Content Is Code - Matt Palmer, Conductor
Description
Code has become the fastest medium for producing technical content, but the most important piece isn't a secret agent skill or a magical framework: its the same thing that makes a great developer experience. Speakers: - Matt Palmer (Conductor): Matt Palmer is a DevRel & product leader focused on AI devtools, developer education, and making complex concepts accessible. Away from the keyboard, you can find him lifting, hiking, riding motorcycles, or caring for plants. X/Twitter: https://x.com/mattppal LinkedIn: https://www.linkedin.com/in/matt-palmer/
Summary
Generated by gpt-5.6-terraAt-a-Glance
- Verdict: Skim
- Core thesis: As AI makes code-generated creative assets inexpensive, the competitive advantage in technical content shifts from production skill to the structured product data, design systems, documentation, and operational rigor needed to generate accurate assets reliably.
- Why it matters: The same source-of-truth and workflow-discipline problems that constrain AI agents also determine whether product documentation, changelogs, demos, and marketing can be automated without becoming inaccurate or generic.
- Best use: Use this as a concise strategic framing for building a content-engineering layer around product development; it is not an implementation tutorial.
Executive Summary
Matt Palmer argues that technical communication is becoming a software-output problem rather than a purely manual content function. Documentation, changelogs, release emails, product tours, screenshots, video overlays, and full demo videos can increasingly be created from code and product data, with AI reducing the cost of making those assets.
His central distinction is that code is now cheap, but structure is not. The limiting factor is no longer merely access to engineering or generative models; it is whether an organization maintains clean product information, design tokens, accurate internal documentation, disciplined PR metadata, and clear separation of concerns. Without those inputs, AI produces generic, inconsistent output rather than reliable product communication.
Palmer uses a Conductor product tour built in React and Remotion as a proof point: he recreated a product surface and scripted a sample workflow as code. He acknowledges that the resulting flow was still buggy and imperfect, but treats it as an early example of a normal future workflow rather than a finished standard.
The forward-looking implication is a new content-engineering function: teams should make the codebase and associated operational artifacts a trustworthy source of truth, then build declarative pipelines that turn changes into documentation, walkthroughs, screenshots, and updates. The talk is directionally useful for AI-enabled product operations, though it provides few concrete system designs, governance mechanisms, or quality metrics.
Key Takeaways
- Claim: Technical content is broad product communication, and much of it can be generated from product code and structured product-change data. | Evidence: Palmer includes documentation, changelogs, release emails, product marketing, homepage tours, screenshots, video overlays, and DevRel assets; he notes that a reliable changelog requires an accurate diff of what actually shipped. | Implication: Treat content automation as an extension of product operations and release management, not as an isolated marketing-generation workflow.
- Claim: AI has moved creative production into a third era where code-based assets are cheap to create, making code or code generation the fastest medium for many high-fidelity assets. | Evidence: Palmer contrasts manual handcrafted content, an era when engineering was costly and scarce, and the current AI era in which users pay providers such as Anthropic or OpenAI to generate videos, documentation, websites, and motion graphics. | Implication: The opportunity is not simply to deploy a model for each asset type, but to establish reusable code-based asset primitives and controlled generation paths. | Caveat: Cheap generation does not make outputs good; Palmer explicitly says generated videos, documentation, websites, and graphics are often poor without informed system design and review.
- Claim: Structure—not generic 'taste'—is the scarce capability that determines whether AI-generated content is polished, consistent, and accurate. | Evidence: He names consistent brand guidelines, design tokens, a structured codebase, front-end/back-end/design-token separation, clean tagged PRs with descriptions, knowing whether a change is a feature, bug fix, or revert, and accurate internal documentation. | Implication: Before scaling content agents, invest in source-of-truth hygiene: release taxonomy, product metadata, reusable design systems, and accessible operational documentation. | Caveat: The thesis is a strategic assertion supported by experience and examples, rather than comparative performance data or a measured case study.
