I think most reasonable people understand that politicians are fallible and can be shortsighted and make mistakes. This is part of being human and part of life in government which, at it's core, is just a huge risk management agency. That's not to say that honest mistakes don't carry weight - Bush Jr may have truly believed everything he claimed about Iraq prior to invading it, but it was still a fool's errand, built on a scaffold of lies, and one of the biggest wastes of resources and lives in US history. Bush should be excoriated for that mistake, even if it was honest.

The problem is when intent and motive undermine the attempt at honest governance. If a politician has ulterior motives, such as personal enrichment, or to gain political advantage over an opposing person or party, to save face in the light of a mistake, or any number of other human foibles and failings, that's when it becomes a valid point to make.

For the helium reserve, it seems to have been a victim of shortsighted attempts to reduce the deficit. The program had accumulated almost $2 billion to buy and store it, and some people thought it wasn't needed.

When it comes to Trump, many people don't trust his motives because he's shown repeatedly to do and say things that are dishonest or hide the true intent. It seems fair to ask who stands to benefit from this change in helium classification.

Well said. This is a very succinct way of describing the secondary effects for everyone around someone using AI to feel "more productive". Sending people massive emails you didn't even bother to read, creating huge design documents that are just dozens of meaningless pages and offer nothing of tangible value, or asking fellow developers to reviews 50,000 line code changes you didn't write (or read), etc.

I use AI at work for development and I know that the frontier models in a harness like Claude Code are more capable than many AI critics claim but I also see the results they create in a skeptical light. For a prediction chatbot they are surprisingly good at writing software, but also surprisingly bad compared to a decently skilled human. They can generate massive amounts of design documents and code in a very short time, and easily overwhelm humans trying to read, review, verify, and test the changes. The same thing is happening in other kinds work as well.

Drug use is an apt metaphor. The Dr Feelgood of this decade.

We're running a business here. We need real software, not toys created as a fun weekend experiment with a chatbot or burning money to run an agent and get 50% (or more likely 5%) of the way there.

The real world is more complicated than that. For example, we still have a Windows 7 machine in my office. It has some very expensive but still very useful software locked to it that has no direct replacement available on either Linux or Windows 11. Obviously we have to be careful about security and controlling access to a legacy OS like that.

Blunt statements about "forcing an upgrade" are worthless in this kind of situation. Chances are anyone who is still using a system like that has a good reason for it, at least if it's in use professionally. We buy these assets because they solve problems. Those problems might or might not have a useful and cost-effective solution available on a different platform. If there is no modern solution available and the original still works, your statement that "the old ways are obsolete" is by definition false.

What you do need to do is rationally assess the risks and the threats you face and provide other ways of mitigating the risks and protecting against the threats if you can't rely on, for example, a legacy OS that no longer gets security updates to be secure by itself. But then ideally you shouldn't be relying on any single device or piece of software being secure as your only line of defence anyway, and assuming you already have some degree of defence in depth, accommodating unusual requirements is more a redistribution of security responsibilities than a fundamental change of approach.

Yes, it does look like we broadly agree here.

I suppose the main thing I'd have said very differently is the terminology, in that I wouldn't regard the kind of incompetent senior as a senior in the first place. But then I'm pretty old school about this: to me, a junior developer needs quite close supervision to produce useful results, a regular/mid developer can perform routine tasks with minimal supervision but might need more help with larger or more complex tasks, while a senior is someone you can give more or less any task on your project to and they'll figure it out, and senior is also the level of someone who would be capable of supervising and training a junior.

We've had a wave of people, particularly since the tech boom during COVID, who have obviously been over-promoted due to the employee's market and title inflation. They might now have titles like "senior" or even "lead" or "staff", but they don't have the skills and experience to operate successfully at those levels. I agree the acceleration of everything due to increasing AI use tends to show these people up and I agree that a good mid-level developer using AI might well outperform them. But then I suspect in reality a good mid-level developer was probably outperforming them before AI as well, it just wasn't so obvious.

That is wildly optimistic IME. Frontier models do a great job of sounding authoritative on any given subject. This is not the same as actually having expert-level knowledge and understanding, and the choices the models make when you ask them to implement something can and often do reflect that. There are several domains I've worked in over the past few years where AIs lack even basic knowledge an expert practitioner would have, probably because these fields are too specialised to have a lot of books available or papers published, so presumably there hasn't been much subject-specific content to train the models on.

I don't think it's usually about what AI "did really badly on" at this point. It's more about whether the overheads of adding all the guard rails and then reviewing and correcting all the output end up costing at least a similar amount to just writing the code yourself.

For a junior developer, I'd say frontier models passed their standard some time ago. Today's LLMs routinely write code at the level of a competent junior-mid but with the advantage of an inhumanly broad knowledge base.

For a good senior, I'm much less convinced. The frontier models are hit-and-miss when it comes to writing good test suites, which matters a lot when they rely on tests so heavily to reach acceptable quality. IME, they are still mostly miss when it comes to the business insight that seniors will take into account and design/architecture decisions that keep the code manageable as it evolves. While they have become much better at recognising existing patterns in the code, and I would say they follow them reasonably well the majority of the time now, there are still bizarre anomalies that creep in, and they aren't very good at extending those patterns when necessary.

