By Nicole Jones
ExxonMobil – allegedly – predicted climate change as early as the 1970s. Researchers claim the oil and gas giant ‘predicted how burning fossil fuels would warm the planet’, but publicly downplayed the science of climate change and continued aggressive oil exploration and production in pursuit of profits. Shareholder primacy prevailed over sustainability; plus ça change.
In fairness to Big Oil, they were hardly alone. The story of the 20th century is one of industries driven by a short-term profit impetus using natural resources like oil and coal to fuel unprecedented growth, efficiency, and productivity. Corporations became giants by producing cheap plastic products; oil-powered ships connected the world’s supply chains; progress, progress, progress.
We now know unequivocally that these gains came at the cost of significant environmental degradation and a global climate crisis. A generous view would be: they knew not what they did. A less generous take is that the short-term benefits of resource use outweighed the risk; the cost of environmental degradation was an unfortunate but acceptable trade-off for growth.
Fast forward to 2024, and we haven’t exactly learnt our lesson. But what’s interesting about the current moment is that climate change is no longer just an ethical or ecological issue. It’s a profit problem. Wildfires, floods, droughts, hurricanes: the frequency and severity of climate-related disasters are directly impacting supply chains and global markets, leading to rising operational costs, product shortages, and infrastructure damage.
In agriculture, energy, insurance, real estate – climate-related risks are already costing billions. We’re (mostly) agreed that ignoring environmental degradation in pursuit of value is no longer a viable strategy. You can’t make money on a dead planet.
But this is not a story about oil and gas. This is a story about our minds.
It’s an exciting time to be alive, if you’re in the business of saving time. Generative AI promises extraordinary and immediate productivity gains for business; Goldman Sachs research predicts Gen AI will start having a measurable impact on US GDP as soon as 2027. We can automate tasks, analyse data, accelerate workflows, generate content. We can create extraordinary things.
I have been – and remain – excited by Gen AI. We’re using it within our agency in numerous ways, from scamping to ideation to building knowledge hubs and pulling insights. I used Gen AI repeatedly in my research for this article, and used an AI transcription tool to capture wide-ranging input from my husband.
I have been – and remain – cautious about the already-well-discussed negative impact. Deep fakes, bias, IP issues. (An aside. This isn’t a story about the environmental impact of Gen AI, although the resource consumption and carbon footprint of AI model training, data centres, and ongoing computation are all pressing issues.)
But whatever side of the fence you land on, there’s little sense in trying to fight against the use of it. Technology change inevitably creates challenges. You can rail against the principle of Gen AI, if you care to – but you’ll only be standing on the beach shouting at the tide.
My real fear lurks further, on a distant horizon. That far-off crisis – the gathering storm – is something altogether harder to measure or quantify, and compounded by another complex issue. A crisis not environmental, but cognitive: the slow degradation of our capacity to engage deeply, consider critically, and navigate complexity.
In other words, our ability to think.
In creative circles, much discussion around what we can or should use Gen AI for has already happened. Creatives are rightly quick to jump to defend the sanctity of creative tasks: an AI cannot write a truly great ad, an AI cannot art direct with taste. The already old adage is that Gen AI will enhance and supplement our endeavours, not supplant them. We will continue to take joy and pride in our work. AI will not replace creative minds.
In Good News for said creatives, that’s an easy enough case to make from a profit perspective. While we’ve certainly seen displacement of content writers across the wider sector – particularly for freelancers – the reality within our agency is that clients still want real creative, and real strategists, and real work, even if we’re using AI tools to push boundaries or make it happen.
(As a point of clarity: this discussion centres around Gen AI tools as typically used by the general population at work – not, for example, the extraordinary AI-enabled tools and state-of-the-art deep learning techniques revolutionising medical diagnostics.)
We’re less quick, understandably, to protest the automation of ‘routine tasks’. Automation of manual labour is, after all, the constant evolution of the modern age. Summarising reports, analysing data, pulling together call notes. Filling out a spreadsheet, sorting your emails, repeatedly responding to the same request.
