Goodbye Coding!
I’m a CTO who spent 30 years writing code. That era is over. Here’s what comes next.
I wrote my first line of code in 1995 on a Commodore 64. Commodore BASIC. No IDE, no syntax highlighting — just a blinking cursor after READY. and a kid figuring out that 10 PRINT "HELLO" followed by RUN was the closest thing to magic. Over the three decades since, I’ve shipped enterprise systems, wrestled MongoDB clusters into submission, architected microservices, and debugged code at 2am because a deployment went sideways.
Last month I realised something unsettling: I hadn’t opened my code editor in three weeks. Not because I was lazy. Because I didn’t need to.
I was still shipping features. Still fixing bugs. Still building products. But the craft I’d spent thirty years mastering — the act of translating intent into syntax — was no longer the bottleneck. It wasn’t even the job anymore.
Coding, as we knew it, is dead. And I say that as someone who loved it.
What Actually Changed
Let me be precise about what I mean, because “AI writes code now” is a lazy take that misses the point.
Twelve months ago, AI could autocomplete functions and generate boilerplate. Useful, sure. A faster pair of hands. But you still needed to think in code, structure the architecture, wire the systems together, and babysit every output.
That’s not where we are anymore.
Today I describe what I want built — in plain English, with business context — and an agent goes away, reads my codebase, writes the implementation, runs the tests, and opens a pull request. Sometimes multiple agents do this in parallel across different parts of the same project.
The shift wasn’t incremental. It was a phase change. We went from “AI helps you code faster” to “AI codes, and you supervise.”
If you’re a CTO still thinking about AI as a productivity tool for your engineering team, you’re already behind.
The Two Tools That Broke the Dam
Two products crystallised this shift for me: OpenAI’s Codex and Anthropic’s Claude Code. They represent different philosophies but arrive at the same conclusion — the developer’s role is fundamentally changing.
Codex: The Parallel Workforce
OpenAI’s Codex is now a full cloud-based software engineering agent. You point it at a GitHub repo, describe what you want, and it spins up its own sandboxed environment to do the work. Multiple tasks run in parallel. It reads your codebase, understands your conventions, writes features, fixes bugs, and proposes pull requests.
The latest models — GPT-5.3-Codex — can sustain work across millions of tokens. OpenAI showed it building complete, multi-level games autonomously over the course of days, iterating on its own output without human intervention. The Codex desktop app is essentially a command centre where you supervise a team of AI agents, each working on isolated branches of your code.
This isn’t a copilot sitting in your editor. It’s a junior engineering team that works 24/7, doesn’t get tired, and scales horizontally on demand.
What made me sit up was the introduction of Skills and Automations. Skills let Codex go beyond code generation into prototyping, documentation, and code understanding — aligned with your team’s standards. Automations mean Codex can work unprompted: triaging issues, monitoring alerts, handling CI/CD. The agent doesn’t wait for you to ask. It picks up work.
Claude Code: The Senior Engineer in Your Terminal
Claude Code takes a different approach. It lives in your terminal. It understands your codebase. It reasons about architecture.
Where Codex feels like managing a team, Claude Code feels like working alongside a very senior engineer who happens to have perfect recall of every file in your project.
Claude Code was released a year ago as a command-line tool and quickly became what many consider the best AI coding assistant available. Enterprise adoption grew 5.5x in its first few months. Microsoft, Google, and — ironically — even OpenAI employees were using it. That’s not marketing spin. That’s a signal about where actual engineering work is getting done.
What sets Claude Code apart is depth. Anthropic’s Opus 4.6 model recently wrote a working C compiler in Rust from scratch — one capable of compiling the Linux kernel. Sixteen agents collaborating, costing $20,000, producing something that would have taken a team of specialists months. The compiler isn’t optimised, but it works. That’s the part that matters.
Then there’s Claude Code Security, launched weeks ago. It doesn’t just scan for known vulnerability patterns. It reasons about your codebase like a human security researcher — tracing data flows, understanding component interactions, finding bugs that had gone undetected for decades in open-source libraries. Over 500 previously unknown zero-day vulnerabilities found in its initial runs.
The trajectory is clear: Claude Code is evolving from “a tool that writes code” to “a tool that does engineering.”
What the CTO Role Looks Like Now
Here’s what I’ve noticed changing in my own day-to-day.
