This is a personal reflection, not a forecast dressed up as one. I am writing about a trend I think is real, but the second-order consequences are guesses, and I am sure some of them are wrong. The single most important economic fact about AI is not that the models are getting smarter. It is that intelligence is getting cheap. For all of human history, thinking has been expensive. Doctors, lawyers, engineers, accountants, researchers, designers, programmers, analysts - the entire knowledge economy was built on the premise that competent cognition is a scarce resource you have to pay for. Universities exist to credential it. Firms exist to ration it. Salaries exist to compensate it. Whole cities exist because the cognitive workers cluster in them. Strip away the abundance of competent thinking and the post-industrial world stops making sense. ...

The views in this post are my own personal reflections on the industry as a whole, written in my own time. They are not about any specific employer, team, or colleague, past or present. Almost every confident take on the future of software engineering assumes a particular kind of person at the centre of it. A senior. Someone who can read a generated diff and feel which line is wrong before they can articulate why. Someone with taste, judgement, and a working theory of the system in their head. Someone who can curate, review, and steer fleets of agents. ...

Most self-improvement advice assumes a step that almost nobody actually completes. It assumes you have looked at yourself clearly. It assumes you know, with reasonable accuracy, what you are good at, what you avoid, where your time really goes, what you actually want, and what you keep telling yourself to avoid the discomfort of changing. In practice, that step is the bottleneck. You can read every book, follow every system, build every habit tracker, and still go in circles for years if the underlying picture you hold of yourself is slightly off. Goals that match a fictional version of you cannot be reached by the real one. ...

The word “hallucination” is one of the most successful pieces of accidental marketing in our industry. It is a soft, almost endearing way to describe an LLM stating with full confidence that a function exists when it does not, that a court case was decided when it was not, that a paper was written by an author who has never published in that field. It makes the failure sound like a quirk rather than the central reliability problem of the entire technology. ...

OpenAI released GPT-5.5 on April 23, 2026, weeks after GPT-5.4 and only months after GPT-5. The cadence is starting to feel relentless. Codenamed “Spud” internally, GPT-5.5 is the first fully retrained base model since GPT-4.5 - architecture, pretraining corpus, and agent-oriented objectives all reworked from scratch. The question worth asking is whether any of this is actually significant, or if we’ve reached the part of the curve where every new release looks like a small step. ...

This is another “thinking out loud” post, in the same spirit as the agent-first architecture piece. I do not know how any of this is going to land. I am writing it partly because the question has been rattling around in my head for months, and partly because I suspect a lot of people in and around software are quietly wondering the same thing without quite wanting to say it out loud. ...

This is a “thinking out loud” post, not a report from the front lines. I have no evidence any of this is happening at scale, and it is not how my current day job looks. These are just ideas I keep turning over, and I wanted to write them down to see if they hold together. The question I keep coming back to is simple. If AI agents continue to improve at the rate they seem to be, what does engineering work look like five or ten years from now? Not tomorrow. Not next quarter. Further out, where the shape of the job might actually be different. ...

For the last three years, $20 a month has been the magic number. Claude Pro, ChatGPT Plus, Gemini Advanced, Copilot Pro, Cursor Pro - all twenty dollars, all clearly priced to anchor against Netflix rather than against enterprise software. That anchor is cracking. The labs are burning cash on inference for power users, the frontier models cost more per token than they did a year ago, and agent tools like Claude Code and Codex are consuming ten to a hundred times the compute a chat session does. Something has to give. ...

Meta has started installing tracking software on the work laptops of its US-based employees. It captures keystrokes, mouse movements, clicks, and occasional screenshots. The captured activity is fed back into training data for AI agents. There is no opt-out. The program was disclosed to staff in an internal memo in April 2026, and the response from inside the company has been about what you would expect. The program is called the Model Capability Initiative, and it sits under a broader effort branded as the Agent Transformation Accelerator within Meta’s Superintelligence Labs, the AI group now led by former Scale AI CEO Alexandr Wang. ...

A year ago, OpenAI’s image generation went viral for Studio Ghibli portraits. That was GPT Image 1 - impressive, playful, and fundamentally still a party trick. ChatGPT Images 2.0, released on April 22nd 2026, is a different thing entirely. It’s the version that starts to look genuinely useful. It Thinks Before It Draws The headline feature is reasoning. Images 2.0 doesn’t just accept a prompt and immediately generate - it can “think” first, working through the requirements before producing anything. For paid subscribers on Plus, Pro, Business, or Enterprise, this thinking mode is fully unlocked. For everyone else, the baseline quality improvements are still there. ...