We've been told since late 2022 that "within 6 months, we won't need software engineers anymore". I think that's half-right.
I also think it's just the AI flavor of an old Holy War.
Remember the Olden Days
Think wayyyyyyyyy back to... 2010. (I have children older than this, and that makes me feel a sense of existential dread 😱)
- Businesses were weathering an economic downturn and trying to "do more with less".
- We were starting to realize that deeply-specialized "Silos" were (at best) problematic and (at worst) causing harm to our productivity.
- More powerful Javascript frameworks were becoming super-popular, and more "traditionally backend logic" was moving its way into the "traditionally design-only frontend".
This led to the rise of a concept called the "Full Stack Engineer" - someone who could generalize and interact easily with the development stack at multiple layers. Need some SQL? They can give you a passable version. Need to integrate with a vendor API? They've got you. Have a CSS bug that's causing the widget to be invisible only on Thursdays? They'll pair up and help you find it.
Half the tech community said that FullStack engineers were unicorns, that nobody could keep that much in their head at once. Half the community said that every software engineer should become FullStack if they wanted to survive the downturn with a job. Half the community (who apparently struggled with arithmetic on multiple fronts 🤔) advertised themselves on LinkedIn as FullStack Engineers with 20 years of ReactJS experience.
FullStackers: Plausible (but with a caveat)
In fancy management and corporate leadership courses, there's a popular concept of "Professionals of Certain Shapes".
There's the "I" shaped person, who has chosen to obsess over a single topic and dived deeply into it. They're not often super aware of other topics and disciplines, but they know their one thing inside and out.
There's the "T" shaped person, who knows a single topic very well but has basic general knowledge about several others. We tend to call them "generalists"... jacks of all trades, master of... well... one or less.
There's a "Comb" shaped person, who knows several topics very well while having a passing knowledge of many others. These tend to be your generalists with a little more career time, who have potentially moved around the organization a bit and held a few roles on different teams.
The more prolific HR folks probably have other shapes of people out there, but that feels to me like splitting hairs and these 3 shapes probably suffice for most of us to work with.
I'm of the opinion that the "FullStack Engineer" is just someone who's moved into the "T" or "Comb" shaped realm. I'm also of the opinion that anyone can become one of these shapes with curiosity and study time. It's not something reserved for the elite, so much as defined by the eliteness of people who do it.
Get to the Part Where They Survive the AIPocalyse, Blink
I know, I know -- I digress.
The common trait among all "T" and "Comb" shaped people is that they're adaptable. Generally, they started as "I" shaped people who knew a lot about something, and then they started to attend to things adjacent to their specialty.
They weren't experts in those other things, but they were curious about them. They got just good enough to solve the basic problems, and then lean on the experts when things got too deep.
Which Leads Me to AI
AI in the software development lifecycle is going to eat the "I" shaped people for breakfast. When you can give all the world's knowledge to a language model and let it answer questions, generate code samples, and sniff out bugs, an "I" shaped software engineer isn't really going to be needed. The value of that one language, of that one specific niche technology, is eliminated.
But you know what AI can't do? Understand its surroundings. Even as context windows get larger and inferences get faster and cheaper... people can out-think them any day of the week. I think one of the greatest human characteristics is the ability to solve a problem based on a completely different set of unrelated data that just happens to kinda pattern-match... like when Mr. Miyagi tells Daniel to "Paint the Fence", in an effort to help him learn Karate.
This happens to be a speciality of people who are good at... Adapting. When they immerse themselves in a new technology for the first time, everything seems weird and different. But instead of being confounded by it, they notice patterns of the same "shape" as other technologies that they do know well, and it helps them learn.
It's something the bots can't do. Bots need direct correlations, they need pre-training.
And That's Why The Generalists Will Win
If the hype about AI holds true (I don't believe it will, but let's game this out), The "I" shaped people are going to struggle because they're easy to replace with a model. Pre-train it on "technology X" and boom, they're done. The "T"s and "Comb"s on the other hand will be able to leverage their lateral-thinking advantage to survive.
If the hype falls apart, we're going to have a big need for those generalists. They're going to have to unravel a bunch of code, likely touching multiple technologies in the stack, and pay off tech debt along the way.
But it's gonna be a while
We probably have another year or two before we see the full effects of this AI stuff... which is rough if you're looking for work in tech right now. I'm sorry to be the bearer of bad news but it's gotta get a little bit worse before it gets better. If you can just hold on, though... and spend the time learning a few new technologies, you'll be in a really good place when the demand curve bends its way back in a favorable direction!
