Evan X. Merz

I write a lot about AI on my blog. I've spent my career studying AI. I completed a doctorate using neural networks to write music, and I worked on AI at one of the biggest tech companies in the world. So when I tell you that your job is probably safe from AI, it's not an uninformed opinion.

The natural response to this argument is simply to point to the recent layoffs. Those are due to the economy. AI is just the cover story to protect the stock price.

Here are five reasons why your job is (probably) safe from AI.

1. AI is expensive

Have you seen all the stories in the news about exploding AI costs?

Well, without a significant technical advancement, this is the cheapest that AI will ever be. AI is currently being subsidized by venture capital funding. The VC firms are trying to get you addicted to AI at a cheap price so that they can create a market, then raise the prices to where it is profitable.

Your employer is already struggling to justify their spending on AI. They aren't going to be happy paying more for a technology where the benefit is still so poorly defined.

2. The caching problem

AI can never be as cheap as traditional SaaS businesses due to the caching problem. I've written about this at length, so I'll just quote myself here.

The fundamental problem facing everyone in the AI race is that requests to AI can't be cached in the same way. Even if users are making very similar requests, they might not want exactly the same response. In other words, when user A tries to generate a cartoon image of a cat, they probably want one that looks like their cat. When user B makes the same request, they probably don't want the same image as user A.

Everyone knows that AI involves some very complex math that uses Generative Adverserial Networks. These networks, even when not in training mode, can't be cached like traditional web requests. They need to be run on actual servers.

This is what has resulted in the big debate about server resources. AI inherently requires more computing power than traditional web services.

The point here is not that AI can't use caching, it does. The point isn't that AI can't scale, it can.

The point is that it will never be as affordable as a traditional SaaS application. Even when many of the currently known problems are solved, it will still be at least an order of magnitude more expensive than a typical API.

3. AI is unsustainable

Sustaining the internet requires an immense and efficient supply chain. The world needs thousands of new CPUs and GPUs every day, and it can barely make them fast enough.

Sustaining AI requires an order of magnitude more materials that are difficult to engineer and manufacture at scale. Our world can't even keep up with the scale of AI needs today, as can be seen in the exploding cost of computer memory and graphics chips. How is the world going to sustain AI usage when climate change really starts impacting our global supply chain? The broken supply chain during COVID was a sneak preview of what's going to become more and more frequent in the next decades. It will be a miracle if we can keep the worldwide internet up, let alone the additional needs of running LLMs.

4. Large problems require impossibly large context

Doing your job well requires an immense amount of knowledge. It requires knowledge about the business you work for, knowledge about the customer, and knowledge about how to execute your day to day tasks. For LLMs, this information is called the context window, and it limits the amount of information that an LLM can use to reason about a problem. Expanding the context window is difficult, and the models that use larger context windows are more expensive to run.

There might come a day when an AI can hold all the information needed for relatively small jobs, but even that AI will be expensive, and it's never going to be able to do the high level tasks related to your job.

The real job in software engineering is managing the complexity of the codebase, and this can only be done with a high level understanding of the code and architecture that can't be fit into even a large context window. To a greater or lesser degree, this is true of all office jobs.

5. Some of these problems are unsolvable

The final reason why AI probably isn't going to take your job is that some of the known problems are unsolvable. There are many blank spots on the map of AI that we don't think we can fill in. Even when looking at relatively simple parts of the AI infrastructure that we use today, there's lots of stuff that isn't a 100% reliable solution. This applies to AI guardrails, caching, and even data interchange protocols like MCP.

I've already written about the problems with MCP, so I'll just quote myself again.

The MCP spec doesn't specify how to trigger tools/actions based on the prompt or the output of an LLM. In other words, there is nothing in the spec that says what the user or LLM must say in order to tell the client to fetch data from the MCP server.

This leaves each client to do things a little bit differently, and it means that each client is going to be developed based on however their favored model works. A trigger mechanism that works for one client/model/host combination might not work as well for another client/model/host combo.

This is a massive blank spot in the MCP spec, and is, arguably, the most difficult part of this problem. The spec makes it clear that MCP client to MCP server communication is via JSON-RPC, but it leaves out how the LLM to MCP client communication is supposed to occur.

There are dozens of similar problems facing the AI industry today.

AI is still going to change the world

For these reasons, your job is probably safe from being taken over by AI, but that doesn't mean that AI isn't going to change things. The ability to run a machine that can think at a nearly human level is an incredible super power. It's going to change the way we work and plan and think about the future. You should be learning how to use it, and figuring out how it's going to be useful to you in your life.

Your job is probably safe from AI, but it's definitely not safe from the state of the economy.