I’ve spent more than 20 years building software, but my relationship with computers goes back much further. I still remember the excitement of playing on an Atari and a Commodore 64, when a few pixels moving across a screen could keep us occupied for hours. Later came the family PC, with its chunky monitor and x86 processor, and the fascination of discovering what this machine sitting on our desk could actually do. Computers felt like magical portals back then, but they were also limited.
It’s remarkable to think about how far we’ve travelled since then. Software became better at processing information, connecting systems and automating repetitive work. The internet put an extraordinary amount of knowledge within reach too and it has been fascinating watching the definition of ‘possible’ keep changing.
Today, artificial intelligence (AI) can write code, analyse enormous amounts of data, generate full-fledged decks, be a multi agent team, recognise patterns and make decisions with a speed that would have seemed astonishing not too long ago. While the pace is exhilarating, it’s also worth pausing for a moment.
There is a question we don’t ask often enough when we talk about what AI can do: then what? Much of the conversation around AI is understandably focused on productivity. How much time can we save? How many people can we serve? How quickly can we build something? Which tasks can we ‘automate?’ These are useful questions no doubt, but they’re not the only ones that matter.
If machines can increasingly perform the work we once considered uniquely human, our challenge will not simply be learning how to use increasingly capable tools. It will be deciding what we want those tools to help us accomplish…that distinction matters a great deal.
Companies that thrive in the next chapter of technology may not simply be those that adopt AI first, they may be the ones that understand where it genuinely helps, where human judgement remains essential and what larger purpose sits behind the technology they are building.
I’ve worked with software long enough to know that a system is only as useful as the problem it is designed to solve. We can build remarkably sophisticated technology around a poorly defined objective and still end up solving the wrong problem extremely efficiently. AI makes that possibility much more significant.
You see, as systems become capable of taking on larger parts of a workflow, someone (a human being) still needs to understand what the system is doing. Someone needs to question its assumptions, examine its decisions and recognise when an apparently sensible answer has consequences that aren’t sensible at all. And let’s not get started on AI’s increasing propensity to go rogue, but that’s a topic for another day.
Responsibility cannot be automated. This is particularly important because AI systems don’t exist in isolation. They operate within businesses, schools, hospitals, governments and communities. A decision made by a system can affect whether someone gets a loan, how a candidate is assessed for a job, whether a doctor’s diagnosis is correct, what information a child encounters online or how a customer is treated.
The technical question may be whether the system can make those pivotal decisions. The human question is whether it should. That is where I think the conversation around AI needs to become more mature.
We spend enormous amounts of time asking whether something is technically possible, but we should spend at least some of that time asking whether it’s fair, necessary and worth doing. This doesn’t mean resisting technology. Quite the contrary.
Look, I’m optimistic about what AI can make possible…its potential is far beyond what we can grasp even now. But potential is not the same thing as progress that depends on what we choose to do with that potential.
One of the more interesting consequences of AI may therefore be that, as execution becomes cheaper and faster, deciding what deserves to be executed becomes more important. If a team can turn an idea into a prototype in days rather than months, the competitive advantage has shifted away from simply having the resources to build something. It lies in identifying the right problem, understanding the people affected by it and asking better questions before the building begins. That is a very human responsibility. It also changes what we should be teaching the next generation.
Technical skills will remain important. But curiosity, judgement, empathy, critical thinking and the ability to understand consequences may become just as valuable. We will need people who can work with intelligent systems without surrendering their own intelligence to them. Perhaps that is the paradox of this technological moment.
The more capable our machines become, the more important it may be for us to understand what makes our contribution different, not because humans will always be faster…we won’t. But because we’re the ones who experience the consequences of the systems we build. We care about outcomes. We attach meaning to them. We live with the social, economic and human effects of our decisions.
Science fiction has spent decades imagining machines that can think, create and act. We’re now beginning to build versions of those very machines in our everyday lives. That is an extraordinary achievement.
The companies that thrive in the next chapter of technology may not simply be those that adopt AI first, they may be the ones that understand where it genuinely helps, where human judgement remains essential and what larger purpose sits behind the technology they are building. AI can, and is, accelerating the future, but it’s us who gets to decide where it takes us.
The article does not necessarily reflect the opinion of Business Recorder or its owners.