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Last week, I observed, in the words of Taylor Swift, that there is a lot going on right now. So it’s ironic that this week I only managed to amplify two posts of the many that I read on the web.

But I did manage to post two longer articles, and I sat down with around a dozen people in Sydney and Melbourne—people working across enterprise, startups, agencies and more—getting a sense of just what the state of AI and software engineering is right now.

As has been quoted widely in recent days, Tom Dale observed:

I don’t know why this week became the tipping point, but nearly every software engineer I’ve talked to is experiencing some degree of mental health crisis.

It’s not fear necessarily, just the cognitive overload from living in an inflection point.

This resonates with me. It resonates with nearly everyone I’ve met with this week. There seems little doubt that it is becoming more widely apparent we are living in very interesting times. Times of genuine transformation. The consensus of people I know who have been in our industry for decades is that we have never seen anything of this scale or pace in our professional lives. Perhaps in our entire lives.

Predictions that a year ago seemed absurd—or even predictions made a year ago that just months ago might have seemed absurd, like Dario Amodei’s prediction that by the end of 2025, 90% of code would be written by large language models—have come true.

We also struggle to see what is plainly in front of our faces. Anyone tracking the stock prices of even software darlings like Adobe will have noted before last week that their share price has halved in the last two years. And they’re not alone. Despite the evidence being obvious, it was only in the last week or so that the broader market and the punditocracy caught up with the reality that something has transformed in the software industry.

And everyone who genuinely has a sense of what’s happening, also has very little idea of how to respond to it. Certainly not in the specifics. There’s an adage in Hollywood, coined by William Goldman, legendary screenwriter of Butch Cassidy and the Sundance Kid and The Princess Bride, among many others: “Nobody knows anything.” I think that’s an adage well worth taking to heart right now.

So what’s to be done? Experiment and explore. Perhaps a little less talking and a little more doing. A little less scepticism and rush to judgement. Fewer hot takes and more shipping.

The Scarcity Trap

On LinkedIn and social media I frequently see observations like this: “Code generation allows us to go ten times faster in the wrong direction.” It’s clever. It’s also glib and misses the point almost entirely when it comes to the transformation these tools are having on the production of what we traditionally call software.

We’re so used as software engineers and product teams to focusing our energy for days, weeks, or months on a single thing we’re building. We’ve developed techniques like sprints and agile to manage that scarce resource of developer attention.

But that’s an antiquated way of thinking. We no longer need, as teams and individuals, to focus our attention on a single thing for days, weeks, and months. We can focus on multiple things either in parallel or sequentially, so that inside a vastly shorter time frame we can produce much more.

We’re not going ten times faster in the wrong direction. We’re going ten times faster in ten different directions. A hundred different directions.

We can take many more shots on goal. And if you have any understanding what our American friends we call soccer, you’ll know how often teams win is correlated with how often they take shots on goal.

But if you’ve been working in this industry for years, your mindset will almost certainly be that your attention is so scarce it must be very carefully managed and doled out. We err on the side of not heading in the wrong direction. But that’s just the wrong way of seeing it now. The current generation of code-generation models—starting with Opus 4.6 and GPT-5.2, and now 5.3 Codex—are a step function better than what came before. And it’s not just the models. We now recognise that the harnesses we put around those models—OpenAI’s Codex, Claude Code, Google’s AntiGravity, and others—make an extraordinary difference. We’re not simply waiting for new models on the order of months, either. We saw a jump from OpenAI’s Codex 5.2 to 5.3 in a matter of weeks. Opus 4.5 to Opus 4.6 again in a matter of weeks. And the harnesses can improve on a much shorter timeframes still.

The single great challenge for software engineers, for designers, for product people—for the people who have traditionally made digital things with software—is to shift from the scarcity mindset. Away from managing the risk of wasting the precious resource of your attention, toward the instinct to take more shots on goal.

The Spoon Moment

This connects to another mindset shift that I wrote about this week, which occurred to me on a walk with my very good friend Mark Pesce.

Many people have compared these tools to the “I know kung fu” moment from The Matrix—Neo gets plugged in, his eyes flicker, and suddenly he has capabilities he never had before. That’s real. I’ve written semantic search engines in Python, a language I barely know. Many developers will have similar stories.

But I think there’s an even more important lesson from The Matrix. The scene where a child bends a spoon and tells a quizzical Neo: “The secret is…there is no spoon.”

