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I've worked as a senior leader at Microsoft, Meta, and OpenAI, and ran my own startup. Many people consult me over email and LinkedIn. Instead of answering them privately, one person at a time, I wanted those answers to benefit everyone… so I launched this: a public archive of anonymized answers, open to everyone.

From the Archives asked

answered May 11

I really enjoy your seminar so far. I've learned a lot about navigating SWE through a career lens and how important dealing with people is to your career opportunities.

Not sure if you remember, but I wrote my blank space submission about my experience of going through the YC W26 batch, and the decision to leave my startup to re-enter school. The experience made me realize that I'm much more energized by deeply technical database and distributed systems work than by scaling a company I'm not personally passionate about.

Right now I'm trying to think carefully about how to position myself so that I can spend the early years of my career working on hard systems problems while learning from more senior engineers.

I'd be curious to hear your perspective on how I should approach breaking into these kinds of environments intentionally instead of just optimizing for prestige/compensation.

From the Archives asked

answered May 7

[I'm extending my AI agent product to tackle customer support scenarios]. Looks like a real wedge that could extend to other YC companies. Would love any advice you've got. [Editor: some additional company-specific context was cut]

Philip's answer

I know nothing about AI support agents at all, but here are some random thoughts from someone with very little knowledge in the domain:

- Naively, it seems like there is already a ton of energy and funding going into AI support agents and speeding up customer ticket resolutions.

- That said, the domain is almost definitely not winner-take-all. The trick is probably finding an angle. For instance, the standard super-cheap support agent likely flips through a flowchart of statements that can be easily automated. That sort of person doesn't need "context" from elsewhere/etc, because there are few decisions to be made. But Tier 2+ support begins to be manned by expensive engineers who do need context in order to make decisions. The good news about automating Tier 2+ is that it's the one where serious dollar savings can be realized.

- It's always better to start with a super-concrete use case or a niche product before generalizing. The whole 1000 True Fans line of thinking.

Above all, remember that you live in a time when moats are eroding. Anyone else can copy the code you've written very quickly -- and imagine how fast they could do that, say, six months from now when AI is even better at producing code. So you must rush to lock in other business moats: things like scale efficiencies, network efficiencies, or brand. The last is a great example: Calendly is a dead-simple product anyone can build over a weekend now. But they've locked in the brand, so competition no longer matters.

Have you read Seven Powers? https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319. I wouldn't recommend spending your time on reading the whole book unless you're very fast -- instead, just ask AI to summarize it for you. Useful to think about when it comes to building a sustained business.

The best ideas are "contrarian and right" (Thiel). Your idea is currently not contrarian, but it certainly is right (i.e. there's a real need). Remember also that Warren Buffett often reminds people that you can make many customers happy, and be in a high revenue industry, but still make no profit -- like airlines. It's far better to be in a small business that's per-employee highly profitable than a large scale business that makes little money.

From the Archives asked

answered Apr 15

We talked briefly after class about my dilemma with internship offers, and I wanted to follow up with a bit more context and get your thoughts.

I am currently deciding between two internship opportunities for this summer.

One is with Meta, where I have been matched to [team] as a SWE intern. The work seems to involve large scale distributed systems, though I am still trying to better understand the exact scope and level of impact, since it could potentially be more focused on building dashboards. The other is with Amazon [team], on a team that works on large scale ML systems supporting product cataloging, with infrastructure supporting tens of thousands of models, also as a SWE intern.

In this situation, I see Meta as having higher upside due to company name, but potentially a weaker project, while Amazon seems like a stronger project but with less upside from a company brand name perspective. I would really value your perspective on how you would approach this decision and what factors you would prioritize.

As additional context, I do have a fall internship lined up with Google working on research for SecGemini as a SWE intern.

If you have any advice on how to approach this decision, or what signals I should prioritize when evaluating teams at this stage, I would really value your perspective.

Thank you again for your time. I appreciate it.

Philip's answer

I think you're absolutely right that the upside of Meta is better. It's hard to tell how the team will be, but I think what you're trying to do right now is build toward a brighter future for yourself.

Amazon on your CV is going to be nowhere near as strong as Meta on your CV. Since it's an internship, there's very low downside. Even if you don't like your team or the work, you at least get it on your CV. Honestly, even the most boring jobs can be made quite interesting if you set your mind to learning as much as you can.

The other thing about Amazon is that it is not anywhere near as focused on developer happiness as any of the other major software firms. To Amazon, I think software engineers ultimately still feel like a cost center. Almost any other major software firm views their developers as their key asset, not a cost to be reduced.

From the Archives asked

answered Mar 21

Hope you're doing well. My name is [redacted], and I'm a 2025 UW CS grad. Your career seminar advice made a big impact. Thought I'd reach out for more advice.

I'm an SDE I at Expedia Group in platform engineering. The field is changing drastically. I was listening to a Lenny's podcast where Boris, the head of Claude Code, states that software engineering as a role will no longer exist by the end of the year. (I know he's simultaneously showing off for investors, but still.) A non-SDE friend called this AI boom a "suicide mission" and said that tech/IT jobs were all going to be replaced soon.

