Ramez Naam

Scientists have discovered AI data centers are even worse for the environment than we thought. Big data centers create "heat islands" that raise local surface temperatures an average of 3.6°F, enough to potentially devastate local wildlife and water supply.

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

Morgan Stanley has again raised its capex forecasts for the five hyperscalers Amazon, Alphabet, Meta, Microsoft, and Oracle. It now expects them to spend about $805bn this year, up from a previous estimate of $765bn. For next year, the forecast has been lifted from $951bn to $1.1TRILLION. To put that into perspective, their 2026 spending alone would be roughly equal to what all non-tech companies in the S&P 500 spent combined in 2025. The expected ~$800bn for 2026 is nearly double 2025 levels and about three times what was spent in 2024.

I think returns to intelligence are nonlinear because decisions are path-dependent early choices in code, experiments, or strategy can compound positively or negatively over time for example by avoiding dead ends or preserving optionality it's why I am a big fan of very long running tasks and massive benchmarking budgets GPT-5.5 and Mythos Preview are only marginally more intelligent than previous models and have pretty much the same performance up to 10M tokens, but after that they go absolutely ballistic

Unmanned Systems Forces struck Su-57 and Su-34 fighter jets It has been confirmed that on April 25, at the Shagol airfield in the Chelyabinsk region of russia, operators of the 1st Separate Center struck two Su-57 fighter jets, an Su-34 fighter-bomber, and an SU of unknown modification. The strike was delivered at a distance of 1,700 km from the state border. "Hunting for Su-34 multi-role fighter-bombers and Su-57 fifth-generation fighter jets is of critical importance for reducing the enemy's strike potential. The Su-34, as the main strike platform, is capable of carrying a wide range of guided bombs and missiles, delivering strikes on critical infrastructure, military facilities, and civilian targets at a distance of up to 1,000 km. Every destroyed Su-34 means a reduction in the number of airstrikes, saved civilian lives, and a decreased burden on air defense systems. The Su-57, as the most modern russian fighter jet with low-observable technologies, poses a special threat to aviation and air defense systems." — Commander of the USF Robert "Magyar" Brovdi. The estimated cost of one Su-34 aircraft is approximately $35–50 million. The cost of an Su-57 fighter jet reaches $100–120 million per unit. USF: One step ahead!

There is a narrative out there that the economy will soon be ruled by a single (maybe two) AI ceo overlord who owns most of the capital. This picture suggests otherwise. The landscape is very competitive with open models clipping at the heals of closed ones. There is still no mote. Most of AI benefits are downstream.