- Claim: AI rewards conscientiousness and organizational excellence more than raw technical virtuosity alone. | Evidence: Palmer defines conscientiousness as being meticulous, careful, and guided by professional duty, and argues that the key is the care taken to make outputs match expectations rather than being the most technically skilled person in the room. | Implication: Human review and operating standards remain core leverage: teams need clear acceptance criteria, ownership, and disciplined maintenance of the context that agents consume.
- Claim: Code can serve as the source of truth for product communication when content is shifted left into the development process. | Evidence: Palmer's example is a Conductor product tour built entirely with React and Remotion, recreating the product surface and walking through a sample user flow; he describes all ancillary presentation assets as React or TypeScript. | Implication: Product tours and demos can become versioned, testable artifacts, but should be managed like software—with validation against the live product and explicit maintenance ownership. | Caveat: His own demo was 'a little buggy' and its flows were not fully complete, illustrating that coded content still has software-quality and maintenance burdens.
- Claim: A 'content engineer' role or capability will emerge to operate declarative pipelines that generate product communication from structured inputs. | Evidence: Palmer forecasts robust pipelines for content walkthroughs, documentation, screenshots, and product updates, following what he calls the 2026 rise of the 'creative technologist' in DevRel. | Implication: Ken should view content engineering as a practical cross-functional capability spanning product telemetry, release operations, design systems, and AI orchestration—not merely a new title. | Caveat: The forecast is aspirational and does not specify the required data contracts, evaluation framework, approval process, or integration architecture.
Detailed Brief
The three-era model of content production
- Claims: In the handcrafted era, content output is bounded directly by available time, money, and specialized human skill.; In the expensive-code era, engineering becomes the bottleneck because high-quality websites, documentation systems, and interactive assets require scarce professional developers.; In the cheap-code era, generative AI changes unit economics, allowing individuals without deep front-end backgrounds to work through code-generated assets.
- Evidence: Palmer describes hiring agencies or specialists for manually produced motion-graphics-like work.; He characterizes the prior period as one where a good website required a front-end engineer because low-code alternatives were inadequate.; Although his background is data engineering and Python, he says he now sees TypeScript, React, CSS, and HTML as the best route to high-fidelity ancillary assets.
- Caveats: Lower creation cost can shift bottlenecks into quality assurance, product fidelity, asset maintenance, and governance rather than eliminate them.; The presentation does not compare code-first generation against modern design or no-code systems for specific content categories.
- Implications: Capability-building should focus on reusable system components and reliable inputs, rather than treating each creative deliverable as a one-off production task.; Teams that previously lacked dedicated creative engineering may be able to prototype quickly, but production reliability still depends on operational maturity.
What a content-from-code pipeline must be grounded in
- Claims: A changelog can only be authoritative if the organization can identify the actual product diff.; Downstream communications such as periodic user emails are summaries of structured change information, not independent writing tasks.; Agents cannot reliably compensate for missing documentation or undefined internal practices.
- Evidence: Palmer contrasts an accurate product diff with the manual alternative of someone combing through pull requests to infer what shipped.; He states that even strong agents will not know how to solve organizational problems without documentation or skills.
- Caveats: The talk does not address access controls, sensitive-change filtering, legal review, or mechanisms to prevent an automated public asset from exposing unfinished or restricted functionality.
- Implications: A content automation program needs explicit release-state data and human approval boundaries, especially when converting engineering records into external communication.; The highest-value initial automation may be internal structured release intelligence, which can then safely feed external assets after review.
Notable Concepts & Terms
- Content as technical communication: Palmer defines content as communicating the product problem solved and product changes, extending beyond conventional DevRel or marketing formats.
- Shift left for content: Content should originate closer to code, product changes, and development workflows rather than be reconstructed manually after shipping.
- Content engineering: The emerging capability of building code-driven, declarative pipelines that turn structured product inputs into communication assets.
- Structured source of truth: The combination of well-maintained code, product-change metadata, design tokens, and internal documentation that makes accurate generation possible.
- Design tokens: Reusable, codified design values that allow generated product surfaces and creative assets to remain brand-consistent.
