Layers Wants to Be the Marketing Brain Your App Never Had

The Macro: Marketing Automation Is a Crowded Room That Keeps Getting Bigger

Marketing automation was already large and loud before AI agents arrived. Depending on which analyst you trust, and the variance here is genuinely remarkable, the global market sits somewhere between $6.65 billion and $47 billion in 2025. Grand View Research puts it at the lower end, projecting growth to $15.58 billion by 2030 at a 15.3% CAGR. MarketsandMarkets is considerably more bullish, tagging the 2025 figure at $47 billion and the 2030 projection at $81 billion.

Reconciling those numbers is probably a fool’s errand.

The directional consensus holds across sources: the market is large, growing at roughly 11 to 15% annually, and AI is the primary accelerant. The more interesting story isn’t the size. It’s the positioning war happening inside it.

Legacy players like HubSpot, Marketo, and Salesforce Marketing Cloud built their moats around CRM integration and workflow automation. They’re good at managing audiences you’ve already acquired. What they’ve historically been worse at is helping small teams generate and distribute content at the cadence that modern social platforms demand, or doing anything that requires understanding what your product actually does.

That gap is where a new generation of tools is crowding in. Platforms built around AI-generated content, autonomous social posting, and programmatic ad management have multiplied fast. The pitch is almost always some variant of the same thing: let the machine handle distribution so your team can handle everything else. Layers is pitching a sharper version of that. Not just automation, but automation that ostensibly starts from your codebase rather than a marketing brief. That’s a more specific claim, and specific claims are worth examining closely.

The Micro: It Reads Your Code. Then It Posts About It.

The core premise of Layers is that most marketing tools are context-blind. You give them a brief, a brand guide, maybe some audience data, and they generate content that is at best plausibly on-brand. Layers claims to go one layer deeper (the pun is load-bearing, yes) by connecting directly to your codebase, inferring what your product actually does, and using that as the foundation for marketing output.

In practice, the platform appears to operate as a stack of distinct functional modules it calls, predictably, layers. There’s a Content Generation layer that performs trend research and produces content tailored to your app and audience. A Social Distribution layer that schedules and posts to TikTok and Instagram with timing optimization. A Social Engagement layer that monitors comments and generates replies designed to pass as human. Coverage also extends to Apple, Meta, and TikTok ads, App Store Optimization, and something described as a managed UGC creator program.

That’s a wide surface area for a single platform.

Whether the code-awareness angle is a genuine technical differentiator or a clever framing of something more conventional, say connecting to your app’s description and metadata, isn’t something the public-facing materials resolve cleanly. The claim is interesting enough to take seriously and vague enough to watch carefully.

It got solid traction on launch day. The comment-to-vote ratio suggested people had actual questions, which is usually a better signal than pure upvote accumulation from a founder’s network.

The apparent target is the solo founder or tiny team building in what the Product Hunt taxonomy calls the “vibe coding” era. People shipping products fast, with no marketing function and no budget for one.

The Verdict

Layers is solving a real problem. The founder who can write code faster than they can write tweets is not a hypothetical. It’s basically the modal user of every early-stage indie builder community online. An automated marketing stack that genuinely understands the product it’s promoting, rather than hallucinating generic copy about your “innovative solution,” would be worth a lot.

The honest uncertainty is whether “code-aware” is architecture or marketing. If Layers has built something that meaningfully parses application structure to inform content strategy, that’s a durable technical edge. If it’s a well-designed onboarding flow that extracts the same information a good intake form would, it’s a better-than-average automation tool. Still useful, just less defensible.

At 30 days, my question is activation. Do users who connect their repo actually get content that feels specific to their product, or does it revert to the same mid-level generic output every other AI content tool produces? At 60 days, it’s retention. Autonomous posting tools live and die on whether the content they generate is embarrassing enough to make founders turn them off. At 90 days, it’s whether the ad automation and ASO features actually move numbers or exist primarily to fill out a feature matrix.

I think this is probably a genuinely useful tool for solo technical founders who have zero marketing bandwidth and need something running in the background. I’m more skeptical it holds up for anyone with enough marketing context to notice when the output is generic. The “code-aware” claim is the whole bet here, and I’m not taking it at face value until someone shows their work.