At a Glance
AI has reignited a familiar moment, like the early days of the internet, where the ability to build has accelerated dramatically. But while building is now faster and cheaper, the real challenge hasn’t changed: deciding what to build and why. This blog explores how product management risks slipping into more output instead of better decisions, as organisations continue to reward delivery over judgement. As customers gain the ability to build for themselves and AI shapes behaviour at scale, the responsibility of product managers becomes more critical. The role must shift toward clearer thinking, stronger decision-making, and greater accountability for the outcomes we put into the world.
I keep thinking about what it felt like when the internet arrived in Australia. Everyone was excited that they could finally build something customers could interact with directly. Off the back of that, we built a huge number of products and services. At the start of 1995, there were a couple of hundred Australian websites. Six months later, there were thousands. By the end of the decade, hundreds of thousands. There was a rush to build, a sense of possibility, and a feeling that we were suddenly closer to our customers than ever before.
That is what this moment feels like now. Around 41 percent of Australian SMEs say they are adopting AI, and about half of all Australians have used generative AI in the past year. The curve is back.
But two things are different this time.
The first is that the tools we have now give us far more control over how we work. We can build much faster than we ever could before.
The second is harder to sit with. In some cases, our customers can now build the thing they need themselves. They are not always competitors, but the gap between what we can do and what they can do is smaller than it has ever been.
Which brings me to what product people are supposed to do in this moment.
In theory, this should be our moment. If building is cheaper and faster, then the value of deciding what to build should go up. The role should shift upstream. Less time in delivery, more time shaping direction, finding opportunities, and making calls about what is worth pursuing.
That is the hopeful version.
The more honest version is that we are not really doing that.
We have always said that understanding the customer is the most important part of the job. We have always said that identifying the right problems matters more than shipping solutions. But in practice, we have often been pulled into delivery because that is what organisations reward.
AI has not changed that. If anything, it has made it more obvious.
When building gets cheaper, the temptation to build more increases. When velocity increases, output becomes even easier to measure. So instead of moving up into the harder work of judgment and trade-offs, we often move sideways into building because it is finally fast enough to feel satisfying.
It is not just a behavioural problem. It is a structural one.
Most product people are still working in environments where strategy is unclear, incentives favour output, and decision-making sits above them. In those conditions, moving faster does not help. It just means we commit to the wrong things sooner and have less time to realise it before the next decision arrives.
So the risk is not just that we build more. It is that we ship more of the wrong things, faster.
Some of those things will quietly fail. Some will create ongoing costs that no one planned for. Some will damage trust in ways that are hard to reverse. The cost of putting something into the world does not disappear just because the cost of building it goes down.
There is another cost that matters even more.
Product people have always studied behaviour, but we also shape it. Most of the time, shaping has been at the edges of people’s lives. What they buy, what they watch, how they get around.
AI is different. It is shaping how people think, how they write, how they make decisions, how they search for information. In a very short period of time, these tools have started to influence the cognitive habits of millions of people.
That shift has happened quickly, and largely without much conversation about whether we want it.
Which raises a question that is not really about AI.
It is about responsibility.
Product people are not the only ones who shape outcomes. Executives, regulators, and organisations all play a role. But we are often the last point before something goes into the world. We make the calls about what gets built and how it shows up.
So if we are not thinking about the impact of what we ship, it is not clear who is.
There are ways to be both commercially responsible and ethically grounded. But that requires trade-offs, and most organisations are not set up to make those trade-offs easily.
That tension has always been there. AI just makes it harder to ignore.
None of this is new. That is the uncomfortable part.
We have known for years that understanding the problem matters more than shipping the solution. We have known that our job is not just to observe behaviour but to shape it. We have known that speed without direction is not progress.
AI has not changed those fundamentals. It has just increased the consequences of getting them wrong.
So I keep coming back to the same questions I always have.
- Who is this product for?
- What problem does it solve?
- What benefit does the customer receive?
- What benefit does the organisation receive by putting it into the market?
Those questions have not changed.
What has changed is how quickly we can act on the answers, and how visible the consequences are when we get them wrong.
The role has the chance to become what it should have been all along.
The question is whether the systems we work in, and the choices we make inside them, will actually let that happen.