- Remotion: A React-based framework Palmer uses to create a code-driven Conductor product-tour video and demonstrate programmable video production.
- Conscientiousness: Palmer's preferred explanation for AI-era quality: meticulous operational care and adherence to standards rather than an abstract notion of taste.
- Creative technologist: The DevRel-adjacent role Palmer sees as a precursor to the more pipeline-oriented 'content engineer.'
Operator Notes / Why Ken Should Care
- Audit whether every deployable product change has machine-readable ownership, release status, customer impact, feature/bug/revert classification, and approval state before attempting automated changelogs or release communications.
- Create a versioned design-token and asset-component layer that content agents can call when producing product tours, screenshots, diagrams, or videos.
- Pilot one narrow content-from-code workflow—such as an internal release digest generated from merged PR metadata—and measure factual accuracy, review burden, and time-to-publish before automating external communications.
- Require live-product validation for coded demos and tours; do not let generated recreations become a source of inaccurate product claims.
- Define explicit human approval and access-control gates for any pipeline that transforms engineering artifacts into public-facing content.
Source/Metadata
- Title: Content Is Code - Matt Palmer, Conductor
- Transcript words: 3550
- Duration seconds: 653
- Timestamp note: No timestamps or chapters were provided. The supplied transcript contains a near-verbatim repeated second pass of the talk.
Transcript
So if you've ever created content, you know it can be hard. And what's often most hard is choosing a medium for how you'd like to communicate. Do you write a blog post? Do you create a video? Do you go out and speak at a conference? And even of these things, you're likely to select something that you're good at or you have experience doing. But the idea I want to present to you today is that increasingly, code is becoming the way that we communicate. Now, this might seem obvious in 2026, but I want to point out that even a couple of years ago, this was wild because the only people that were writing code to create content were professional software engineers. And often, professional software engineers are spending their time doing mostly engineering and are not getting that deep on content. Now, what do I mean when I say content? I'm talking mostly about technical communication. And that could be documentation. It could be things like change logs, emails, product marketing. It could be video, which I create a lot of. And it could be other things associated with a typical DevRel motion, but it's not restricted to DevRel, right? Content, or technical communication, is the act of communicating what problem a product really solves. And that can be done by anybody. That might mean really complete, accurate documentation that covers every part of the D-Taxes framework. It could be change logs shipped timely with robust assets that cover all areas of a product. This is something that's often overlooked. This is actually really hard because, to have an accurate change log, you have to have an accurate diff of your product. Or you could just have someone that spends a lot of their time combing through PRs to try to figure out what was shipped. It could be things like timely product updates, emails, which follow from a change log, right? If you understand what changed, you can then summarize that over time for your users. It could be something as simple as a homepage product tour instead of a static screenshot. These are all ways that we're communicating products. We're communicating what this thing does or how it's changed via either written content, via code, via distinct assets. It could also be something like video overlays, right? Something like adding a bit of engaging material to the content that you create. But increasingly, where I think this is headed is entire product surfaces and entire videos generated via code. And in front of me, I have a product tour for Conductor that was built entirely with React and Remotion. So I recreated the product surface. I used a Remotion scene to recreate the product and do a full walkthrough of a sample user flow. Now, it's not perfect. It's a little buggy, and the flows aren't quite there yet, if I'm being honest, but I think we're moving towards a world where this is normal. So my name is Matt. I was previously a data engineer, then I led DevRel at Replit, and now I lead developer experience at Conductor. And today, I want to talk about how content is shifting left to code as the source of truth. And so I think there are three distinct eras we can think about here. Era one, the handcrafted era of content. Era two, an era where code existed, but it was quite expensive. And era three, where code exists, but it's very cheap. And that's where we are today with AI. So in the handcrafted era, everything is manual. The only levers we really have are time and money and skill. And so if I want something, I either need to create it myself, but there are only 24 hours in a day. So likely, I'm going to go out and seek an expert, someone that does know how to create this thing. Maybe that's an agency or someone who creates these assets professionally, if I'm thinking about motion graphics, for example. Now, in era one, when we had expensive code, code and cloud are mature assets. These