I think in systems, not syntax. My value is in deciding what to build, why, and how the pieces fit together. The implementation is increasingly delegated. When I describe a feature to Claude Code or spin up a Codex task, I’m operating at the architecture and product level. The code is a byproduct.
I review more than I write. Pull requests from AI agents are a daily occurrence. My job is to evaluate whether the approach is right, whether edge cases are handled, whether the solution fits the broader system. I’m a code reviewer, not a code writer. This is a fundamentally different skill.
Speed compounds differently. When you can run five agents in parallel — one building a feature, one writing tests, one fixing a bug, one updating documentation, one handling a migration — your throughput doesn’t increase linearly. It explodes. A task that would have taken my small team a week now takes an afternoon of supervision.
The hiring calculus has flipped. I used to hire for “can this person write clean code?” Now I hire for “can this person specify clearly what needs to be built, evaluate whether AI output is correct, and architect systems that are agent-friendly?” That’s a very different profile. Strong communicators with deep domain knowledge are suddenly more valuable than fast typists with encyclopaedic framework knowledge.
This Isn’t Theory. I’m Living It Across Three Companies.
I don’t say any of this from the sidelines. I’m a self-funded founder running three companies simultaneously from Sydney, and every one of them is being shaped by this shift.
SearchFit: Building an AI Product With AI
SearchFit.ai is an AI search visibility platform — it helps brands understand and optimise how they appear across AI-powered search engines like ChatGPT, Perplexity, and Google’s AI Overviews. The old SEO playbook is dying. When answers come from AI, not blue links, visibility means something completely different. SearchFit tracks that new reality.
Here’s the thing: I’m building an AI product almost entirely with AI.
The SearchFit codebase is 25% written by Claude Code and Codex agents. Feature development that would have required a team of three or four engineers is handled by me directing agents — describing what I need, reviewing the pull requests, and iterating on the architecture. Database schema design, API endpoints, Shopify App Store integrations, pricing logic, documentation — agents handle the implementation. I handle the product decisions.
This is what “goodbye coding” looks like in practice. With a very small technical team we are shipping a SaaS product at the pace of a funded startup, because the agents have replaced the team I would have needed to hire. The economics of building software have fundamentally changed. You don’t need a seed round to build a real product anymore. You need domain expertise, architectural judgement, and the ability to direct AI agents effectively.
Capitaly: Agentic Sales and Pipeline Building
Capitaly.ai is an AI-powered capital raising platform for founders. It connects founders with investors, helps them prepare for raises, and builds the relationships that lead to funded rounds.
What’s changed the game here is agentic pipeline building. Traditional sales and BD meant a human manually researching leads, crafting outreach, following up, updating CRMs, and nurturing relationships across dozens of touchpoints. It was labour-intensive and didn’t scale — especially for a bootstrapped company.
Now, AI agents handle the heavy lifting of the pipeline. Researching investor fit. Personalising outreach at scale. Monitoring signals that indicate when a fund is actively deploying. Following up with context-aware messages that feel human because the agent understands the full history of the relationship.
The human role — my role — is strategy. Which investor segments to target. What the value proposition is for each audience. When to push and when to nurture. The agents execute the playbook. I write the playbook.
This is the pattern I see emerging across every B2B company: agentic sales isn’t about replacing salespeople. It’s about giving one person the pipeline capacity of a team of ten. The founders who figure this out first will have an absurd advantage in 2026.
What This Means If You’re a Developer
I’m not going to sugarcoat this. If your entire value proposition is “I write code,” you’re in trouble.
But here’s the thing most doom-and-gloom takes miss: the demand for software hasn’t decreased. It’s exploded. Every business wants custom tools, automations, internal platforms, and AI-powered products. The constraint was never ideas — it was implementation capacity. AI just removed the constraint.
What’s valuable now:
Understanding the problem space. AI can write any code you describe. The hard part is describing the right thing. Product thinking, user empathy, domain expertise — these are the new premium skills.
Architectural judgement. AI agents can build components, but someone needs to decide how those components fit together, what the data model looks like, where to draw service boundaries, and what trade-offs to accept. This requires experience that no model currently has.
Taste. This sounds vague but it’s not. Knowing when a solution is over-engineered, when an abstraction is premature, when the simple approach is the right one — that’s taste. AI generates options. Humans choose wisely.
Verification and trust. Every line of AI-generated code needs to be reviewed. Security implications need to be understood. Edge cases need to be caught. The more code AI writes, the more critical review becomes.