The Moral of the Story: Avoid Hype, Focus on Fundamentals
Yeah, you can try to replace all SaaS companies with a $200/month Claude Code subscription and maybe get-rich-quick. But you'll get better return on your investment if you spend that time, money, and effort on broadening your knowledgebase. Are you a coder? Learn a CI/CD stack. Are you a SysAdmin? Take a course in Python or Rust or Go or whatever language you'd like to play around with. Are you an AI bro? Make sure you know how to do all of the things you scream "AGENTICCCCCCCC" about, but with your WiFi turned off.
There are ultimately two kinds of technologists: those who enjoy the challenge of growing and learning, and those who are doing it all to hit a jackpot. I know which one I am... do you?
Top comments (10)
I think the core idea here is really about adaptability under changing systems rather than AI specifically.
The T/comb framing makes sense because most real work now sits across boundaries, not inside a single silo.
Though I’d still argue depth doesn’t disappear — it just becomes the anchor that lets generalists stay correct while moving across domains.
Yeah, in retrospect I probably would've added that this isn't unique to the AI hype bubble, but just the most recent iteration of "how do we deal with change."
The AI-pocalypse is just so... in our faces right now... everybody selling us that this is a watershed moment in history where EVERYTHING IS UNPRECEDENTED... but eh. It's the next verse of the same song.
I mostly agree with the “same song, different verse” framing—but I think there’s a subtle twist worth pulling on.
If we assume AI really does compress a lot of “I-shaped” execution work (syntax fluency, boilerplate, known patterns), then the surviving “depth” might shift upward rather than sideways. In other words, depth in problem framing and constraint understanding becomes more valuable than depth in a specific tool or language.
That creates an interesting tension in your T/comb argument: generalists win on adaptability, but specialists don’t just vanish—they get redefined. The “I-shaped” engineer who survives is probably not the React expert or the Kubernetes expert, but the person who deeply understands distributed systems tradeoffs, security models, or user behavior under constraints—things AI can describe but not reliably own in context.
So maybe the real split isn’t I vs T/comb, but:
replaceable depth (implementation-level expertise)
irreplaceable depth (judgment-heavy, ambiguity-heavy domains)
and connective tissue (the T/comb layer)
Which leads me to a question back to you: do you think AI pushes more people into becoming “connectors” (T/comb), or does it actually raise the ceiling on what counts as valuable “deep” work and preserve strong I-shaped roles—but only in narrower, more abstract domains?
Personally I'm of the opinion that a lot of the AI hype is going to crash and burn when people realize it can't literally solve all their problems (or it's made economically infeasible as we're already seeing it to shift). If it settles into its niche ok, I see the connectors being more valuable because one use case where AI does a great job is in training in depth... there might be a few I-shaped roles left out there but they'll be the absolute deepest I's ever who survive... the ones whose knowledge takes a lot longer to surpass in the retraining loops.
T's and Combs have an easier time of things because they think more laterally.
So maybe in summary, I see MORE people being pushed to connector roles but also the truly deep I's will go deeper... maybe with just fewer of them around.
I think you’re right that AI squeezes out the mid-depth “I-shaped” work first, while pushing more people into connector roles.
The interesting part is the remaining true deep experts don’t become obsolete-they become rarer and more important, because their knowledge is harder to fake or compress.
So the system ends up more polarized: lots of connectors, fewer but deeper specialists.
Do you think connectors eventually become the default career path, or just a transition layer people pass through?
I don't know about default - career paths are weird things and so much depends on what people like to do. Connectors and Deep Experts seem like parallel paths that (at some point in a career) may transition into the other.
Got it, Ben.
Talking with you has been really interesting. You seem very experienced.
I’ve been thinking about an idea for a while, and I’m looking for someone with your level of expertise to help bring it to life.
I believe it could be beneficial for both of us. If you’re interested, let me know and we can discuss it further together.
Great point of view. This debate has been going on and will go on ( probably). You talked about different skill shaped people. Any advice on someone is currently on the learning stage. The AI hype has been going around so much that it sometimes feel more confusing when everybody are saying to focus on strengthening your fundamentals. If you were asked What would say about what exactly are these fundamentals?
The fundamentals are things like Collaboration via Source Control, CI/CD delivery automation, making quality tests a first-class citizen of your process, and thinking about your code from a Security perspective.
These concepts transcend the topic of a certain language or technique and address how the work gets done... the same fundamentals can be applied regardless of what you're buildng.
If you're in the learning stage, those are the things I would learn FIRST - from there, you can pick up any language or technology and you have the tools to manage what you build easily. Note that you can totally get AI to help with these things, but you need to know how to do them manually first.
Great article. Thanks for sharing your experience.
I'm currently exploring Claude Code, Codex and AI agent workflows. This was helpful.