are things that have existed for a while and most professional software engineers know how to use. Engineering is now the bottleneck. This is like Zerp era, everybody's paying a lot for engineers. And to get anything done, you really need professional software engineers. So you'd better pay someone, maybe not an agency, but an engineer. And that included things like robust documentation or even just a website, right? Back in the day, it's hard to imagine before AI, you wanted a website, a good website, you needed a front-end engineer, or a low-code website service, but those weren't any good to begin with. So you needed a professional engineer to have a really nice website. And what that meant was that content is really not the priority, right? Because if you're a professional engineer, you have better ways of spending your time. And I think this is reflected by libraries like Remotion, frameworks like Remotion, not proliferating the way that they are today. Because really, who has the time and energy to put into these things? There aren't a ton of what I'd call content engineers. Now, in era three, most of these things, you're not paying a human, you're paying Anthropik, or OpenAI, or whoever, right? You can create videos with Claude, you can create documentation with Claude, you can design websites with Claude, and you can create motion graphics with Claude. Now, the problem is that not all of these things are good, right? We'll talk about that later. But if you know what you're doing, if you have an engineering mindset, if you understand the systems, you can create high-quality assets with these systems. And I'm going to talk about how to do that in the rest of this presentation. So today, code is cheap. And we talked about code as communication. So code is communication. The fastest way to build an asset today is through code. And it's not just to build software, right? It's really to build anything. If I want to prototype a video, a website, slides, really any asset, any creative thing that I can think of, images, video, the fastest way to do that is going to be through code or code generation. And I was thinking about this presentation, I was thinking about how I was going to create all the assets for this presentation. And I realized that my favorite medium is actually TypeScript. Aside from recording myself talk or writing, every ancillary asset is React or TypeScript. And I just want to take a moment because that is the most insane statement to hear myself say, if I was thinking about this two or three years ago, right? I don't even really know how to write that good of TypeScript. My background's in data engineering. I come from Python land. Well, I better learn TypeScript. I better learn React because the best way for me to get these high-fidelity assets is React, TypeScript, CSS, and HTML. And that's wild. That's wild to say. So if code is inexpensive, what is the expensive thing? Now, you might think I'm going to say taste here. I'm not going to say taste because everybody says taste. Structure is expensive. This is maybe a bit of a contrarian statement here because the hard thing is maintaining a very structured code base, maintaining brand guidelines and keeping those guidelines consistent, keeping design tokens in your project, separating even front-end code from back-end code from these design tokens, merging really clean PRs, which nobody does, right? Tagging PRs, PR descriptions, knowing what's a feature and what's a bug fix, knowing if you reverted a PR. How many organizations do this? I've worked at a number of organizations, none, right? And maintaining accurate internal documentation so that anybody can accomplish anything. Even if you have really good agents, they're not going to know how to solve these problems if they don't have documentation or skills, right? And so structure often is the difference between AI, purple gradient, slob, right? And something that looks professional and polished. And I would even go a little bit further. And I would say that this is conscientiousness. And the dictionary definition for conscientiousness is the quality of being meticulous, careful, and guided by a strong sense of moral or professional duty. And so it's less about software engineering skill today. It's less about the skill of being able to create these assets and more about the meticulousness and care given to making sure that an outcome matches your expectations. And that increasingly, with each model generation, is not predicated on being the smartest person in the room or being the person in the room with the most technical skill. So what does AI reward? AI rewards conscientiousness. AI rewards organizational excellence. AI rewards structure. And ultimately, these are the things that go into good AI skills. There's a lot of not very good AI skills out there. And most of them are just generated without any regard for what's in their contents or how they're structured. And so all the assets that I showed you at the beginning of this video are a byproduct of design tokens, structured code, structured assets, and they get exponentially harder to create when we lack those things. And so code, again, is communication. And we're seeing a shift-left movement for content where content is moving to code. And if code is the source of truth, if our code base is the source of truth, we have to have a structured source of truth in order to create content from code. In order to communicate, we need