AI & Your Tech Career

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After you apply, you can reach out to Segun Akinyemi (the creator of this website) on LinkedIn for a referral. Be sure to include the link for each internship you applied to and a copy of your resume!

Created and maintained by Segun Akinyemi

This site is always available at aka.ms/nocap

A practical, continuously updated collection of resources on AI and its impact on tech careers. Answers to the questions I keep getting asked, plus the skills, tools, and courses to actually go do something about it. Built by a software engineer who uses AI every day at work and is tired of watching people panic.

Quitting programming as a career right now because of LLMs would be like quitting carpentry as a career thanks to the invention of the table saw.
Simon Willison, co-creator of Django
One of the most respected voices on AI-assisted software development. Read his blog.

Reality Check Industry Outlook What to Learn Where to Learn Tools Who to Follow What to Read & Watch Get Connected

Hello there! I'm Segun Akinyemi, a software engineer at a company you've probably heard of. This site exists because I got tired of watching people get misled into thinking they should quit computer science, give up on software engineering, and go sit in a hole while waiting for AI to destroy the world.

At work. At events. At conferences. Everywhere I go, people ask me if my entire career field is gone now because AI writes code. That's like asking if mathematicians disappeared when calculators were invented, or if accountants died when Excel came out. The tool changed but the work didn't go away. Coding manually is dead, but software engineering isn't.

Even before AI, people never understood what software engineers actually do. It's always been a lot more than writing code. The confusion is understandable if you only listen to hype peddling CEOs, journalists, and influencers who need clicks and investors. Listen to the technical practitioners, the people actually building the stuff, and it's a very different story.

I keep seeing students, job seekers, and new grads ask whether they should quit their whole life and become farmers because some money loving, non-technical, shareholder groveling CEO said AI will replace all white collar jobs. Or some AI CEO claims software engineers will be replaced in months, while his company aggressively hires them.

Those statements exist to convince investors that the billions being spent on data center build out are worth it. Never mind that many of their AI products flat out suck. It's insane. AI is making CEOs delusional, and they'll say anything to avoid looking like they're falling behind, even when they clearly don't understand what they're talking about.

This site exists to combat AI hot take nonsense and calm anxieties. I keep it updated. Connect with me on LinkedIn.

Reality Check

Before you doom spiral about AI's impact on software development, read these. Short answers here, links to the longer essays if you want depth.

No. Coding as manual labor has been automated. Software engineering, the judgment, the systems thinking, the accountability, is becoming more valuable, not less. Companies building AI are still hiring engineers by the thousands. OpenAI, Anthropic, Google, Apple, Meta, Amazon, and Microsoft all have open engineering roles right now. If AI could do all the work, they wouldn't be hiring. Read my full take.

No. We don't have AGI. We don't have Jarvis. We don't have movie level AI. What we have is a text-generating non-deterministic probability engine that's incredible, but falls apart when you introduce sufficient complexity outside its training data, no matter how much context engineering you apply. These models predict the next token (word) in a sequence. They do not understand what they're saying. They're tools, not replacements for thinking. Read more.

Yes. Anyone telling you not to is wrong. People far smarter than me have said as much. AI tools amplify existing expertise. They make you faster if you know what you're doing. If you don't know what you're doing, AI just makes you faster at being wrong. A technical foundation is what separates someone who can use AI effectively from someone who copies and pastes and prays. Read more.

No. People have been predicting developers would be replaced since COBOL came out in the 1960s. This is a great graphic depicting that history. Every decade, new tools arrive, the "no more developers" dream persists, and then reality sets in. The complexity of software is intellectual, not mechanical. No tool has ever eliminated the need for humans who think clearly about hard problems. As Simon Willison, co-creator of Django, put it: "Quitting programming as a career right now because of LLMs would be like quitting carpentry as a career thanks to the invention of the table saw." Read the history.

Yes. AI can generate code, but your job is to deliver code you have proven to work. That requires understanding what the code does, catching when the AI is confidently wrong, and knowing how to debug it when things break. And things always break. AI amplifies existing expertise. The more you know, the more productive it makes you. The less you know, the more dangerous it makes you. Read more.

CEOs say things that make investors want to give them money. That's literally their job. Follow the actions, not the words. OpenAI, Anthropic, Google, Apple, Meta, Amazon, and Microsoft are aggressively hiring engineers right now. If their own AI could do all the work, they'd save the billions they're spending on salaries. They can't, so they're hiring.

Even better, the fearmongers are already walking it back. Fortune reported that Sam Altman (CEO of OpenAI) and Dario Amodei (CEO of Anthropic) are softening the job apocalypse story after years of selling companies the fantasy that AI would let them lay off all their staff and save a fortune. People doing the work every day already knew the boring truth. AI is a great tool. It can make skilled people faster. It's not a replacement for judgment, accountability, or the humans who understand the work.

Mostly no. The layoffs you're seeing aren't because AI did those people's jobs. They're because AI is ridiculously expensive to run. Companies need to free up cash for data centers, GPUs, and compute infrastructure, so they're cutting headcount to fund the build out. Tech giants like Google, Amazon, Meta, and Microsoft are cutting to fund GPU purchases, not because AI replaced those workers. Their revenues are growing. Their stock prices are climbing. They're firing people to reallocate money from payroll to compute. You can track tech layoffs here. And the companies that did cut jobs blaming AI are already reversing course and rehiring.

