Where I stand with AI, and where a reader might

A year ago I did not believe AI assistants were useful for my kind of work, and I thought what they cost in energy was hard to defend. Now I use one every working day. I did not get there overnight, and I did not change my mind about everything, so I want to tell it in the order it happened.

Where I started

I had two objections, and they were of different kinds.

The first was usefulness. Chemical engineering gave me a modeling habit before it gave me any data. My first project in graduate school was a steady-state flowsheet in Aspen Plus, where every stream was computed and nothing was measured, and the experimental work that followed was the opposite, real measurements and a mass balance that closed on good days. Neither needed a learning algorithm, and for most of that decade I read about neural networks the way I read about fusion. The value of my work sits in a measured number, and I did not see what a language model could add to that.

The second was cost. I work on fuels and energy, and a technology that needs new power stations for its data centres did not look like something an engineer should use casually. The International Energy Agency puts data centres at around 1.5 percent of the world’s electricity in 2024, about 415 terawatt-hours, and expects that to more than double, to around 945 terawatt-hours, by 2030, with AI as the largest driver of the growth.11International Energy Agency (2025), executive summary: data centres accounted for around 1.5% of the world’s electricity consumption in 2024, or 415 TWh, and consumption is set to more than double to around 945 TWh by 2030, with AI the most important driver.

What changed my mind

A friend of mine, a graduate student in the Department of Civil Engineering at UW, convinced me to try it and get started. So I started, in November 2025, having never written a line of Python. The first obstacle was the one every beginner meets, what to install and why it does not run. A hosted notebook took that question away. Google Colab gave me a working Python cell in a browser with nothing installed, and my first physics-informed neural network, on a first-order reactor, ran the same evening. Within a week I had a reading list that went from linear regression to gradient boosting, written for me in the order I needed it.

The second step was the opposite of the first. In February 2026 I moved the work onto my own machine: Python, an editor, a virtual environment, Jupyter. That month is the tall bar in the figure. Most of those thousand messages were error texts: a package name I had mistyped, a Windows alias that pretended to be Python, a search that ran overnight and wrote nothing to disk. Each cost an evening, and each is now a short note on this site. What I learned in that month is where an assistant earns its place: beside work I am doing myself, one cell at a time, explaining the error in front of me. When I let it run the whole analysis in its own window, I learned nothing and could check nothing.

From March to the end of August the curve in the figure is flat. The record behind the figure is my sessions with one assistant, and in those months I was not using it, so the record shows no new tool there. I used other tools, and I read books and articles and took courses, and I practiced on what I had met in February. None of that is in this record.

The third step came in the autumn, when I opened a coding agent in a folder on my own computer. At this stage prompting has little to do with it. The work is to read every line the model writes and run it and keep nothing I cannot explain. The model is fast and confident, and the second property is the dangerous one.

What I did about the cost

The second objection I did not resolve, so I put numbers beside it.

Google has published an estimate for its own assistant: the median text prompt uses 0.24 watt-hours of energy, emits 0.03 grams of carbon dioxide equivalent and consumes 0.26 millilitres of water, and by the company’s account the energy per prompt fell by a factor of 33 over twelve months.22Vahdat and Dean (2025): the median Gemini Apps text prompt is estimated at 0.24 Wh, 0.03 g CO2e and 0.26 mL of water, with energy per median prompt down 33 times over a recent twelve-month period. The figures are the company’s own. That is a company measuring its own product, and it covers a median text prompt, not a long session with a large model. In September 2026 I drew energy per query against model size as an animated chart, to see the shape for myself: the larger the model, the more each answer costs.

So two things are true at the same time. The cost of one question is small and falling. The total is large and rising, because use grows faster than efficiency improves. I use these tools every working day now, and I count the objection as open.