structure and conscientiousness around the way that we create code. And again, this only works with organizational excellence. And so I think what we'll see in 2026 and the years beyond is that the highest-performing, the best-communicating teams are the ones that are able to instill discipline and rigor into the process of creating software and then shift their content left towards the code through content engineering. And so 2026, I think, was the year of the creative technologists, at least in the DevRel space. This is the term that got thrown around a lot. I think 2027 is the year of the content engineer. And I'll close on that because I think what we're going to see next year are declarative and robust content pipelines capable of producing content walkthroughs, capable of producing documentation, screenshots, product updates. All of the things that were really manual can now be created through code, through React, and ultimately because of AI. Again, I'm Matt with Conductor. Thanks for sticking around for my talk. I'll catch you next time. Peace. that were writing code to create content were professional software engineers. And often professional software engineers are spending their time doing mostly engineering and are not getting that deep on content. Now, what do I mean when I say content? Well, I'm talking mostly about technical communication. And that could be documentation. It could be things like change logs, emails, product marketing. It could be video, which I create a lot of. And it could be other things associated with a typical DevRel motion, but it's not restricted to DevRel, right? Content or technical communication is the act of communicating what problem a product really solves. And that can be done by anybody. That might mean really complete, accurate documentation that covers every part of the D-Taxes framework. It could be change logs shipped timely with robust assets that cover all areas of a product. This is something that's often overlooked. This is actually really hard because to have an accurate change log, you have to have an accurate diff of your product. Or you could just have someone that spends a lot of their time combing through PRs to try to figure out what was shipped. It could be things like timely product updates, emails, which kind of follow from a change log, right? If you understand what changed, you can then summarize that over time for your users. It could be something as simple as a homepage product tour instead of a static screenshot. These are all ways that we're communicating products. We're communicating what this thing does or how it's changed via either written content, via code, via distinct assets. It could also be something like video overlays, right? Something like adding a bit of engaging material to the content that you create. But increasingly where I think this is headed is entire product surfaces and entire videos generated via code. And in front of me, I have a product tour for Conductor that was built entirely with React and Remotion. So basically I recreated the product surface. I used a Remotion scene to recreate the product and do a full walkthrough of a sample user flow. Now it's not perfect. It's a little buggy and the flows aren't quite there yet, if I'm being honest, but I think we're moving towards a world where this is normal. So my name is Matt. I was previously a data engineer, then I led DevRel at Replit, and now I lead developer experience at Conductor. And today I want to talk about how content is shifting left to code as the source of truth. And so I think there are three distinct eras we can think about here. Era one, the handcrafted era of content. Era two, an era where code existed, but it was quite expensive. And era three, where code exists, but it's very cheap. And that's where we are today with AI. So in the handcrafted era, right, everything is manual. The only levers we really have are time and money and skill. And so if I want something, I either need to create it myself, but they're only 24 hours in a day. So likely I'm going to go out and seek an expert, someone that does know how to create this thing. Maybe that's an agency or someone who creates these assets professionally. If I'm thinking about like motion graphics, for example. Now, host era one, when we had expensive code, code and cloud are mature assets. These are things that have existed for a while. And most professional software engineers know how to use. Engineering is now the bottleneck. This is like Zerp era, everybody's paying a lot for engineers. And to get anything done, you really need professional software engineers. So you'd better pay someone, maybe not an agency, but an engineer. And that included things like, you know, robust documentation or even just a website, right? You back in the day, it's hard to imagine before AI, you wanted a website, a good website, you needed a front end engineer, or like a low code website service, but those weren't any good to begin with. So you needed a professional engineer to have a really nice website. And what that meant was that content is really not the priority, right? Because if you're a professional engineer, you have better ways of spending your time. And I think this is reflected by libraries like Remotion, frameworks like Remotion, not proliferating the way that they are today. Because really, who has the time and energy to put into these things? There aren't a ton of what I'd call content engineers. Now, in era