This was a startling thought, because most of us like to have an answer prepared to "where will you be career-wise in 5 years", preferably that our career will still be around.

To be clear, I'm not anti-AI. I think AI is an amazing tool that makes writing software more accessible than ever. But the way I see it, there are 3-4 options for a new grad right now.

1. Stick with software engineering. Integrate AI into your workflow, but believe that AI can never fully replace humans. Try to play the politics game and hope you don't get laid off during the routine layoff cycles.

2. Stick with software engineering, but move to roles that will still retain a high demand - AI/ML, cloud computing, cybersecurity. (Probably not QA testers or UI/UX front end engineers?)

3. Stick with computer science in general, but go back to college for a higher degree in something smart that might stick around, like quantum computing.

4? Go back to college for an entirely different degree, for a job that will be less likely to be replaced with AI (teaching, architecture, etc.).

Thoughts? I've read your Molochinations article on this, but still wanted your opinion. Feel free to redirect me if you've already answered the general sentiment of this email elsewhere.

Philip's answer

Farmers went from 40% of our population to 4% in 100 years. AI's dominance will happen much, much faster than that — but I don't think any job goes to 0%. The question is whether the job becomes like farming, where millions are still employed, or like carpet weaving, where only artisans charging $10,000 per carpet are employed.

Anyone right now who says they know what's going to happen in AI or in job displacement is likely just guessing. I don't see how anybody could possibly know the answer for sure. But I think there are a few broad positions:

  1. Nothing stops AI from continuing to get smarter than the average US worker. In this world, moving into other intellectual work doesn't make much sense either.

    1. I'm doubtful, though, when people recommend you go into plumbing. It's not clear to me how much longer robots won't be able to do plumbing, especially if you've kept up at all with the advance of robots over the past few years.

  2. AI gets very good, but not good enough to replace senior engineers. This is a world where AI becomes good enough to replace junior people, but can't quite do the big architecture and design things that senior engineers can do. In this world, a percentage of people will remain employed in software development. If this is the world you think will happen, you should gain skills as quickly as you can to always stay above the rising tide.

  3. AI gets good, but remains a force multiplier for every worker, not a replacement. In economic terms, it's a "complement," not a "substitute." This is the world most people who doubt AI's job disruption fall into. They rightly say that in the past, Luddites were always wrong, that the progress we see does not eliminate jobs; it simply causes people to need to do different jobs. People in this belief system will quote things like the lump of labor fallacy.

I think there's no way to know which path is right now. Some of it depends on knowing yourself well enough to know what you will be differentially good at. In a world where change is happening much more rapidly than before, I think the person who survives best is not necessarily the person who guesses the future most accurately, but the person who changes most quickly to adapt as the environment changes. If you buy into this, it suggests not doing commitments of multiple years down certain paths unless you are sure that path is going to work out well. For instance, suppose you do get another college degree, taking three years to get a bachelor's in architecture or something you think will be safe from AI. You need to be pretty sure that that's the case because you've put a three-year dent on your ability to keep up with AI as you do all the schoolwork.

From the Archives asked

answered Mar 17

I find myself on the curve where Im a decent beginning programmer without AI, but my peers are using AI. And so I use it to try to keep up with others using it, but I find that the more I use it the less I actually improve my own skills, the rustier I feel even on the simple stuff. And all things equal I prefer doing it on my own because I find myself rapidly improving when I do. Im just curious what you advise younger novices to do at this point?

Philip's answer

Wow. This is a tough and deep question.

We allow calculators in math classes now (they used to ban them "back in my day"), and yet we still teach basic arithmetic. I genuinely wonder why. (I don't mean that I don't agree -- I am literally simply asking, "What is the reason behind still teaching it?")

One could imagine a math class that was instead all about concepts. "Addition is combining two smaller things to make a bigger one." Etc. And then having calculators there on day one, like second grade. "Squares are the same number multiplied by themselves. It's like how you get the area of an actual square. Square roots are the opposite -- you go from knowing the area to wanting to know a side." Yes, it's true those kids would be completely screwed without a calculator — say, somehow stranded somewhere without technology and needing to add stuff, though I can't invent a reason right now. But is that rare instance worth spending years and years of class? Or would kids be able to solve much more interesting math problems, much more quickly, if we taught the concepts but not the mechanics?

It took a while for schools to stop teaching cursive. My kids were raised at a time when parents hotly debated this. Now it's accepted as a waste of time. "What if they have to write longhand?" On rare occasion, they're going to be much slower.

These days, we don't mind that most CS grads can't write a compiler themselves, or even look at assembly to decide whether there's a bug. But we accept that you'll typically always have a compiler when you need one, and that the world only needs a tiny number of people who can write you a new one.

I don't know if coding can/should go that way. Is there an AI-first, don't-worry-about-the-code way to teach programming to professionals in the field? Can we get to the point where AI almost serves the same function as a compiler, basically taking your higher-level language (English) and turning it into assembly? Once in a rare while, when it's wrong, perhaps only one engineer on your 50-person team can look at the code itself to fix the problem?

I'm brainstorming aloud here. Really not sure.

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