How Good Are We at Predicting the Future?

I would suggest we are terrible at it. And that should give us pause when it comes to AI.

The history of technology prediction is a history of confident wrongness. In 1943, Thomas Watson, chairman of IBM, reportedly said the world market for computers would stretch to “maybe five.” In 1995, the astronomer Clifford Stoll wrote a Newsweek piece dismissing the internet as overhyped, arguing that online databases would never replace newspapers and that e-commerce was a fantasy. Even Bill Gates, no technological slouch, admitted he overestimated what technology could do in two years and underestimated what it could do in ten.

The pattern is remarkably consistent. We either wildly overestimate the short-term impact of new technologies (remember when blockchain was going to replace every institution within five years?) or we catastrophically underestimate their long-term consequences. Nobody building the early internet imagined it would reshape elections, create trillion-dollar companies, or leave millions of people psychologically dependent on dopamine hits from their phones.

It isn’t just that we fail to predict which technologies will emerge; we fail spectacularly at predicting the second- and third-order effects of the technologies we already have. Social media was supposed to connect the world. It did. It also polarised it. The smartphone was a communication device. It became an anxiety machine for an entire generation of teenagers. Nobody planned these outcomes. They emerged.

Now consider AI. We are in the grip of what might be the most transformative technology since electricity, and we are making predictions about it with the same confidence and likely the same accuracy as every generation before us. The optimists tell us AI will cure diseases, solve climate change, and unleash unprecedented productivity. The pessimists warn of mass unemployment, surveillance states, and existential risk. Both camps speak with remarkable certainty about a technology whose capabilities are shifting month by month.

The truth is that we do not know. We do not know because the most significant impacts of AI will almost certainly be the ones nobody is currently talking about. Just as the most profound consequence of the printing press wasn’t better books but the Reformation, the most profound consequence of AI probably isn’t anything on today’s conference agendas.

Perhaps we can agree on this: the speed of change is different this time. Previous technological revolutions gave societies time to adapt, however imperfectly. AI is compressing that timeline dramatically. The gap between invention and widespread disruption is shrinking, which means our poor track record of prediction becomes even more dangerous, because we have less time to course-correct when we get things wrong.

So where does that leave us? Not with better predictions, but perhaps with a need for better humility. The wisest response to AI may not be to forecast its future with false precision, but to build adaptability and agility in our institutions, our education, and ourselves. Because the one thing we can predict with confidence is that we’ll get it wrong.

They always help….

A walk; good music; pencil and paper and just write; a good book; a conversation with your dog; a long bath; weeding the garden.

The Compact Guides.

Long-time followers of my (mostly) daily writings will know that way back I wrote several business and personal development books. I then took a long pause to get my novel writing started. Late last year I returned to non-fiction with my Companion Series. The intention is that these are short, very easy to read and 100% practical and instantly available worldwide. The first four are out and the series will continue this year.

The first four:

How to Beat ChatGPT or How to Not Say AI Took My Job.

MEDS: meditation, exercise, diet and sleep; a powerful daily strategy for wellness.

The Tools of Excellence. 70 Devices, Concepts or Strategies for Brilliance.

Do Less yet Achieve More: the 80/20 strategy that transforms productivity.

From Liverpool Cellar to London Rooftop

The Beatles’ first performance under that name took place on 9 February 1961 at The Cavern Club in Liverpool.

While a setlist from the lunchtime show is not reliably documented, the song most widely cited as opening their early Cavern-era performances is “Some Other Guy,” a rhythm & blues number originally recorded by Richie Barrett. It became a staple of their early live sets in 1961–62.

The Beatles’ final live appearance as a group was the famous Rooftop Concert on 30 January 1969, atop Apple Corps headquarters in London. The final song they ever performed live in public was: “Get Back” which they played twice. After finishing the final take, John Lennon closed with his famous line: “I’d like to say thank you on behalf of the group and ourselves, and I hope we passed the audition.”

Parkinson’s Law

The name of C. Northcote Parkinson is attached to one of the most quoted observations in modern organisational life:

Work expands so as to fill the time available for its completion.

First published in The Economist in 1955 and later developed in Parkinson’s Law, the insight was presented with mock-scientific seriousness and dry humour. Yet beneath the satire lay a sharp diagnosis of institutional behaviour: give a task a week and it will take a week; give it a day, and it may well be finished in hours. The work does not necessarily grow because it must; it grows because humans, and especially organisations, expand to occupy the space available.

Parkinson’s deeper target was bureaucracy. Drawing on his expertise as a naval historian of empire, he observed that administrative bodies tend to grow regardless of the amount of real work to be done. Officials create subordinates, not rivals. Committees multiply. PowerPoints (he said documents, of course) circulate. In one of his most famous examples, he noted that the British Admiralty increased in size even as the number of ships declined. The machinery expands even as the mission contracts.

From this flowed his related principles. One is the Law of Triviality, often known as “bikeshedding.” A committee tasked with approving plans for a nuclear reactor may spend mere minutes on the complex engineering because few feel qualified to comment, but debate at great length the cost of a staff bike shed. (These of course were the days when many staff would arrive by bike!). Trivial matters attract disproportionate attention precisely because they are accessible. The small crowds out the significant.

Another observation, sometimes called the Law of Delay, suggests that postponement is often a disguised form of refusal. To delay a decision is to hope the issue will dissolve or be resolved elsewhere. In organisational life, delay becomes both shield and strategy.

Taken together, these laws describe systems that inflate, distract, and defer. They are less about time management than about structural self-preservation. Institutions, left unchecked, grow for their own sake. Meetings lengthen. Processes multiply. Energy shifts from purpose to maintenance.

It’s Monday. You have been warned!

Correlation and Causation

Do you too become a little frustrated at the increasing list of foods and activities which are good/bad for us? And if you delve a little, you discover tiny sample sizes, poorly constructed trials and a simple confusion between correlation and causation?

The Classic Warning

A strong correlation does not prove causation. Otherwise, we’d conclude that carrying lighters causes lung cancer.

The Exam Question

A study shows that students who revise more get higher grades. Does revision cause good grades?

Answer: Probably. But the exam board would like you to say: “There may be other variables involved.”