three, most of these things, you're not paying a human, you're paying Anthropik, or OpenAI, or whoever, right? You can create videos with Claude, you can create documentation with Claude, you can design websites with Claude, and you can create motion graphics with Claude. Now, the problem is that not all of these things are good, right? We'll talk about that later. But if you know what you're doing, if you have an engineering mindset, if you understand the systems, you can create high quality assets with these systems. And I'm going to talk about how to do that in the rest of this presentation. So today, code is cheap. And we talked about code as communication. So code is communication. The fastest way to build an asset today is through code. And it's not just to build software, right? It's really to build anything. If I want to prototype a video, a website, slides. I mean, really any asset, any creative thing that I can think of, images, video, the fastest way to do that is going to be through code or code generation. And I was thinking about this presentation, I was thinking about how I was going to create all the assets for this presentation. And I realized that my favorite medium is actually TypeScript. Aside from recording myself talk or writing, every ancillary asset is React or TypeScript. And I just want to take a moment because that is the most insane statement to hear myself say, if I was thinking about this two or three years ago, right? I don't even really know how to write that good of TypeScript. You know, my background's in data engineering. I come from Python land. Well, I better learn TypeScript. I better learn React because the best way for me to get these high fidelity assets is React, TypeScript, CSS, CSS, and HTML. And that's wild. That's wild to say. So if code is inexpensive, what is the expensive thing? Now, you might think I'm going to say taste here. I'm not going to say taste because everybody says taste. Structure is expensive. This is maybe a bit of a contrarian statement here because the hard thing is maintaining a very structured code base, maintaining brand guidelines and keeping those guidelines consistent, keeping design tokens in your project, separating even front-end code from back-end code from these design tokens, merging really clean PRs, which nobody does, right? Tagging PRs, PR descriptions, knowing what's a feature and what's a bug fix, knowing if you reverted a PR. How many organizations do this? I've worked at a number of organizations, none, right? And maintaining accurate internal documentation so that anybody can accomplish anything. Even if you have really good agents, they're not going to know how to solve these problems if they don't have documentation or skills, right? And so structure often is the difference between AI, purple gradient, slob, right? And something that looks professional and polished. And I would even go a little bit further. And I would say that this is conscientiousness. And the dictionary definition for conscientiousness is the quality of being meticulous, careful, and guided by a strong sense of moral or professional duty. And so it's less about software engineering skill today. It's less about the skill of being able to create these assets and more about the meticulousness and care given to making sure that an outcome matches your expectations. And that increasingly with each model generation is not predicated on being the smartest person in the room or being the person in the room with the most technical skill. So what does AI reward? AI rewards conscientiousness. AI rewards organizational excellence. AI rewards structure. And ultimately, these are the things that go into good AI skills. There's a lot of not very good AI skills out there. And most of them are just generated without any regard for what's in their contents or how they're structured. And so all the assets that I showed you at the beginning of this video are a byproduct of design tokens, structured code, structured assets, and they get exponentially harder to create when we lack those things. And so code, again, is communication. And we're seeing a shift left movement for content where content is moving to code. And if code, right, is the source of truth, if our code base is the source of truth, we have to have a structured source of truth in order to create content from code. In order to communicate, we need structure and conscientiousness around the way that we create code. And again, this only works with organizational excellence. And so I think what we'll see in 2026 and the years beyond is that the highest performing, the best communicating teams are the ones that are able to instill discipline and rigor into the process of creating software and then shift their content left towards the code through content engineering. And so 2026, I think, was the year of the creative technologists, at least in the dev rel space, this is the term that got thrown around a lot. I think 2027 is the year of the content engineer. And I'll close on that because I think what we're going to see next year are declarative and robust content pipelines capable of producing content walkthroughs, capable of producing documentation, screenshots, product updates, product updates, all of the things that were really manual can now be created through code, through React, and ultimately because of AI. Again, I'm Matt with Conductor. Thanks for sticking around for my talk. I'll catch you next time. Peace.