Transcript
00:00:00Let's talk about one of the most stupid trends we saw over the last couple of weeks and months,
00:00:05which, as it seems, is coming to an end already. Rightfully so, because it makes no sense.
00:00:12Token maxing. Token maxing, in case you don't know, is simply about using, or one could say
00:00:18burning, as many AI tokens as you possibly can per month, per year, whatever time period you
00:00:24were measuring. So, the idea, from a company perspective, because this is a term coming from
00:00:30the enterprise world, the idea really is that you want to incentivize your employees to use as many
00:00:37AI tokens, for example, through tools like Cloud Code. And just as a side note, that is a useful tool,
00:00:44just like Codex and these other tools. You can get work done through them. I got courses on Cloud Code
00:00:50and Codex if you want to learn more. They're really in-depth and show you some tips and tricks. But
00:00:54the idea is that you use these tools to burn or use as many tokens as possible, because
00:01:00that will give you great outputs, right? No. As mentioned, these tools are valuable. As a developer,
00:01:09I believe you need to be able to work with these tools, but use them as assistance. The idea behind
00:01:16token maxing, or the incentive behind token maxing, clearly, of course, is that you just waste tokens in
00:01:23the end, that you mindlessly spend them, that you prompt after prompt after prompt, that you look at the
00:01:29output as little as possible, or not at all, of course, because that will just keep you from prompting
00:01:36more. We've heard about companies having internal leaderboards, where the people that spend the most
00:01:42tokens would, well, be on top and potentially get some rewards. And of course, that makes no sense. And of course,
00:01:50I'm mostly talking about AI being used for development here, because that's where I'm coming from. But I would
00:01:57say it doesn't make sense in any context. But especially if we're talking about using AI for writing
00:02:03code or for generating code, you want to understand and review that code. It's not about spitting out as
00:02:11much code as possible. It never was. Even before AI, it was not a good idea to measure the productivity of a
00:02:20developer by the lines of code they can write on a given day. And it's not different with AI. The quality
00:02:27matters. And I know that this seems to be something not all companies would agree with these days. But yes,
00:02:35it does. If you go down the, in the end, wipe coding rabbit hole, and you have AI generate all that
00:02:43spaghetti code, and you totally lose track of what's going on, and you don't understand what the code is
00:02:50about, and you were not able to dive into the code manually at some point, because it's just too much,
00:02:55then you lost. Then you truly lost. Because AI is far from perfect, as we probably all know. So of course,
00:03:03we need that human touch, that human control, to use AI efficiently and to get good results. And
00:03:11that's why I've been saying for all my videos, and I still strongly believe in that, AI can be a useful
00:03:17tool. But it's a tool. It's not the replacement of developers, no matter how much certain companies are
00:03:23wishing for that. And of course, the entire idea behind token maxing in the end is that, hopefully,
00:03:30from the company's perspective, you can get to a point where your employees are using AI so much that
00:03:37they are producing lots of amazing output. And as a company, you can at some point say, great, now we
00:03:42don't need all those employees anymore, or at least we need less of them. Now, turns out this is not
00:03:49working out too well. There is this report about Uber, which got quite popular on X, for example, over the
00:03:57last weeks, where the Uber COO, and I think also their CTO, in the end mentioned that they burned through
00:04:05their entire 2026 AI budget in four months. So they had a budget of tokens they wanted to pay for or use,
00:04:14and they burned through it within a few months. Now, of course, and that's important, I think,
00:04:20one reason is that the budget was likely set in 2025, one would assume, or at the end of 2025 or early
00:04:292026. And then we had agentic coding take off in early 2026. So that happened. And that happened,
00:04:40of course, because certain models like Opus 4.5, but also GPT 5.4, or Codex before that, got really
00:04:50good or got better, especially at following instructions at the end of last year. And then
00:04:55those tools, Cloud Code, Codex, on which I have those courses I mentioned, which are great, the tools and
00:05:00the courses, those also got better and used those models really efficiently. And of course, also other
00:05:05tools like Pi, which is an amazing coding agent, and so on. Now, the combination of that led to more
00:05:12usage of these tools. But since we're talking about agentic coding here, where these tools,
00:05:18or where the models in these tools think and use tools, call tools, do searches, analyze the search
00:05:25results. That all costs way more tokens than the way we or these companies used AI last year, where it was
00:05:35shorter sessions, not so many long running agentic sessions. And of course, the longer a session runs,
00:05:40the more tokens it burns. So the calculation that happened in 2025 has nothing to do with the reality
00:05:47of how AI is being used in 2026 with those enhanced agentic coding models and the tools around them.
