LLMs are eroding my software engineering career and I don't know what to do

For example: people in developing countries throw away more food then in developed countries although it is relative to their income much more value. The reason is because they often do not own fridges, use a lot of rice which spoils fast or have difficulties with the food supply chain in other forms.

Food waste is a bad indicator for food value.

I would bet my life and everyone I care abouts' lives that much more than 70% CAN write fizz buzz.

I give that context because unlike a lot of you, I’m not a world class FAANG engineer and never will be. It is from this context all of my thoughts on AI flow. I work with people who are trying to use AI to produce work involving entire markets, roles, skillsets and technologies they don't even know exist, let alone understand.

> I had the opposite thought.

Until I recently got pulled back deep into engineering despite not being hands on for close to a decade, so did I. I was pulled in not because of any pure technical capability but instead because it's been recognised the team requires more. The skills I thought served only to help me stay employed in any role in the most basic roles are increasingly turning out to be things other's do not have and are becoming increasingly important.

These are skills I always assumed crucial to “baseline competency” for everyone, but yet where a significant amount of them do not, and these individuals are now finding themselves in positions where they are less useful than me as a result. Many of them can not simply be acquired from AI either, and require years of active growth and practice.

> Being a generalist was very useful to me 5 years ago. Now AI models have made everyone a generalist.

I think they could, but have not. Not at a scale required for me to have significant concern.

AI works as well as the context you can provide, and you don't know what you don’t know. If the context is shallow, so to will be the output, even when it looks convincing and that “looks convincing” part I believe is the most dangerous part.

As an example; I've been (recently) attached to an engineering team, despite last holding that title pre-2015, after AI assisted work contributed to a multi million dollar contract loss. A customer experienced an outage, it was "fixed" and everyone moved on. A month later another outage occurred of a greater scale. A huge amount of time was wasted doubling down on the original AI finding, because the actual root cause had not been identified or understood, because it had been "fixed". Turns out AI had identified and "fixed" a symptom, not a root cause.

I was able to identify and resolve the real issue because I had wider operational and infrastructure context the team lacked, but the damage was done. Trust was gone, the client lost, and layoffs will follow. Those layoffs will be “because of AI,” but not any "10x'ing" of productivity. Instead it will be because plausible but wrong work made it into production and hid a very real problem as a result.

That’s the issue with AI as I see it now. It generates answers that survive initial scrutiny while completely missing wider context leading to cases where more impactful but hidden problems are introduced.

> That wide but not terribly deep skillset was immediately devalued by the AI models.

Perhaps “generalist” was the wrong word here.

Most "engineers" I have worked with are extremely deep in their area and surprisingly limited outside it. Even with AI, they struggle to move beyond their specialty because they lack broader foundations underneath not just modern infrastructure, but a range of areas equally important to the health of a business. My advantage has never been being the best engineer in the room, I knew early in my career I’d never compete with the engineer who can patch our kernel before upstream does, despite wishing I could.

What ended up mattering instead was becoming the "95% guy" across infrastructure, networking, systems, operations, business, customer success, and people management that allows me to work with people/organisations and ultimately connect dots in a way even the best engineers I have worked with can not. AI can help you develop skills in areas you don't have, but starting with most of it in areas in which people have exactly none, and where people seem extremely resistant to developing it with or without AI, has me significantly further ahead in the curve. Ironically, at least in my experience so far, AI has made that more valuable, not less.

> they make poor code

I consider this to be the least important part. We have testing, review, and process for that.

I believe (and have instructed juniors as such) that the real value of valuable technical people has never been producing rockstar code, or being a clone of Linus. It's in having a deep foundational understanding of the building blocks underpinning the now endless layers of abstraction, understanding consequences, tradeoffs, failure patterns, business impact, customer communication, customer wants and needs, and ultimately I guess to sum it up, organisational reality.

This feels more important than ever when they can generate plausible looking technical output instantly that they may be able to validate, but equally produce plausible output in a huge range of areas they absolutely can not, but for which their successes in code have led them to believe they can. Because they underestimate what they don't know and in fact often assume they know far more than they do with no real basis for such a belief.

On the whole, I think I would end my thoughts like this.

For years I lived with the stress that "rockstar" engineers would lead to me eventually becoming irrelevant, much in the same way I might fear AI. So far, being 95% across customers, leadership, sales, support, engineering, and business strategy without losing the technical depth underneath it has meant this fear was unfounded and in fact put me ahead of them. I believe I am not isolated in this, and that in fact we will see more of it.

All else aside, my roles as of the last decade often require me to be in the room and working with humans. AI has not changed this, and there is no current indication it will. The requirement will be that I continue to remain in the room, only now with AI. This is for many reasons including regulatory, because portions of what I do involve systems that if mishandled could lead to more than just a loss of profit. There may be less of these roles, but as it stands I see nothing to indicate they will not exist.

What companies do you consult and on what