As Harvard Business Review put it, companies are laying off workers because of AI's potential, not its performance. Some CEOs are just using AI as a convenient excuse for cost-cutting. Case in point: Block (Jack Dorsey's company, he also founded Twitter) cut nearly half its workforce and blamed AI, but current employees told The Guardian that 95% of AI-generated code still needs human fixes and the real motive was posturing for investors. Even Sam Altman, CEO of OpenAI (creators of ChatGPT), admitted that some companies are "AI washing" their layoffs. Matheus Lima said it best:

Will some companies use AI as an excuse to cut headcount? Absolutely. Some already have. There will be layoffs blamed on 'AI efficiency gains' that are really just cost-cutting dressed up as something else.
Matheus Lima, AI Can Write Your Code. It Can't Do Your Job.

Don't let headlines scare you out of a career.

They've Been Lying To Us

Sam Altman (OpenAI CEO) and Dario Amodei (Anthropic CEO) have spent years fear mongering about AI replacing all white-collar jobs. Now they're walking it back and saying what level-headed people have been saying this whole time. AI is a great tool, but it's not a replacement for human judgment and expertise. Read this before letting a shameless CEO scare you out of a career.

Read The Fortune Piece

What to Learn

Not just what matters, but exactly where to go learn it. Every area listed below is still relevant in the age of AI coding. Every resource below is free. These are listed in a rough progression, start at the top and work your way down.

At some point in school, everyone got to use a calculator. Yet, some kids still failed the test. If you don't understand the material, a calculator won't save you. You won't even know what to type in. AI in software development works the same way. You can vibe code your way to something that works, but without real understanding, you'll never build anything that lasts. Employers don't pay for vibes. If you want to get paid, learn the fundamentals.
Segun Akinyemi, creator of this site. Shameless self-quote.

The foundation everything else builds on. Programming, algorithms, how computers work. If you skip this, you can vibe code all day, but you won't be able to pass a technical interview to get a real software engineering job. There are no shortcuts to mastery.

Take these Harvard CS50 courses and you are set. Seriously. They are free, world class, and they will give you the knowledge you actually need. Everything else on this page becomes 10x more useful once you have these basics down.

CS50: Introduction to Computer Science The starting point. The most famous CS course in the world.
CS50: Introduction to Programming with Python Python is the language of AI. Start here if you want to code.
CS50: Introduction to Databases with SQL Data is everything. This teaches you how it's stored and queried.
CS50: Web Programming with Python and JavaScript How the internet actually works, and how to build for it.
CS50: Introduction to Artificial Intelligence with Python Optional. Gets into machine learning territory, which is a different discipline from software engineering. Worth exploring if you're curious, but not required to be a productive developer.

Also check out The Missing Semester from MIT, which covers the practical tools (shell, Git, debugging, dev environments) that CS programs skip. It was updated in 2026 with AI tools now folded into every lecture.

Been coding in Python with the help of LLMs. That would have been impossible without the grounding that CS50 gave me.
u/extopico, a CS50 student who came back two years later to confirm the fundamentals paid off.

How you track changes to code and collaborate with others. With AI writing code for you, version control has never been more important. You need branches, you need backups, you need to stop AI from blowing away your codebase.

The moment you're on a team with other developers, if you don't understand Git, you cannot function. Merge conflicts, rebasing, cherry-picking. You don't have to memorize terminal commands. GUI tools like the one built into VS Code work great. But you have to understand what's happening under the hood.

GitHub Skills In my opinion, this is way better than starting with a book because you learn by doing. The "First Day on GitHub" and "First Week on GitHub" tutorials are just about all you need to get going.
Learn Git Branching Interactive, visual, and the best way to actually understand branching, merging, and rebasing. This sort of stuff matters when you start using Git on a team, so not just you coding by yourself.
The Official Git Book A deep dive, honestly deeper than most people care to go. Reading chapters 1 and 2 is a good idea though.

How data is stored, organized, and retrieved. AI has not made database knowledge obsolete. It has made it more important. AI retrieval systems (RAG, vector search, agentic queries) all depend on well-designed databases. SQL is everything.

SQLBolt Interactive SQL lessons. Start here.
SQL Murder Mystery Learn SQL by solving a murder. Seriously. It's fun.
SQL Zoo Practice with real queries across progressively harder exercises.
FreeCodeCamp Relational Database Certification Hands-on with PostgreSQL and Bash. Covers database design, normalization, and administration. Goes beyond just SQL syntax.

Application Program Interface. How software systems talk to each other. An MCP (Model Context Protocol) server is only as good as the API behind it. Without APIs, AI literally cannot do any of the agentic stuff people want. Good API design is critical for efficiency, cost, and preventing AI from touching what it shouldn't.

APIs for Beginners FreeCodeCamp. Clear, practical, beginner-friendly.
Public APIs List Hundreds of free APIs. Pick one, fire up your AI coding tool, and build something with it.

