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#llm #ai #ethics #workskills 

We are in uncharted territory - LLMs and "AI" based tools are everywhere, embedded in so much of what we do, but they are not well-understood by most. If you try to get your head around what is possible with these things, then you are faced with a barrage of marketing-speak and ill-informed hype. There is some signal in the noise, but it's very hard to stay on top of.

Technical folks struggle to find the signal in the noise, and many business brains get swept away by the hype (and by Zuckerberg's aspirational announcements... thanks, Zuk). There are a few common archetypes that have emerged:

There's the AI-ate-my-job developer, the one-with-the-vibe junior, the embrace-ai-or-get-out CEO, the cathedral-building anti-slop senior, the vc-funding-focused entrepreneur, and the not-on-my-watch IT gatekeeper. There are also people who seem to be doing things very sensibly, but it's worth looking closely at the extremes - there are a lot of pitfalls to avoid, and wisdom to be adapted and adopted (#steal-like-an-engineer).

There are clear benefits to fast iteration and quick prototypes, and adopting LLM tools on complex brown-field projects often has mixed results. It's worth knowing about the (shifting) limitations of these tools, and the emerging best practices (which often look a lot like the best practices that emerged in the before-times). It is worthwhile to deeply consider safety-nets and guard-rails as we lean on these tools, we need to iterate on our approach to engineering rather than focusing on writing more code faster (LOC is a bad measure of productivity, we know this!)

The other thing we really need to think about and solve for as a community is: how do we play the long game? I'm pretty convinced that we will still need human software engineers for a long time to come, and good ones at that. Skill comes from experience, and the junior developer job market is a bit of a dog show.

How should junior devs prepare for the future? And how should organisations adapt so that they have people that they can rely on 5 years from now? I have some opinions...

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Software engineering will remain relevant despite AI advances, but success requires strategic adaptation. Organizations should invest 70% of AI resources in people (culture, training, psychological safety), 20% in infrastructure, and only 10% in models. LLMs enable faster development but introduce new challenges: code review becomes harder, human connection decreases, and blast radius increases. Future-proof strategies include diversifying model dependencies, building guardrails, focusing on clean context, and forming dynamic project teams. Individual developers should hone craft fundamentals, expand agency beyond code, and develop orchestration skills. The bubble may burst, but AI adoption done right—with emphasis on human skills, experimentation safety, and workflow redesign—creates lasting competitive advantage.

Playing the long game - Sheena O'Connell - NDC London 2026