These are, indeed, boring tasks. (I used Chat GPT to give me a comprehensive list of ‘boring work tasks that AI could handle’.) I’d personally rather bash my head into a wall than fill out a spreadsheet in any capacity. And – working in an agency, being chronically busy – I’m all for a good time saver.
And yet: I worry. There’s a reason we have to eat our vegetables rather than scoffing crisps for dinner; sometimes we need to do the opposite of what we prefer, because it benefits us down the road.
I can feel it in myself already. A desire to outsource my thinking, particularly if I’m not all that interested in the thing I’m meant to be thinking about. I’d rather hop in the car to nip to the shops than walk 25 minutes; I’d rather get AI to make a list of topics for me. I don’t worry too much though – re. the walking or the thinking – because I also do pilates five times a week, and am deeply experienced in researching niche topics. But dumping ‘routine tasks’ onto Gen AI is damn tempting.
We’ve been on this ride for a while, obviously. With every iteration of human communication, the way we process information has changed: the internet promotes shallow reading and text skimming, and automation complacency is a real and recognisable phenomenon. Authors like Nicholas Carr have already warned against how the internet is ‘reshaping our thoughts and behaviours, leaving us with dwindling attention spans.’
Where, then, are we headed next? If we stop reading reports altogether and simply ask ChatGPT to bang together the relevant highlights, what happens to our minds? How far can we push ‘cognitive offloading’? More pressingly, what does this mean for the ‘next generation’ of the workforce – those who will never know work without Gen AI – and their ability to serve the world they live in over the next twenty, thirty, forty years?
My job title is Head of Copy. When I train writers, I always start with the same basic instruction: to get better at writing, you have to read everything you can, as often as you can, and you have to try to write every day. Writing is a muscle that grows flabby with disuse.
The same is true of thinking. Our neural pathways are shaped by consistent practice, learning and problem solving; cognitive growth comes from pushing our brains. And herein lies the risk: GenAI is an extraordinary tool in the hands of people who already have well-honed critical skills, but how does this play out for people in their early careers without the ‘hard yards’ behind them?
Take data analysis. Working to extract insight from detailed reports, data and information is crucial to our ability to identify relevant facts and form conclusions. If you want to make a ‘data-driven decision’ (and who doesn’t, these days?) you actually need to be able to understand data; bunging it through ChatGPT and asking for the highlights won’t cut it.
Likewise: gathering information. Conducting in-depth research, evaluating the reliability of sources, and learning to succinctly synthesise findings enables us to make informed, well-rounded decisions, and even anticipate what’s coming next. Or creative problem solving. Brainstorming and design-thinking exercises – essentially, encouraging teams to come up with new ideas – fosters the ability to go beyond obvious and explore alternative solutions.
Strategists have to be able to analyse and distil information. Creatives have to learn how to approach old problems from new angles. The next generation of business leaders need the critical thinking skills to make sound judgments and solve complex problems.
And there has to be some legwork in all of this: some hard-won muscle memory from just… doing things. The tiring, boring, manual way. Perhaps I’m wrong – I hope I’m wrong – but I worry ceaselessly for how people learn to think, in a world where you can outsource the dull stuff wholesale. We’re poised to seriously fail young people, if we don’t help them learn to think.
Sometimes, I like to deep dive the internet for five-minute profiles of people I admire. Particularly creative people, particularly Creative women. Their origin stories nearly always involve a paragraph of praise for the mentors, managers and bright-spark colleagues who got them there. Being next to and surrounded by people doing the job you’d like to do, brilliantly, has always been critical.
Over the last decade, I have been managed by just two people. Both have been fantastic: giving of their time, deeply involved in my progression and growth. I currently manage four people, and I try to do for them what has been done for me. When I feel that I have let them down, it is nearly always a failure to be giving of my time.
Because it’s hard. We’re all under pressure all the time. Many agency folk live in a world where every hour is accounted for and every day of delay matters; it feels increasingly difficult to find the time and attention to dedicate to coaching junior staff, particularly in working models where tacit knowledge transfer has undeniably been lost. Little wonder the time-saving lure of AI is so strong.