00:05:55But nonetheless, Uber burned through its entire budget. Now, if they were getting amazing results,
00:06:02they would surely increase their budget, but doesn't look like that is what happened. An NVIDIA executive
00:06:10also said the cost of compute is far beyond the cost of employees. So it's more expensive right now to
00:06:18use AI than to use humans. Now, of course, you could say doesn't matter if AI is 10 times as productive
00:06:25as a human employee. It's fine if it's 10 times or eight times as expensive, right? Maybe it would even
00:06:31be fine if it were 15 times as expensive because it can get even better, whereas for the human
00:06:39productivity, it can also increase, but probably not as sharply as that of AI.
00:06:45But we're also not near to these numbers, 10x, 15x, because again, the number of lines of code generated is
00:06:54not a good measure. And we need human employees with their experience, with their empathy, with their
00:07:01understanding of a code base, with their connection to other departments and a company, with all those
00:07:08nuances that make up a job. Of course, with all the trust that is assigned to a human. And of course,
00:07:15also with their deep understanding of what makes a good code base, what will likely come next in a
00:07:21code base, which future capabilities may be needed. All things AI models are missing, of course. So it's so
00:07:29stupid from so many different angles to compare productivity from AI models with human productivity.
00:07:36And the first companies are seeing that, I think. Which is why all that token maxing here is coming to
00:07:43an end. You can read about more and more companies like Amazon, Meta and many, many others that are
00:07:48cutting back on their token leaderboards, that are cutting back on their AI budgets or on the token
00:07:54maxing approach here. And I truly hope, I don't know though, I hope that we'll soon enter an era where
00:08:02things will settle down a bit more. AI is here to stay and AI is useful. It's a useful tool.
00:08:09It can make you more productive. It's great for doing extra research. It's great for producing that
00:08:15boilerplate code or also the non boilerplate code. But based on clearly defined specifications with human
00:08:22review, ideally based on some code base that was at least shaped and fine tuned by a human, AI can be
00:08:30really useful there. And it can even be useful for wipe coding if you need a little tool that just does
00:08:38something you need to get done right now, which you don't plan on publishing to the world, where you don't
00:08:43care about all the bugs and where you will not add a lot of features, which you don't have to maintain.
00:08:48It can be great for that too, for these one-off tools. There are many great use cases for AI and
00:08:55it's a technology that's here that will stay and that will become better, of course. And nobody knows
00:09:00what will be the case in 10 years or so. But right now, I really hope things will settle down a bit more
00:09:07and we'll use AI for what it is, a useful tool, but not that magic thing right now that changes
00:09:15everything and we'll get rid of all the jobs and we'll replace all employees and all humans within
00:09:20the next 12 months or so. And it looks like, probably for publicity reasons though, that even our favorite
00:09:28tech CEOs, Sam Altman and especially also Dario Amodei, are kind of retreating regarding those pretty
00:09:36strong statements of how soon AI will replace pretty much all white collar work, right? Sam Altman said
00:09:45in an interview that he was pretty wrong about AI's economic impact. And Anthropic CEO Dario Amodei,
00:09:52who not so long ago mentioned that most or pretty much all of white collar work will be replaced by AI
00:09:59relatively soon, now says automation may actually expand the work people do. Probably though,
00:10:06because their PR department told them that whilst it's amazing for selling their tools to companies
00:10:13when they say how many employees they can replace, it's not so amazing if the entire world turns against
00:10:21them. So I didn't care too much about their statements before and I still don't now that they reverse
00:10:28them, I always was pretty convinced that nowhere near in the future will AI replace all white collar work.
00:10:37I'm sure it will actually lead more to more work. That has been the case with all those technological
00:10:43breakthroughs. And like with all of them, we just don't see how future roles will look like. But when we
00:10:48take a look at coding, we're not even close to the point where you would want to let AI write all the
00:10:56code and not care about it at all for any serious product. At least I definitely wouldn't and I think
00:11:03any company that would, would make grave mistakes. But as it seems, companies also are hopefully starting to
00:11:11realize that AI is better used as a great tool instead of an all-in do-everything solution.
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