The Uncomfortable Truth About “Vibe Coding”
Something interesting happened over the recent holiday period. Claude Code went viral with non-programmers. People with zero coding experience were building apps, launching tools, shipping products. They called it “vibe coding” — just describe what you want and let the AI figure it out.
Part of me loves this. The democratisation of software creation is genuinely exciting. People who couldn’t participate before now can.
But part of me worries. Because vibe-coded apps are like houses built without architects. They might look fine. They might even work. But when the load increases, when edge cases surface, when security matters — the foundations crack.
The gap between “it works on my machine” and “it works in production at scale” is still enormous. And that gap is where engineering expertise lives. AI hasn’t eliminated that gap. If anything, by making it trivially easy to create software, it’s made the gap more dangerous.
Where This Goes Next
I think we’re about 12 months away from a world where:
Most routine software engineering tasks are fully automated
AI agents handle the complete development lifecycle — from issue triage to deployment to monitoring
The “10x engineer” isn’t someone who codes ten times faster, but someone who can effectively supervise ten AI agents simultaneously
Non-technical founders can build and ship MVPs without writing a line of code (this is already happening)
Engineering teams shrink in headcount but grow in output by an order of magnitude
Anthropic just announced Claude Cowork — essentially Claude Code for non-developers. Private plugin marketplaces. Deep integrations with Google Drive, Gmail, DocuSign, and dozens of other enterprise tools. The direction is unmistakable: AI agents that don’t just write code, but do knowledge work end-to-end.
OpenAI’s Codex app is heading the same way — from coding tool to autonomous software development platform.
PADISO: Running Agentic Workloads for the Enterprise
The third company in the portfolio is PADISO.ai — an AI and automation consultancy. While SearchFit and Capitaly are products I’m building, PADISO is where I see what the market actually needs right now. And what the market needs is help running agentic workloads.
Most companies aren’t struggling with “should we use AI?” anymore. That debate is over. They’re struggling with “how do we actually deploy AI agents that do real work, reliably, at scale, without breaking everything?”
That’s the gap PADISO fills. We design and deploy agentic workflows for clients — everything from automated revenue tracking across booking platforms to Power BI dashboards that update themselves, from email automation pipelines to full process orchestration using tools like N8N, Claude Code, and custom agent architectures.
What I’ve learned running PADISO is that the hard part isn’t the AI. The hard part is the integration. Real businesses have messy data, legacy systems, workflows that evolved organically over decades, and teams that don’t think in terms of prompts and agents. The value isn’t in showing a client that Claude can write code. It’s in wiring an agent into their actual operational reality — their booking systems, their CRMs, their reporting stack — and making it work Monday through Friday without human intervention.
This is where the “goodbye coding” thesis gets real for enterprises. The companies that will win aren’t the ones that hire more engineers. They’re the ones that learn to orchestrate agentic workloads across their existing infrastructure. The CTO of 2026 isn’t managing a team of developers. They’re managing a fleet of agents, with a small team of humans who understand both the business domain and the AI capabilities well enough to keep everything running.
PADISO generates around $20K a month in revenue — profitably, without VC funding — and that revenue funds the product bets like SearchFit and Capitaly. It’s the consultancy-funded model: use services revenue to bankroll products. But increasingly, the consulting work itself is being done with AI agents. I’m using AI to build the AI consultancy that funds the AI products. If that sentence doesn’t capture the moment we’re in, I don’t know what does.
The Real Goodbye
I’m not saying goodbye to building things. I’m saying goodbye to the specific act of sitting in an editor, holding the entire context of a system in my head, and translating logic into syntax character by character.
That was beautiful work. It was craft. From Commodore BASIC to TypeScript, from line numbers to microservices — every era taught me something. I’ll miss the flow state, the satisfaction of an elegant solution, the quiet pride of a clean diff.
But I won’t miss the repetition. The boilerplate. The context-switching between thinking about what to build and figuring out how to express it. The yak-shaving.
What I do now is closer to what I always wanted to do: think about hard problems, make consequential decisions, and build things that matter. The code is just a detail.
Goodbye coding. Hello engineering.
I’m Kevin — a serial entrepreneur, CTO-turned-CEO running three companies from Sydney. I write about the intersection of technical leadership and AI-first building. If this resonated, subscribe for more.