But even as cognitive offloading reduces the development of thinking skills, the busyness of our working lives means we spend a diminishingly small share of our time explicitly training the critical skills of the next generation of workers. Thus, early-career people are failed twice over. We spend less time teaching them to think, because we are busy. Then we compound the problem, encouraging them to spend less time learning how to think, because thinking can be outsourced to technology.
Obviously, that’s a problem right now – but it’s probably not such a pressing issue that we’ll collectively be spurred into action immediately. But it will be a problem. The absence of active mentorship not only stunts the career growth of young professionals; it weakens organisational succession planning. Boosting productivity through cognitive offloading not only stops people growing today; it leaves us with a knowledge economy in which we scarcely think at all.
Time saved today snowballs into a business crisis down the road.
It’s easy to disparage youth, or dismiss their importance. (Youth certainly has no qualms about disparaging me – a Gen Z colleague recently told me I shouldn’t learn to drive because ‘old people learning to drive is an ick’. I’m 31.)
But young people are what we have, and we need them, to build businesses worth running. If you’re in your 30s and 40s now, looking to become a senior business leader over the next 20 years, today’s 20-somethings will be the middle management you rely on.
And so the responsibility of keeping an eye on this sits with us. We have to find balance: the desire to save time and boost productivity and deliver for clients, with smart tools, is absolutely understandable. And we have to embrace progress; I proudly work for a tech-focused agency with big chops in AI. (I’ve already written thousands of words elsewhere about the great stuff Gen AI can do.)
But we must also heed the warning bell: what we gain in productivity today may be squandered in twenty years, in a world where we cannot think. We already have research that shows ‘Overreliance on AI dialogue systems can significantly impact decision making, critical and analytical thinking abilities by fostering dependency and potentially diminishing individual judgement skills.’ We can’t be the leaders holding the warning in our hands, chasing every small win today as the storm cloud of the next decade gather.
A board has a fiduciary duty to act in the best interests of its shareholders. Business leaders have a responsibility to the company they lead. Line managers have a responsibility to the people we manage. Harnessing Gen AI successfully can serve those ends, but we must take some sort of lesson from the mistakes of the 20th century; we must not sleepwalk into a knowledge crisis that frays the fabric of how we think forever.
The purpose of thinking about these challenges is not to be an ominous soothsayer. It’s to find solutions – to discuss, to course correct, to build a plan for how the future stays ‘thinky’, and we nurture talent that takes us forward.
So to close, here’s three suggestions – I’ll be challenging myself to live up to them.
Make sure early and mid-career people are working on things that stretch them mentally; discuss the process with them, discuss the value of the hard yards, and encourage them to reflect on their own experiences of how automation is changing their work. Set a clear expectation: thinking is non-negotiable, and there are no shortcuts to real understanding. Give them projects that actually make them think and the time and critique to make their outcomes better. Design tasks that build their ability to problem solve. Allow the space for them to get it wrong.
Plan structured and informal opportunities for juniors to learn directly from senior staff. Sack off hot desking; bring back seating plans and sit next to each other. Go out of your way to create environments where everyone can observe and learn from senior management; bring them into key meetings and insist that they go laptop-down. Explain why you’re doing things, even if you think it’s obvious. Set expectations clearly. Don’t let anyone be ‘too busy’ to find time to do things that aren’t absolutely necessary. Mentor someone. Pick twelve senior leaders, and make them each host a tailored lunch and learn every month of the year.
Embrace AI, and what it offers, and the productivity gains it might bring. Measure those gains. Implement regular cognitive performance assessments to track employees’ problem-solving, analytical thinking, and creative skills over time. (Is Dr Kawashima’s Brain Training still a thing?!) Encourage employees to self-assess their skills and cognitive engagement. Combine self-reporting of performance with frank assessment from managers. Track trends. Acknowledge that we risk losing something – and find ways to measure and guard against that loss. Challenge yourself, every single day, about what sort of world we’re building next.
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