Future Of Technology Jobs

As always – these are strictly my personal thoughts and not that of my employer.

Over the last few months, the most frequent question I get from my friends and family is about the future of tech jobs – given I have been in this industry for a while. This past weekend- I spoke with a friend who lost his engineering VP job at a big tech company and then with his son whose job offer has now been delayed twice as an entry level engineer.

The first question I get asked is whether people are let go from tech companies because AI can do their jobs better.

I think that’s part of the answer – when it comes to HR, Operations etc. AI and other tech are mature enough today to take over a lot of functions that have historically needed humans to execute. The fact that AI can do something doesn’t mean that you can switch it on and fire people that afternoon – it still needs a lot of change management and all change is hard. But in the short to medium term – I do expect a lot of functional tasks to be done by machines. I also think that many companies will rush into this without proper planning and get it wrong and pay a price.

For engineering jobs – where someone has to actually code, AI today at best is a job aid and not a replacement. But it is an excellent job aid – which means engineering managers might not need as many developers to get the job done, or they maybe able to get more done with current capacity. The reality is that a lot of engineering teams have excess capacity hidden in plain sight – people doing manual testing and code review instead of having better CI , people hand coding deployment pipes and so on. It doesn’t even need AI to find productivity – but AI will just make a strong case to look at developer productivity at all levels.

The next at risk role is managers who are largely serving an aggregation function with no hands on skills – engineering managers who cannot code at all, sales managers who only look at CRM and don’t make client calls , Ops managers who have no skills in optimization and so on. AI is excellent at summarization – and we already have other tech that can aggregate numbers and make comparisons and so on. All organizations have inertia when it comes to attacking structure – so it might take time, but there will be no place to hide soon for people who don’t have higher order skills to either make more money for their employer or save costs.

Why is this happening now?

That’s just the nature of competition and business cycles in a capitalist system. Resource allocation happens where companies expect the most return. This doesn’t only happen in down turns – look at the massive hiring and wage inflation that happened during the COVID years where the belief was that exponential growth will continue. When that growth stopped real fast – cost simply didn’t keep up with revenue. At high interest rates – revenue going up was always going to be difficult and hence the logical option was to cut costs to improve efficiency.

It also just happened that AI just got some new wind on its sails at the same time – so the risk perception probably is lower now about letting go of employees.

Just as companies hired way more people than they needed in the boom cycle – there is a good chance they will over correct in the bust cycle. I expect this to reverse as soon as fed cuts interest rates but probably we won’t see the levels/speed of hiring we saw in the past given AI is also progressing rather fast along the way.

Sales jobs might be the one exception to this. Example : When Elon Musk fired a lot of people at twitter/X – engineering seems to have found an equilibrium, but they still have revenue issues.

AI and its compute problem

One reason for some tech companies for finding more investment is the initial cost of AI compute. Training AI is extremely expensive and time consuming. The current approach with deep learning ( transformers included ) is to teach a system with the collective knowledge of all humans – and when that runs out, to augment with synthetic data. That’s not how any one human being learns – no child reads all of Wikipedia for example. The GPU, the data centers, the lack of top talent – all of this adds to the cost very fast.

This causes two things

1. Companies will need to find money quickly to fund these expensive R&D initiatives to remain competitive

2. Smaller companies will have to stay a couple of steps behind the big ones in some cases given the high cost.

Open source AI solves some of the problems but what we need is for AI to learn and think in less expensive ways. I am hoping that the research community comes up with viable alternate approaches quickly

So what about the people whose jobs get affected?

1. The skill we need the most to stay ahead of the tech onslaught is to learn and unlearn really fast. There is no saying what is the skill we will need in 5 years – which means we need to be willing to always be learning new things.

2. The “real” top talent will always be in hot demand in all markets. For the rest – we need to have an alternate strategy on top of the constant learning. If you are an above average skilled person in a top tech company and you lost your job – you could still be in high demand for the tech companies in the next tier.

3. Disrupt instead of being disrupted. If you gain experience in using AI to improve HR functions in your current company – you probably have high odds of being considered for a higher order role in your company, or be in demand to do more of that in another company that wants to do AI in HR. I just used HR as an example – this is true for engineers too.

4. Objectively assess your value add – especially if you are a manager. This is a lot harder than it sounds – most of us are not that objective about ourselves. Get to be more hands on and add value. Status quo will get challenged sooner than any of us will like