Building Your Career in AI: Real Talk from the Trenches

Inspired by insights from Andrew Ng and Lawrence Moroney’s career advice talk

Look, the AI space is absolutely wild right now. As someone who’s been deep in developer advocacy, cross-platform development and best practices in software engineering, I’m watching this transformation happen in real-time, and honestly? It’s pretty exciting, but also chaotic as hell.

We’re Living in the Future (Kind of)

💡 Inspired by Andrew Ng’s “This is the golden age to be building“

Here’s what’s changed just in the last year or two:

The tools got ridiculously powerful. I’m talking about LLMs that can actually understand context, workflows that don’t suck, voice AI that sounds more human, and coding assistants that legitimately boost your productivity.

Andrew’s point: We now have AI building blocks that were very difficult or did not exist a year or two ago: large language models, RAG-augmented workflows, voice AI, and deep learning frameworks

I’ve been experimenting with everything from various AI coding assistants to custom MCP servers, and the difference from even six months ago is night and day.

Building stuff got stupid fast. I recently prototyped a video watcher persona system that generates custom advertisement videos on the fly, Multi-agent AI content generators or a “simple” vibe-coded Conclave RPG Game. The bottleneck isn’t typing code anymore, it’s knowing what to build and how to architect it properly.

Andrew’s observation: With AI coding, the speed with which you can get software written is much faster than ever before

The Real Shift: It’s About Vision, Not Just Code

💡 Directly inspired by Andrew Ng’s “Product Management Bottleneck” concept

Here’s something I’ve noticed working on React Native TV projects and AI automation pipelines: the hardest part isn’t implementing features anymore. It’s deciding what to build, writing clear specs, and understanding what users actually need.

Andrew’s key insight: When it is increasingly easy to go from a clearly written software spec to a piece of code, then the bottleneck increasingly is deciding what to build or writing that clear spec for what you actually want to build

This changes everything. The traditional engineer-to-PM ratio is getting compressed. Teams that used to be 8 engineers to 1 PM are trending toward something closer to 1:1.

Andrew noted: “I’m seeing the engineer to PM ratio trending downward maybe even two to one or one to one… some teams I work with the proposed head counts was like 1 PM to one engineer”

Translation for us developers: If you can think like a product person and ship code, you’re gold.

Andrew emphasized: “engineers that can also shape product can move really fast… the subset of engineers that learn to talk to users, get feedback, develop deep empathy for users so that they can make decisions about what to build, those engineers are the fastest moving people”

How to Actually Succeed (From Someone in the Arena)

Stay Current or Get Left Behind

💡 Andrew’s advice on staying at the frontier of tools

I’m not trying to be dramatic, but tooling changes every 3-6 months in this space. I’ve seen it everywhere, AI coding assistants, and automation workflows. Being one generation behind means you’re working twice as hard for half the output.

Andrew’s experience: “If you ask me every three months what my personal favorite coding tool is, it actually probably changes definitely every six months, but quite possibly every three months. Being half a generation behind in these tools means being frankly quite a bit less productive”

Practical advice: Set aside time weekly to experiment with new tools. I literally block calendar time for this.

Build Real Shit

💡 Andrew and Lawrence’s emphasis on building and showing work

Don’t just play around—build things that solve actual problems. I’ve been working on automated video pipelines, MCP servers for app KPIs, and content automation systems. These aren’t toy projects; they’re solving real workflow problems.

Andrew’s encouragement: “Just go and build stuff right… your opportunity to build things and I think showing them to others is greater than ever before”

Lawrence’s advice: “Don’t let your output be for the job you have, let your output be for the job you want”

🔥 Real-world example: Andrej Karpathy recently shared how he used Claude Code to reverse-engineer his entire Lutron home automation system in a single session. It found controllers on his network, discovered open ports, pulled PDF documentation, connected to devices, and even tested by turning his kitchen lights on and off. He’s now “vibe coding” a master command center to replace the janky official app. This is what building real things looks like—taking AI tools and solving actual problems you have.