For 70 years or more humans have written code to make machines do extraordinary things. We have hoisted the layers of abstraction far above the bare machinery—from literally patching cables into a plug board, through punch cards, to type-based interfaces and beyond. But at the end of the day, it has been humans who, through code or direct manipulation of hardware, have produced software.

There’s a scarcity mindset coupled with the mental model that humans are required to produce software. Together, these trap us in a worldview that is no longer true.

Humans are not required to produce software.

We can argue about whether humans should be involved, and to what extent. The code that models and their harnesses produce may not meet particular quality thresholds. It may introduce security, performance, and other challenges. But that’s a separate debate from whether the fundamental reality has shifted. Computers produce software now. Including projects like OpenClaw, the fastest growing GitHub project of all time. Including Claude Co-work. And many, many other significant projects.

When you internalise the implications of that—that a single software engineer or designer or product manager can produce multiple pieces of software in very short orders of time, in the order of minutes to hours, and can do so in parallel with software agents running largely autonomously—your mindset shifts. When you develop the instinct to start by going to your agent—to OpenClaw, to Claude Code, to OpenAI’s Codex, to Google’s AntiGravity—and just telling it what you want to do, that is a profound change.

I call it the spoon moment. When you internalise that there is no spoon. When you stop blocking yourself from simply starting a project. When you see abundance rather than scarcity. That’s transformative.

In my conversations with a lot of software engineers, very few people have really internalised this yet. That’s understandable. This is such a profound shift in the production of software.

Now Problems vs. Forever Problems

The second thing I’ve been thinking about for quite an extended period—and which I also wrote about this week—is what I call “now” problems versus “forever” problems.

A lot of the critique of AI-based software generation has been centred around specific shortcomings of a particular moment. Time and again, over three or more years, I’ve seen people critique a specific limitation that models have in that moment. They can’t do this. They make this kind of mistake.

For quite some time, it was a matter of faith or intuition, among people like myself, like Simon Willison, and others who for whatever reason glimpsed the coming capabilities of these models—that a lot of the things we thought were fundamental problems with large language models when it came to code generation were actually just problems of the moment. Problems of models that were not yet good enough, of allied technologies like harnesses that had not yet matured, of techniques we had yet to develop.

So just as the sense that there are spoons has held us back, this sense that problems of the moment were, are, much more fundamental, more pervasive and pernicious, is another kind of anti-pattern. It keeps us from seeing clearly how these technologies work and how we can work with them.

The Hockey Stick

I think of the end of the movie Her, where Samantha the AI becomes increasingly, exponentially capable, and essentially disappears from the human world.

Humans are not good at exponential thinking. We saw that during COVID. Our brains just don’t work by seeing small numbers doubling for an extended period until suddenly something is profoundly different.

What exponential curves look like in practice is a seemingly long, slow, almost linear growth. Maybe it’s cases of an infection. Maybe it’s interest compounding. And then there is a moment—an inflection point—where suddenly a doubling and a doubling and a doubling takes us from relatively small numbers to very big numbers very quickly.

If we haven’t already turned that corner of the hockey stick, if we haven’t already reached that inflection point, it is very, very close. I suspect—and others have observed the same—that it probably happened sometime late last year.

What that means is unless you’re really working to keep up, it is going to become increasingly difficult.

Unseeing the spoon

These are two things I wrote about this week. But the harder part is undoing the years or decades of mental habit that has built up the models we share collectively.

The single biggest challenge software engineers face when it comes to working with these technologies—and this includes people who are already bought in—is getting past these mental models which constrain how we use them.

That’s my challenge to you in this definitely challenging moment. A moment of transformation that none of us have really seen before. There is no playbook for how we respond to this. But it’s certainly not a matter of just riding things out for a few more weeks or months until we reach some sort of steady state, a new normal.

I don’t think we can say, “By May or September, it’ll all be figured out. We’ll have the techniques. We’ll have the new Agile. We’ll have the new Waterfall. We’ll reach a steady state again, and then I can get down to really knuckling down with these technologies.”

Even if that were the case, the people writing the new playbook are the ones trying to work this stuff out right now. People like Geoff Huntley with his Ralph Wiggum technique, Steve Yegge with Gastown.

You can sit on the sideline and hope it all comes out in the wash. But I don’t think that’s a wise approach. Or you can help shape these technologies, help shape their application, bring them within your organisation and help your organisation adapt.

So there’s your challenge. And there’s your opportunity. If my predictions, and you act on them, I don’t think there’s a significant cost. But, If I’m right and you don’t act on what I think is happening right now, the risk is you really will not be able to participate in what comes next. The window will have closed. Everything will have accelerated away from you too quickly.