The people shaping public discourse about technology—the commentators, columnists, and pundits whose takes get widely read and shared—mostly don’t understand technology very well. They’re journalists, academics, political commentators, or cultural critics who happen to write about tech. Their actual technical knowledge is often shallow.

This isn’t just my impression. Look at who writes the think pieces about AI, cryptocurrency, social media regulation, or any other technology topic that enters public debate. The bylines are rarely engineers, computer scientists, or people with deep technical expertise. They’re generalists applying frameworks from other domains.

Sometimes this works fine. Sometimes it produces commentary that’s confidently wrong in ways that shape policy, investment, and public understanding. The dominance of non-technical voices in technology commentary has consequences worth examining.

Why Technical People Don’t Write Commentary

Part of this is a supply problem. People with deep technical expertise have other options that pay better and feel more impactful than writing commentary. Software engineers make six figures building things. Researchers focus on advancing their fields. Technical founders build companies.

Commentary writing pays okay if you’re at the top of the profession, poorly for everyone else. And the path to becoming a successful commentator requires years of building an audience while making minimal money. Most technical people have better opportunities.

There’s also a cultural mismatch. Tech culture rewards precision, getting things exactly right, showing your work. Commentary culture rewards strong positions, confident takes, compelling narratives. Technical people trained to hedge claims and acknowledge uncertainty often make boring commentators.

So the technical experts mostly stay in their lanes, building and researching. The commentary space gets filled by people with writing skills, audience-building talent, and opinions—but limited technical depth.

When Non-Technical Commentary Works

To be clear, you don’t need to be a programmer to write valuable technology commentary. Some of the best tech writing examines social implications, policy questions, or ethical issues where the relevant expertise is more about society than software.

A political analyst writing about tech regulation, an economist writing about platform markets, a sociologist writing about social media’s effects—these can all be valuable even without deep technical knowledge. The key is staying in your domain of expertise and being honest about limitations.

The problem is when non-technical commentators write confidently about technical questions they don’t understand, making assertions about how technology works, what’s technically possible, or what engineering constraints exist. This produces commentary that sounds authoritative but is often wrong.

The AI Commentary Disaster

Nowhere is this clearer than in AI commentary. The public conversation about AI is dominated by people who don’t understand machine learning, neural networks, or how these systems actually function. They’ve absorbed some terminology and high-level concepts, then extrapolate wildly.

You get commentary asserting that AI systems are “thinking” or “understanding” when they’re doing statistical pattern matching. Or claims about what AI will inevitably do based on sci-fi scenarios rather than technical reality. Or policy proposals that don’t account for how these systems are actually built and deployed.

Organizations like the Team400 team working directly with AI implementation see the gap constantly: public commentary about AI bears little resemblance to the reality of building and deploying these systems. The commentary focuses on speculative futures while missing present-tense issues that actually matter.

This shapes policy debates in unhelpful ways. Regulators and politicians read the commentary, not the technical literature. They form opinions based on what prominent commentators say, not what technical experts know. We end up with regulation addressing imaginary problems while missing real ones.

The Cryptocurrency Case Study

Crypto is another area where non-technical commentary has dominated and misled. For years, tech commentators with limited understanding of cryptography or distributed systems wrote breathless pieces about blockchain “revolutionizing everything.”

They didn’t understand the technical limitations, the tradeoffs, the specific narrow use cases where decentralization might matter versus the vast majority where it doesn’t. They just knew “blockchain = innovation” and wrote accordingly.

When the bubble collapsed, the same commentators pivoted to declaring crypto entirely worthless, again without much technical understanding. They missed the specific legitimate uses while also overestimating the technology’s broader applicability.

Technical experts had been saying “this is useful for some narrow things but massively overhyped for most applications” for years. The non-technical commentary swung between extremes because the commentators lacked the knowledge to make nuanced assessments.

Platform Regulation Commentary

The debate about regulating social media platforms is shaped largely by commentators who don’t understand how content moderation works, what algorithmic ranking actually does, or what technical constraints exist on platform design.

They make confident assertions about how platforms could “just” fix misinformation, or how changing algorithms would “obviously” improve things, without understanding the technical or practical challenges. They propose regulations that sound good in theory but would be technically impossible or create worse problems.

Meanwhile, people who actually build these systems—platform engineers, trust and safety teams, content moderation experts—are largely absent from public commentary. When they do speak, it’s usually in technical venues that policy makers and general audiences don’t read.

The result is policy debates disconnected from technical reality, proposing solutions that can’t work while missing approaches that could.

The Journalism Constraint

Part of why non-technical people dominate tech commentary is structural. Journalism values generalists who can write clearly for broad audiences. Technical experts often struggle with both—they’re specialists who communicate in jargon.

News organizations need people who can write readable commentary on deadline about whatever tech story is trending. That selects for general knowledge and writing ability over deep expertise. The expert’s expert would be slow, dense, and boring to most readers.

This isn’t necessarily wrong. There’s value in translation, making technical topics accessible. But it means the translation layer dominates the commentary, and mistranslations or oversimplifications shape public understanding more than expert knowledge does.

What’s Missing

We need a middle layer that barely exists: technical experts who can communicate clearly for general audiences. People with deep domain knowledge who’ve also developed the skills to explain things without jargon, make complex topics engaging, and write commentary that’s both accessible and accurate.

A few examples exist. Some academics, some technical founders, some engineers who’ve developed writing skills. But it’s rare, and these voices are outnumbered by non-technical commentators by orders of magnitude.

Building this middle layer would require institutions that value and reward it. Tech companies could employ more people in “technical communication” roles that include public commentary. Journalism organizations could hire more technically trained people and support their development as writers. Universities could train technical people in communication.

Instead, we mostly have the status quo: technical people stay technical, commentators stay generalists, and public understanding of technology is shaped by people who don’t deeply understand what they’re writing about.

Consequences

This matters because technology increasingly shapes everything: economics, politics, social relationships, information flow. Public understanding of how technology works affects regulation, investment, individual choices, and collective action.

When that understanding is shaped by commentary that’s technically shallow or wrong, we make worse collective decisions. We regulate the wrong things, ignore real problems, misallocate resources, and form opinions based on misunderstandings.

This isn’t an argument for technocracy or letting engineers make all decisions. Technical understanding isn’t sufficient for good policy or social outcomes. But it’s necessary, and right now it’s largely absent from public commentary.

No Simple Fix

You can’t just tell technical experts to write more commentary. Most don’t want to, and the ones who do often aren’t good at it initially. You can’t tell non-technical commentators to shut up about tech—they’re responding to real demand for accessible writing about technology topics.

What might work is building more paths for technical people to develop communication skills, more incentives for doing public-facing work, more institutions that value technical communication. And on the other side, higher standards for non-technical commentators writing about technology—more pressure to acknowledge limitations, consult experts, get technical details right.

Neither is happening at scale. So we’ll keep getting technology commentary dominated by non-technical voices, with all the limitations and distortions that creates. At least we can be aware of the gap between commentary and reality, even if we can’t close it.