Your portfolio should show you can ship, not just prototype. Conference presentations are great (I do a ton of them), but shipping production code or tools people actually use? That’s what gets attention.

Your Team Matters More Than the Logo

💡 Andrew’s story about the student assigned to Java backend payment processing

I’ve worked with teams at various companies, and here’s the truth: the brand on your business card matters less than the people you’re learning from.

Andrew’s warning: He told a story about a Stanford student who joined a company with a “hot AI brand” that refused to tell them which team they’d join. After signing, they were assigned to backend Java payment processing instead of AI work and left after a year of frustration

Look for:

• Engineers who are genuinely curious and stay current
• Teams that ship regularly, not just talk about shipping
• Environments where you can experiment without getting shut down immediately

Andrew’s emphasis: “One of the most strong predictors for your speed of learning and for your level of success is the people you surround yourself with… instead of working for the company with the hottest brand, sometimes if you find a really good team with really hardworking, knowledgeable, smart people trying to do good with AI, but the company logo just isn’t as hot, I think that often means you actually learn faster”

Access to emerging tech and working with smart people? Invaluable. That’s what moves your career forward.

Think Like a Builder, Not Just a Developer

💡 Lawrence’s “Business Focus” pillar

The best opportunities come when you understand the why behind what you’re building. Whether it’s optimizing a TV interface for 10-foot viewing or designing an AI workflow for content creation, knowing the business context makes you infinitely more valuable.

Lawrence’s framework: “Business focus is non-negotiable… Everything is geared towards production. Everything is biased towards production… the bottom line is that the bottom line is the bottom line”

The Industry is Splitting (And That’s Okay)

💡 Lawrence Moroney’s “Bifurcation of AI” prediction

You’ve got two paths forming:

1. Big AI: Cloud-based LLMs, massive compute, platform services (think Claude, GPT-4, etc.)
2. Small AI: Self-hosted models, edge computing, specialized use cases

Lawrence’s insight: “Over the next five years there’s going to be a bifurcation… Big AI will be what we see today with the large language models getting bigger in the desire to drive towards AGI… The other side is self-hostable models are becoming they’re exploding onto the landscape”

Both are valid. I work across both—using cloud LLMs for content generation while also thinking about edge processing for TV platforms. Pick what aligns with your interests and the problems you want to solve.

Real Talk on Technical Debt

💡 Lawrence’s entire framework on “vibe coding” and technical debt

AI systems can accumulate technical debt fast. I’ve learned this the hard way building automation pipelines. Here’s the framework I use: think about technical debt like financial debt.

Lawrence’s analogy: “Think about debt the way you normally would, right? Buying a house… you end up paying back the bank about a million dollars on half a million owned. That is probably a good debt to take on… A bad debt would be an impulse purchase on a high interest credit card”

Every time you generate code with AI, ask yourself: Is this worth the technical debt I’m taking on?

Lawrence’s framework for avoiding bad technical debt:

• Clear objectives met: You knew what you needed to build, didn’t just spin out code randomly
• Business value delivered: How’s this helping the business? “It’s really cool” isn’t enough
• Human understanding: Can your team understand and maintain this code?

Balance quick prototyping (which AI enables) with sustainable architecture. Document your shit. Your future self (and your team) will thank you.

Lawrence’s warning: “Code is cheap now in the age of generated code. Finished code, engineered code is not cheap”

Navigate the Hype

💡 Lawrence’s extensive discussion on the “Anatomy of Hype”

The AI space is drowning in hype, and you need to learn to filter signal from noise.

Lawrence’s key insight: “The currency of social media is engagement. Accuracy is not the currency of social media… if you are the kind of person who can filter the signal from the noise and then who can encourage others around the signal and not the noise, that puts you in a huge advantage”

When someone comes to you saying “we need to implement agents” or “we need AI,” your first question should be: Why?

