AI

Some thoughts on how the criteria for high-value scientific work are likely to evolve toward fundamental innovation and physical implementation. Generative AI has significantly lowered the barriers to writing and publishing. As the volume of daily publications accelerates, individual papers are increasingly difficult to distinguish within the literature. In this environment, in my view, two categories of work are likely to retain exceptional value: truly original, paradigm-shifting research, and work that translates scientific concepts into the physical world. ...

Interestingly, the chemical industry — and ethylene production in particular — provides a significant historical precedent related to the ongoing discussions on AI and labor. By the mid-20th century, ethylene plants transitioned to automated flow systems operating continuously with minimal human oversight. Labor productivity improvements within the industry reflect virtually constant manning levels independent of plant size. Empirical analysis demonstrates that the scale coefficient for labor is not significantly different from zero [1]. Consequently, once a fixed level of labor is reached, plant output can be increased to any level by merely increasing other inputs while holding labor almost constant. ...

Anthropic has reportedly raised funding at a valuation close to $1 trillion, and SpaceX may eventually seek a valuation near $2 trillion in its IPO. Obviously, one way to think about where things are going is to look carefully at where we are now. About a year ago, I bought an AMD Ryzen AI Max+ 395 system with 128 GB of quad-channel LPDDR5x-8000 memory. Today, I can reliably run one of NVIDIA’s most capable open models, Nemotron 3 Super 120B A12B, locally. That is an extremely impressive model to be running on a personal machine. ...

In this post, I would like to think about agents that already run our world. They are not the fancy agents that have come to dominate the technology discourse. They are time-tested workhorses. To understand the true challenge of creating a trustworthy autonomous system, I propose to think about the most successful and widely deployed agent in history: the Proportional-Integral-Derivative (PID) controller. I choose the PID controller precisely because it is simple, real, and tested time again in the real world. It is a decision-making policy in its purest form. While the control algorithm itself is well-studied, the enormous amount of engineering required to make it operate reliably highlights the critical gap between an abstract algorithm and a functioning agent. Analyzing these necessary steps provides a crucial reality check for any credible claim about autonomy. ...

Apple’s recent research article, The Illusion of Thinking, made waves. But the reaction quickly moved beyond the technical. For some, it confirmed LLM limitations; for others, it was inconsequential and human-level intelligence is inevitable. And so the cycle continues: one side dismisses, the other inflates. This misses the point. The real question isn’t who’s right about the future, but whether these technologies are useful now, and how they might become more useful tomorrow. As an engineer, I think we should spend less energy predicting what these systems will become and more time evaluating what they allow us to do today. ...