AI is everywhere these days, and everyone has opinions and thoughts. These are some of mine.

Full disclosure: for a time I worked for Anthropic, the makers of Claude.ai. I no longer do, and nothing in this post (or elsewhere on this site) is their opinion or is proprietary to them.

How to use AI

My advice about using AI is simple: use AI as an assistant, not an expert, and use it judiciously. Some people will object, “but AI can be wrong!” Yes, and so can the internet in general, but no one now recommends avoiding online resources because they can be wrong. They recommend taking it all with a grain of salt and being careful. That’s what you should do with AI help as well.

We are all learning how to use AI well. Prompt engineering is a new discipline. It surprises me that large language models (LLMs) give better answers if you include phrases like “think step-by-step” or “check your answer before you reply” in your prompt, but they do improve the result. LLMs are not search engines, but like search engines, you have to approach them as unique tools that will do better if you know how to ask the right questions.

If you approach AI thinking that it will hallucinate and be wrong, and then discard it as soon as it does, you are falling victim to confirmation bias. Yes, AI will be wrong sometimes. That doesn’t mean it is useless. It means you have to use it carefully.

I’ve used AI to help me write code when I didn’t know how to get started because it needed more research than I could afford at the moment. The AI didn’t produce finished code, but it got me going in the right direction, and iterating with it got me to working code.

One thing it seemed to do well was to write more tests given a few examples to start from. Your workflow probably has steps where AI can help you. It’s not a magic bullet, it’s a tool that you have to learn how to use.

The future of coding

In beginner-coding spaces like Python Discord, anxious learners ask if there is any point in learning to code, since won’t AI take all the jobs soon anyway?

Simon Willison seems to be our best guide to the head-spinning pace of AI development these days (if you can keep up with the head-spinning pace of his blog!) I like what he said recently about how AI will affect new programmers:

There has never been a better time to learn to code — the learning curve is being shaved down by these new LLM-based tools, and the amount of value people with programming literacy can produce is going up by an order of magnitude.

People who know both coding and LLMs will be a whole lot more attractive to hire to build software than people who just know LLMs for many years to come.

Simon has also emphasized in his writing what I have found: AI lets me write code that I wouldn’t have undertaken without its help. It doesn’t produce the finished code, but it’s a helpful pair-programming assistant.

Can LLMs think?

Another objection I see often: “but LLMs can’t think, they just predict the next word!” I’m not sure we have a consensus understanding of what “think” means in this context. Airplanes don’t fly in the same way that birds do. Automobiles don’t run in the same way that horses do. The important thing is that they accomplish many of the same tasks.

OK, so AI doesn’t think the same way that people do. I’m fine with that. What’s important to me is that it can do some work for me, work that could also be done by people thinking. Cars (“horseless carriages”) do work that used to be done by horses running. No one now complains that cars work differently than horses.

If “just predict the next word” is an accurate description of what LLMs are doing, it’s a demonstration of how surprisingly powerful predicting the next word can be.

Harms

I am concerned about the harms that AI can cause. Some people and organizations are focused on Asimov-style harms (will society collapse, will millions die?) and I am glad they are. But I’m more concerned with Dickens-style harms: people losing jobs not because AI can do their work, but because people in charge will think AI can do other people’s work. Harms due to people misunderstanding what AI does and doesn’t do well and misusing it.

I don’t see easy solutions to these problems. To go back to the car analogy: we’ve been a car society for about 120 years. For most of that time we’ve been leaning more and more towards cars. We are still trying to find the right balance, the right way to reduce the harm they cause while keeping the benefits they give us.

AI will be similar. The technology is not going to go away. We will not turn our back on it and put it back into the bottle. We’ll continue to work on improving how it works and how we work with it. There will be good and bad. The balance will depend on how well we collectively use it and educate each other, and how well we pay attention to what is happening.

Future

The pro-AI hype in the industry now is at a fever pitch, it’s completely overblown. But the anti-AI crowd also seems to be railing against it without a clear understanding of the current capabilities or the useful approaches.

I’m going to be using AI more, and learning where it works well and where it doesn’t.