Lawrence’s story: A European company CEO asked him to implement agents because “everybody’s telling me that I’m going to save business costs.” Lawrence kept asking “why?” until they got to the real need: making salespeople more efficient by reducing research time from 80% to 20% of their work

Learn to make things “as mundane as possible” to truly understand them. Strip away the magic and hype to see what’s actually happening under the hood.

Concrete Actions You Can Take

Diversify Your Skills

💡 Lawrence’s advice on avoiding being a “one-trick pony”

I’m not just an AI person, I do TV platforms, React Native, developer advocacy, content creation, friction logging of new sdks and tools. This breadth makes you resilient and more interesting to work with.

Lawrence’s warning: “Don’t be that one-trick pony who only knows how to do one thing. I’ve worked with brilliant people who are fantastic at coding a particular API or particular framework and then the industry moved on and they got left behind”

Ship to Production

💡 Lawrence’s emphasis that everything is about production now

Move past proofs of concept. Learn deployment, monitoring, scaling. The bar has moved from “can you build cool demos” to “can you deliver business value?”

Lawrence’s observation: “What’s it actually like working in AI right now? As recently as like two or three years ago working in AI was if you can do a thing, you’re great… Unfortunately, that’s not the case anymore. It’s really a lot of today, what you’ll see is the P word production”

Build in Public

💡 Andrew and Lawrence on showing your work

Share what you’re learning. Write posts on dev.to, speak at conferences, create GitHub repos. The community feedback loop is invaluable, and it opens doors you didn’t know existed.

Lawrence’s example: When he interviewed at Google, instead of answering random questions, he showed code he’d built, a Java application running in Google Cloud for predicting stock prices. “My entire interview loop was them asking me about my code… It gave me the power to communicate about things that I knew”

Understand Agentic Workflows

💡 Lawrence’s breakdown of what makes something “agentic”

If you’re going to work with AI agents, understand the four-step pattern:

1. Understand intent: Use LLMs to grasp what needs to be done
2. Planning: Declare available tools and create a plan
3. Execution: Use the tools to get results
4. Reflection: Review results against intent, iterate if needed

This isn’t just buzzword compliance—it’s engineering discipline applied to AI systems.

Prepare for the Bubble (But Don’t Panic)

💡 Lawrence’s discussion on the AI bubble and industry maturation

Look, there’s probably a bubble coming. Lawrence laid out the anatomy: hype at the top, massive VC investment drying up, unrealistic valuations, me-too products everywhere, and a small kernel of real value at the bottom.

Lawrence’s lesson from the dot-com bubble: “Amazon, Google, you know, they did it right. They understood the fundamentals of what it was to build a .com. They understood the fundamentals of what it was to build a business on .com. And when the bubble of hype burst, they didn’t go with it”

The survivors will be those who:

• Focus on fundamentals
• Build real solutions
• Understand the business side
• Diversify their skills

Lawrence’s advice: “For you for your career to avoid the impact of any bubble bursting, focus on the fundamentals, build those real solutions, understand the business side, and most of all, diversify your skills”

Bottom Line

💡 Synthesizing Andrew and Lawrence’s core messages

The AI space is moving fast, but the fundamentals still matter: solve real problems, ship regularly, stay curious, and work with good people. Don’t get caught up in hype cycles—focus on building things that work and creating actual value.

Andrew’s encouragement: “There are so many things that each of you can build and what I find is the number of ideas out in the world is much greater than the number of people with the skill to build them… There are a lot of projects in the world that if you don’t build it I think no one else will build it either”

Business focus is non-negotiable now. Understand the business requirements, focus on production, and become that trusted advisor who can translate technical reality to leadership.

Lawrence’s positioning: “Becoming that trusted advisor… if you are the kind of person who can filter the signal from the noise and then who can encourage others around the signal and not the noise, that puts you in a huge advantage that makes you very distinctive”

And remember: the best tool is the one you actually use and understand deeply, not the shiniest new thing on Hacker News. Code is cheap now in the age of AI—but well-engineered, production-ready code? That’s still expensive and valuable.

Lawrence’s final wisdom: “Ideas are cheap. Execution is everything”

🚀 Now go build something cool.