I think AI advocates also tend to underestimate just how much time it takes to properly review what they are doing. Take a half-day Claude session to implement some new feature. It might take half an hour to give it the initial brief and then review its plan. Make a few edits and it'll propose a new plan that is also long and detailed and has no edit markings to show what it changed so you have to review the whole thing, carefully and in detail, again to pick up the minor changes it also made for no obvious reason. Then you let it start doing its thing. You can't really leave it unattended for long because it'll stop to ask questions. Some of those questions might be whether it can run a screenful of ad-hoc Bash script to make some edits to your source files, because for some reason it decided not to show the actual edits as it usually would, so now you have to figure out what all the sed incantations are actually going to do while looking at those files in another editor or just trust it and then review all the changes properly later anyway. Sometimes it decides to change its plan mid-flight, but not always in a good way, and if you interrupt it to apply corrections or redirect, you break the neat plan flow it was in. Then you get to the end and you can finally see how everything it has done fits together, and you still need to review everything with the same care as you would with a junior developer's code and probably apply a few fixes where it's come up with weird names or not quite followed a pattern or imagined some instructions that were never there and made other changes in other parts of the system that should be out of scope.

If you read to the end of that last paragraph, you know how I typically feel at the end of a half day working with an agentic UI. And the thing is, I do those sessions as experiments fairly often to keep up with progress, but a lot of what I do is still written by hand, which gives me simple, objective data to compare the two ways of working and see whether the AI tools are really making me more productive once you take everything into account.

Currently, based on objective data comparing similar fully completed development tasks like for like, I still outperform agentic workflows using frontier models by about 25-50% on average, and I don't have to pay anyone extra fees to do that, and subjectively I also find it more engaging to work that way. So I don't really buy the hype about AIs making us all orders of magnitude more productive unless you're happy to YOLO everything forever and take the much-discussed risks that comes with.

Personally, I think the optimum use of LLMs right now is probably not to use them for the whole coding workflow like that or rely on agentic tools, but to use them for tasks like answering quick questions about the existing code, doing research like a fancy search engine, writing a first draft of some boilerplate, and writing quick and dirty throwaway code for experiments, one-shot tools and the like, where you don't really care about the quality as long as it gets the job done and you will see immediately if it has in fact got the job done. For these things, the LLM can read and type faster than me, and that's useful.

Fake news. We have such a HIGH IQ president (he has taken and aced FOUR dementia tests!) that nobody is smarter than he is, so there's nothing to be afraid of.

Please go read the definition of "enshittification". It doesn't mean "anything I don't like."

The group that designs Firefox has made some very questionable decisions over the years, but enshittification of the browser is definitely not on the list.

It was probably the other O'Neil, with one L. He has no sense of humour at all.

I could understand bots and mailing lists. The thing I never understood with LinkedIn was how many real people (usually recruitment agents) had apparently spent the time to read my profile and send me a personalised message, yet still thought I'd be interested in a job paying half the market rate for someone with my skills and experience.

I don't know what you mean. Look at the improvement in hiring when you can advertise an open position on LinkedIn and get maybe 100x as many applicants as before! OK, probably 99% of them will be completely unqualified for the role and barely capable of pushing the instant application button, but how is that a problem, really? Surely hiring managers can easily filter them out and no qualified applicant will ever get lost in the noise?

It's like a video I saw on YouTube the other week. Someone was talking all about "skills" for their AI agents and how they'd automated so much of their workflow using them. They started the video by kicking off four different tasks for separate agents to show how productive it was to have all this stuff happening in parallel and how it was so quick and easy to organise.

One of the tasks involved producing an infographic about a certain topic, and the AI duly produced one based on a one-liner prompt, taking only a few seconds of the presenter's time and with no further input from them about the content. The infographic had exactly the level of quality you'd expect from this: a load of boxes and arrows (with some of those arrows starting or finishing floating in space without connecting anything) and some words (a few of which might have been related to the topic mentioned in the prompt).

That infographic was the only substantial output from the whole exercise. It was the worst kind of AI slop imaginable, generated with literally no thought or oversight from the presenter beyond a couple of words about the topic as part of that one-liner prompt. This was probably about the average level of the LinkedIn posts I used to see before I left as well. As you said, I'm not sure you can be a "thought leader" without doing any thinking.

I stopped using LinkedIn when they seemingly decided my account wasn't about me (despite having obvious and publicly verifiable details right there in the description) and wanted me to verify my ID using one of the US-based services with a questionable reputation to get my access back. That's not going to happen, so I just don't use the site any longer.

As far as I can tell, that has made zero difference to my career, and it's one fewer drain on my time. The only thing I have missed is seeing the occasional good news story where a past colleague has been promoted or the like, but I'm not giving up my government-issued credentials to the US-based data harvesting industry for that.

I'm about as far from an LLM booster as possible, and hate to be that guy, but "you're holding it wrong".

ChatGPT shouldn't be used for real software development. It's a toy chatbot. If you're doing development you should be running a harness like Claude Code or OpenCode or whatever locally on your development machine. That gives the model and tools full access to your source, including private repositories, libraries, databases, documentation, bug tracker, logs, etc. These tools are also much, much better at seeing the connections between code and concepts and will do a better job understanding and writing stuff "end to end".

We've reached a point where the quality of the LLM harness is probably equally as important to the LLM itself at writing software, so if you're still using web-based stuff like ChatGPT and pasting single blocks of code or files into it, you're about 2 years behind where most other people are and it's not surprising you don't see any value from it.

We are nowhere near eliminating humans from development, but the current set of harnesses and models actually do a pretty good job when used properly and in the hands of someone who already understands the codebase and domain. They can still fuck up royally, but, well, so can humans.