How you know your code actually works. This is exponentially more important now that AI writes code. "I told AI to make the button blue and I see it's blue" is not testing. Real software has thousands of invisible behaviors that break silently. You need automated tests to catch them.

Software Testing Guide Martin Fowler. A legend in software engineering. Read this to understand why testing matters.
Software Testing Fundamentals Practical overview of testing types and strategies.

Continuous Integration, Continuous Deployment. Automating the process of testing and shipping code. The easiest way to get started is GitHub Actions. At work you'll use whatever your company has (Azure Pipelines, Bitbucket Pipelines, Bamboo, Terraform, Jenkins, whatever), but GitHub Actions lets you practice right now without needing an enterprise job.

What is CI/CD? Clear conceptual explanation.
GitHub Actions The official docs. Start with the example workflow tutorial, then begin automating your own projects.

Choosing architecture and components to meet a goal under constraints: reliability, security, cost, performance, maintainability. This includes understanding how computers communicate over the internet. Networking, DNS, HTTP, load balancing. None of that went away because of AI. It still matters, no be small.

System Design for Beginners FreeCodeCamp. Start here if the topic is new to you.
The System Design Primer The gold mine. Used by thousands to prepare for interviews and understand real-world architecture.
Networking Basics Cisco Networking Academy. Free course on how computers communicate over the internet.
Fundamentals of Computer Networking Microsoft Learn. Free, self-paced, covers the core networking concepts.

There is no cloud. It's just someone else's computer. AWS (Amazon Web Services), Azure (Microsoft's cloud), and GCP (Google Cloud Platform) are the big three. Each has hundreds of products, each complex enough to build a career around. There is no such thing as "knowing" a cloud platform. It's a continual learning process. Start with the fundamentals course for whichever one interests you. If you're unsure, go with AWS. It was the first major cloud provider, it's the biggest, and the skills you learn there transfer to Azure and GCP.

AWS Cloud Practitioner Essentials Amazon's free beginner course for learners starting their cloud journey.
Introduction to Cloud Infrastructure Microsoft's beginner Azure course, with hands-on labs.
Google Cloud Fundamentals Google's entry-level cloud certification path, with hands-on labs.

AI Engineering is the evolution of software engineering. You're not building the models. You're taking them and building with them. RAG, MCP, vector databases, embeddings, context engineering, model evaluation. This is the capstone. Everything above feeds into it.

LLM Zoomcamp Build a RAG app and learn retrieval, evaluation, monitoring, feedback, and Docker.
Hugging Face AI Agents Course Build and benchmark agents while learning tools, orchestration, observability, and evaluation.
Hugging Face Context Course Complete six hands-on units covering skills, MCP, plugins, subagents, hooks, and a minimal agent loop.
My AI Engineering GitHub List Pick a hands-on course from my maintained collection and complete its projects before moving to another.
Getting Started With GitHub Copilot Complete GitHub Skills' interactive course by modifying a real app with Copilot and reviewing the pull request.
Hands-on With GitHub Copilot's Agents Complete the VS Code course: customize Copilot, build a feature with agent mode, test it with Playwright MCP, and create a custom agent.
Claude Code in Action Apply Anthropic's nine lessons on steering long sessions, CLAUDE.md, skills, hooks, automation, and verification in your own repository.
Codex 102 Complete 26 repository tasks covering context, MCP, tests, reviews, skills, subagents, worktrees, and automation.
GitHub Secure Code Game Fix and exploit vulnerable systems to learn secure coding, prompt injection, sandboxing, and agent security.
PortSwigger Web LLM Attacks Attack realistic LLM applications to learn excessive agency, indirect prompt injection, and data exfiltration.

Apparently, in this Brave New World of AI we're living in, we put Engineering after everything now. These are some of the hottest terms being used to describe doing stuff with AI that we've been doing for decades already in Software Engineering. But of course, you need a new term to make it sound cool. While some of these names are silly, if you're a student trying to get hired, understanding what these are and dropping them on your resume, LinkedIn, in conversations, and in interviews, can help you get ahead.

AI Engineering Building reliable applications with AI models, APIs, data, evaluations, and production software.
Prompt Engineering Writing and refining instructions, examples, and constraints so an AI model produces a useful result.
Agentic Engineering The practice of developing software with the assistance of AI agents that can both write and execute code.
Context Engineering The discipline of designing the information and tools available to an AI model while it performs a task.
Harness Engineering The discipline of building the software system of tools, rules, permissions, memory, tests, and runtime that surrounds an AI model and turns it into a working agent.
Loop Engineering Designing the process that repeatedly assigns work to agents, checks the results, records progress, and decides what happens next.
Graph Engineering Designing the relationships between agent processes by mapping how work, state, checks, branches, retries, and human decisions move through the larger system.

Use AI as your tutor. Open ChatGPT, Claude, Gemini, Microsoft Copilot, whatever chatbot you have access to. Ask it to explain a topic. When the explanation doesn't make sense, ask it to explain it differently. Keep going until it clicks. This is the single most underused learning strategy available to you right now, and it's free.