In 2011, Marc Andreessen declared that “software is eating the world.” Fifteen years later, it’s not just software. It’s the engineering mindset itself.
Ian Vanagas at PostHog recently coined the term “the engineeringification of everything” — the spread of engineering tools, skills, and identity into non-engineering roles. Designers have become design engineers. Sales reps have become GTM engineers. And marketers? Marketers are next.
It’s a fundamental shift in what the job requires.
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What engineeringification actually means
Engineeringification isn’t about marketers learning to code (though some will). It’s about adopting the engineering approach to work:
- Hypothesise, test, measure, iterate. Replace “launch and hope” with learn and adapt.
- Build systems, not campaigns. Design growth loops that compound, not one-off blasts that decay.
- Use data to make decisions. Let evidence guide strategy, not the highest-paid person’s opinion.
- Ship fast, learn faster. Favour speed and testing velocity over perfection.
As Vanagas puts it: “Tools change skills, skills reshape identity, and identity demands new tools.” It’s a self-reinforcing loop — and marketing is entering it.
AI is the catalyst
The engineering mindset has been creeping into marketing for years. Agile methods, experimentation frameworks, and prioritisation models all come from product and engineering teams.
AI changed that.
We’re seeing it first-hand across our client work — in website builds, SEO, paid advertising, and CRO. Take SEO as an example. We now apply a bronze-silver-gold data architecture to keyword research and content pipelines — a pattern borrowed directly from data engineering. A year ago, that would have required a data team. Today a marketer with the right tools and an engineering mindset can build it in an afternoon. That’s engineeringification in practice.
Large language models have made complex tools accessible to non-specialists. Platforms like n8n, Clay, and Claude Code let marketers build workflows, automate processes, and ship solutions that previously required an engineering team. This is why the shift is happening now, not in five years.
The barrier to engineering-grade work used to be technical skill. AI has collapsed that barrier. A marketer with the right mindset and an LLM can now do what once required a developer, a data analyst, and a project manager.
This is what context engineering is about — understanding how to structure information so AI can do useful work. It’s an engineering skill — and every marketer needs it.
What engineeringified marketing looks like
You run experiments, not campaigns
Engineering teams ship code, measure the result, and iterate. Engineeringified marketing does the same. You form a hypothesis, design a minimum viable test, run it, analyse the results, and decide what to do next.
This isn’t A/B testing everything — sometimes a scalpel isn’t the right tool. It’s about building a culture of experimentation where learning is the primary output, not content.
You build systems, not deliverables
A campaign is a one-time event. A marketing system is an engine. Engineeringified marketers think in growth loops, input and output metrics, and constraints. They ask: “How do I build something that produces results without me the second time?”
This is the difference between a factory and a laboratory. Factories produce outputs. Laboratories produce knowledge. The best marketing teams are both.
You instrument everything
Engineers don’t ship code without monitoring. Engineeringified marketers don’t launch anything without a tracking plan. They use structured event taxonomies, set guardrail metrics, and understand the pitfalls of their analytics tools.
You can’t improve what you can’t measure. And you can’t measure what you haven’t instrumented.
You prioritise like an engineer
Engineering teams use backlogs and scoring models to decide what to build next. Engineeringified marketers do the same — using frameworks like RICE, ICE, or HIPE to rank their work by expected impact, not by who shouted loudest in the last meeting.
You design for automation
The shift from operator to architect is core to engineeringification. Instead of manually executing every workflow, you design systems that run themselves — using AI agents, orchestration tools, and automation platforms like n8n, Make, or Zapier.
This is democratisation
“The line between technical and non-technical work isn’t disappearing, it’s being redrawn.”
— Ian Vanagas, The Engineeringification of Everything
Some will argue that giving marketers engineering tools dilutes expertise. That “real” engineering requires formal training and accreditation. That this is deprofessionalisation dressed up as progress.
They’re wrong.
Marketing has been gatekept by specialists for too long — SEO experts, analytics consultants, automation vendors. Engineeringification gives every marketer more leverage. It doesn’t replace depth. It makes depth accessible.
The marketer who can set up a data architecture, run a statistically valid experiment, build an automated workflow, and evaluate AI outputs isn’t less of a marketer. They’re a more capable one.
This is what first-principles thinking looks like in practice — stripping away the layers of abstraction between a marketer and the systems that drive growth.
You don’t need a new title. You need a new mindset.
The PostHog article tracks how identity shifts follow skill shifts — designers become “design engineers,” salespeople become “GTM engineers.” Marketing will see the same pressure to relabel.
But the label matters less than the substance. You don’t need to call yourself a growth engineer or a generalist marketer. You need to adopt the builder’s mindset:
- Start from the problem, not the channel. Engineers solve problems. Marketers too often start from “we need a blog post” instead of “we need to increase activation.”
- Build in public, iterate fast. Ship a minimum viable experiment, learn, and ship again. Two-way door decisions don’t need committees.
- Measure what matters. Know your north star metric. Decompose it into driver metrics. Ignore vanity numbers.
- Design for compounding. Campaigns are linear. Growth systems are exponential. Build the system.
- Allocate time to learn. Google gave engineers 20% time. Your marketing team needs dedicated experimentation time too.
The shift is already underway
Marketing is engineeringifying whether individual marketers embrace it or not. AI is making engineering tools accessible. Companies are hiring for engineering-adjacent marketing skills. The growth marketing process already looks more like a software development lifecycle than a traditional campaign calendar.
Kyle Poyar’s Growth Unhinged recently explored a related idea — becoming an AI-native operator. But engineeringification goes further than AI adoption. Learning to use AI tools is table stakes. The deeper shift is in how you think — hypotheses over hunches, systems over campaigns, measurement over gut feel. AI is the catalyst, but the engineering mindset is the real unlock.
“Software is eating the world.”
— Marc Andreessen, Why Software Is Eating the World (2011)
Software ate the world. Now the engineering mindset is eating every role in it — marketing included. The marketers who lean in, who think like builders, who treat growth as a system to be engineered rather than a campaign to be launched — they’ll be the ones who win.
The tools exist. The playbooks are written. What’s left is the willingness to use them.