This may seem an off-topic for someone focused on the pragmatic implementation of Net Zero, but bear with me, it’ll all make sense.
In a recent conversation with Tien Hua we’ve been talking about the need for a massive rethink about energy infrastructure, generation, supply and costings. We all know that cheap and abundant energy is a critical component to prosperity, both in-country in our houses and businesses, but also on the international stage.
If you’ve missed this point that’s reasonable as it’s ‘just there’ so let me explain my mindset; energy is the only reason anything gets done. Whether it’s the chemical energy you burn in your biological systems that keeps you moving or the energy to industrialise, farm at scale, manufacture goods, ship them around the world, run computers, homes, offices, factories, manufacturing or project military power. It all takes energy and the release of greater volumes of energy from denser sources; from wood, to coal, to oil, to gas to nuclear and eventually to fusion – is the steel thread that guides us forward. Take it away and we’re back in the dark whipping horses for power. We’re so used to power around us we forget about it, right until there’s a power cut and our lives stand still for a few hours. We need power and more of it, now (0), in order to remain competitive on the world stage and advance as a global civilisation.
So you’d think the UK in particular, the US to a great degree and across Europe we’d be showing world class delivery of energy sources, generations and delivery? Not so. The current mess with the critical national power generation and infrastructure (1, 2, 3) is a demonstration of the short sighted and near term thinking of policy makers and implementers that is costing this country and many nations dearly. They knew the need was coming, see link 0 above, and did the minimum. But make the connection already…
The connection is that thinking isn’t limited to governments – as part of the AI hype happening now we see companies thinking the same way (short sighted and near term) around staffing, service development and delivery, technology pathways and client engagement. The reason our conversation moved on to AI was the obviously massive amount of energy that data centres delivering AI will need, the ability to scale due to this (and other issues like hardware and space) along with the impact on the IT workforce.
That’s where we come back to the topic of this article and why the ‘short sighted and near term’ thinking comes into play.
Layoffs in the AI frenzy
The news of LinkedIn (MS) laying off staff as they become more AI oriented isn’t the start of a trend, it’s been going on with gusto for at least for the last year (4), hundreds of thousands of jobs have already gone in a sector that was competing for staff just a few years ago. Covid and AI have been a double whammy. Of course it’s only going to get worse in some regards.
Why worse? Companies are too focused on the bottom line – cut costs, improve margins, automate / replace with AI, dismiss competent staff. Improving the bottom line is always wise of course, waste and inefficiency have no place in the modern business. The issue that AI is highlighting is this becomes that absolute focus and it never ends well.
What companies should be doing is reinvesting in those to-be-laid-off staff, retraining them in AI, running skunk works projects to AI the business process and practice, transform, expand, speed up. Growth, efficiency, capability, capacity. Instead AI seems to be an excuse to reinforce the seemingly prevalent do-the-same-with-less attitude. Instead we should be seeing a do-more-with-the-same attitude. However, the focus on bottom line instead of top line doesn’t support this.
This creates an all too frequently unacknowledged stress and uncertainty for staff too. Sure, staff should be owning their development, growth, education, value proposition, and just like everyone had to get on board with using a PC and mastering MS Office twenty years ago, cloud 10 years ago, big data engineering 5 years ago, etc. people need to do that same with AI (which reminds me, go and get the AI Pulse Newsletter). In this situation learning about AI is a defensive move by staff, upskill to save your job but we know it likely won’t work. We all understand that management will take management decisions and they rarely go past bottom line profitability, that’s a management level defensive move. For staff, the rewards of this defensive upskilling come later, very often in the next role.
So where’s the failure?
Bottom v top is one as mentioned. The main failure by the companies is in failing to engage competent staff and simply cutting them as a knee jerk reaction to either a) a potential future impact of slowed growth or (and?) b) the currently undefined hope AI will resolve perceived challenges. The fact is though companies have been struggling in the post Covid recovery. Covid was a boon for IT projects, homeworking cutting both OpEx and CapEx costs and improving staff retention. Post all this we know of IT consultancies in particular that have had an increasing bench, struggled to land new clients, slowed or stopped recruitment and yes, laid off staff by the thousands. AI is seen as the next best thing to help here. AI will save us, get rid of the staff, address the bottom line, do the same with less. The problem again is if it’s b) above, read back up two paragraphs for what companies should be doing to help realise the hope of AI.
It was the same back in my early career in manufacturing. China made things tough, so instead of pivoting, innovating, taking fresh new approaches that leveraged existing networks of staff and their skills it was cut cut cut. Cut staff numbers (but now we have no capacity to deliver) and bring in lights-out automation (except this is costly and competent staff have now gone). Cut design staff as we’re not selling as many products (and now we won’t as we have no new product pipeline). Close premises (reduce capacity and resilience) and compress the shop floor footprint (limiting growth). Offshore (loss of control), outsource (reduced quality, complex logistics, longer lead times). Same pattern, different industry. China was the AI of manufacturing. Guess what, manufacturing is practically dead in the UK and it’s down to both energy costs and that short sighted, near term, bottom line again. But… operational efficiency, bottom line, increased margins… of course these are critical and should always be sought but the long term needs safeguarding.
What happened at the consumer electronics factory where I was the Process and Quality Engineer? It got bought out and shut down. IT companies better hope that AI capability flourishes and the energy costs layered into service costs don’t stack up too much.
Anyway, thanks for reading, share your thoughts. Back to articles that are more on point!
Mark.