What the figure shows

All three panels are drawn from one source, my sessions with an assistant, and they measure only that. Panel a counts the days in each month on which I had at least one working session: 193 days in a little under twelve months, with February and September at 26 each and June at 4. Panel b counts the messages I wrote, and February alone, with a little over a thousand, holds more than the three months after it combined, which is what learning to program looks like from the inside. Panel c is the learning curve. It counts the tools, methods and working habits I met for the first time, 46 in all, and it has the shape of a staircase with one long landing. It climbs from November to February, when 36 of the 46 arrived. From March to the end of August it is flat, with one first meeting on the last days of August. The flat stretch is not idle time. The bars in panels a and b run through it, which is practice on what I had already met, and it also holds the other tools and the reading that a record of my sessions with one assistant does not see. The curve rises again in September. Named beside it, month by month, are the things I had to learn before I could work with an agent on my own machine, in a terminal and an editor.

The figure is a count of days and first meetings in one kind of record. It is not a measure of competence, and it is not the whole of the year.

Working with an agent did not start overnight

The last squares on the curve are the ones people ask about: a coding agent running on my own machine, with skills, memory files and subagents. It looks like a sudden change, and it was not. Each of those rests on something earlier on the same line.

In November 2025 I learned to keep context in files and not in the chat, because a long conversation forgets and a file does not. In January 2026 a project with an assistant in a cloud workspace forced Git, a repository and a tracker on me, since the file list had outgrown the conversation. In February came the terminal, the package manager, a virtual environment and a notebook on my own machine, and the habit of having a plan reviewed and criticized before anything is built. By August I had met a database, an API and a deployment.

Only then, in September 2026, did I open a coding agent in a folder on my own computer. What made it usable in the first week was everything above: I could read what it wrote, I knew what a commit was for, I knew why the environment mattered, and I already had the habit of asking for the plan first. Skills, which are saved procedures, and memory files, which are saved context, are the same two habits from November and February given names by the tool.

An engineer who starts with the agent and skips the year will get output sooner than I did. I do not think they will be able to check it.

Where that leaves me

A year ago I would have said that I ask a chat assistant for a draft or a summary now and then, and I would have defended staying there. Now I run models on my own hardware and I train and validate models on small tables of my own. I also work with a coding agent in a folder on my computer. Training a model on a small table is easy. Validating the result is the whole problem, and that is where my effort goes.

I have not published a machine learning method or result that someone else can reproduce, and I have not shipped anything that runs without me. What I can do is also narrow. I can handle a small table of runs, and for everything else I am still the person who asks the assistant. Physics-informed networks for reactor models are on my list because the problem fits. Özdemir and Yildirim describe the gap when they note that a purely data-driven model has no knowledge of thermodynamic limits and nothing stops it from predicting a conversion above equilibrium.33Özdemir and Yildirim (2025), p. 10, on hybrid and physics-informed models.

What I still do not trust

An assistant that is asked about a specific published number is wrong often enough that I read the source for every number I use, without exception. Al Sharah and colleagues put the general version of this well when they warn that easy access lets people apply methods they do not understand.44Al Sharah et al. (2024), pp. 623 to 624, under “challenges and drawbacks”. The warning applies to the chat window as much as to a random forest.

I also do not trust my own speed. A year of this has made me faster at producing things, and speed checks nothing. The check I keep is the one from February: if I cannot run it and explain it, it does not go in.

For a reader who has not started

The first step that worked for me was the cheapest one, a hosted notebook with nothing installed and one real problem from my own field. The pieces in the From zero series are the notes I would have wanted in that first month, in the order I needed them.

Footnotes

1. International Energy Agency (2025), executive summary: data centres accounted for around 1.5% of the world’s electricity consumption in 2024, or 415 TWh, and consumption is set to more than double to around 945 TWh by 2030, with AI the most important driver. ↩

2. Vahdat and Dean (2025): the median Gemini Apps text prompt is estimated at 0.24 Wh, 0.03 g CO2e and 0.26 mL of water, with energy per median prompt down 33 times over a recent twelve-month period. The figures are the company’s own. ↩

3. Özdemir and Yildirim (2025), p. 10, on hybrid and physics-informed models. ↩

4. Al Sharah et al. (2024), pp. 623 to 624, under “challenges and drawbacks”. ↩