you already are. Leaders pull further ahead, laggards fall further behind, the middle gets squeezed.
It sounds hyperbolic, but the undertone was there in the coffee chats and dinners. Folks are excited. But there was also quiet conviction that everybody is behind and the hard questions are just beginning. For some, enterprise restrictions limit which models get deployed. For others it was apprenticeship loss. How does a 25-year-old analyst get the reps to know what good looks like when the first draft is always waiting, or when the MD prompts the model instead of asking the analyst?
For everyone, whatever they are doing, it is not enough.
I feel it too. But that may be the point. If reasoning is abundant and AI is an amplifier, advantage moves underneath the model or above it. Underneath, sits proprietary data, controls and tech. Above, sits the judgment to make decisions before the model has the answer. Both are hard, but the kind of hard AI will reward.
Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!
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