Stop Selling Software – Sell the Outcome It Produces

01 /The Concept

In most B2B startups that try to build an audience through expert content on LinkedIn, the founder is typically the only one posting. It works, but slowly – one person's reach is inherently capped by the platform's algorithms.

The math changes dramatically if the whole leadership team posts. More voices mean more reach, more reach means more inbound leads. But executing that at scale is harder than it sounds. You need to keep people motivated, coordinate topics, and manage a publishing schedule. Outsourcing to an external agency runs $150,000 a year. Using off-the-shelf AI produces generic, detectable AI content that readers recoil from.

Imagine AI was built to solve this. It's an AI agency that helps entire startup teams create and publish high-quality expert content that actually converts.

The value isn't just more posts with higher total reach – it's coordinated posts. Content across the team is aligned to attack a single topic from multiple angles and speak to different audience segments simultaneously. When the CEO publishes a post on company strategy, the VP of Sales follows with customer case studies that validate it, while the VP of Marketing publishes a post on the underlying process that executes it.

Critically, none of this reads like corporate boilerplate. Before generating a single word, Imagine AI builds a 100-plus-page internal profile for every person involved in the content program. These profiles are assembled from transcribed interviews, previous articles, recorded talks, podcast appearances, and any other first-person content the individual has created.

The AI then analyzes each profile so that everything it writes matches the person's actual thinking patterns, tone, and voice – to the point where a well-calibrated reader couldn't distinguish the draft from something the person wrote themselves. And the word "draft" matters: the employee reads, edits, and approves every post before it goes live.

Every client company also gets a dedicated human content strategist from Imagine AI – handling topic selection, scheduling, and coordination. But even this person works AI-assisted, not from scratch.

One more important layer: the platform tracks which posts are published and what engagement they drive, while simultaneously monitoring the growth in inbound leads. It then connects the two signals to understand what to write, and how, to generate more qualified pipeline.

In other words, Imagine AI optimizes for business outcomes – not for likes and impressions.

Despite involving more people in the content process, the time burden shrinks sharply. Total content preparation time – topic selection, coordination, scheduling – drops from 8 hours to 30 minutes. An employee reviewing and polishing a post spends 5 minutes instead of an hour writing from scratch.

Most client companies end up spending just 2–4 hours per month on manual work, because everything else is handled by the startup and its AI platform.

Imagine AI graduated from Y Combinator last December but only published its public launch post two days ago. Despite that, the business is already real: roughly 40 client companies, which have collectively generated $31 million in incremental revenue using the platform.

02 /Why It Matters

Traditional content agencies that write posts for B2B startup founders are plentiful. Storyarb – launched by the founder of Morning Brew, sold in late 2020 for $75 million – is a well-known example in this space.

And the prices are serious. Storyarb's tiers run from $10,000 to $17,000 per month – roughly $150,000 per year, the same figure Imagine AI cites as the status quo cost. At those rates, clients get a newsletter 2–4 times per month and 2–8 LinkedIn posts per week tied to the same themes.

Imagine AI hasn't published pricing, but given that it positions $150,000/year as a reason to look elsewhere, its prices are likely significantly lower. Not because it's discounting – but because it has substantially reduced the cost and time of its own operations through smart use of AI.

The broader AI agency category is clearly on the rise. Y Combinator's latest "Requests for Startups" list, published for its current batch, has singled out "AI-native agencies" as a specific thesis. The reasoning goes like this:

Agencies have always been hard to scale: thin margins, slow manual processes, and revenue tied directly to headcount. AI changes that equation.

Instead of selling clients an AI platform to solve their problem, you can use that same software yourself – to sell clients the end result at 100x the cost of a software subscription to the equivalent tool.

"Agencies of the future" will look like software companies, not traditional agencies – with software-company margins (~80%) instead of traditional agency margins (~10%). And unlike a headcount-driven agency, they can actually scale, because growth no longer requires proportional hiring.

Recent examples across verticals tell the same story. Crosby ([related review](/review/bolee-prostaja-model-dlja-sozdanija-perspektivnogo-ii-produkta)) is a legal AI agency that raised two rounds last year totaling $25.8M. WorkHero ([related review](/review/prodavaj-vot-takoj-servis-vmesto-it-platformy)) outsources office management to HVAC companies using AI-augmented staff, raising $5M in its first round. Catalyne ([related review](/review/na-bljudechke-25-idealnyh-klientov)) is a marketing AI agency that raised $31M on crowdfunding, promising clients actual paying customers. And Phoebe ([related review](/review/platforma-dlja-samoj-bolshoj-professii)) raised $9.5M to turn home care companies into "self-piloting agencies." That's legal work, field services, marketing, and elder care – all running on the same model.

03 /Opportunities

The most clear-eyed founders have already started asking: what should startups that normally ship software platforms do in an era where software itself is becoming cheap to build? If any potential customer can code their own platform with AI assistance, the platform itself isn't the moat.

The answer is straightforward: stop selling the platform – start selling outcomes.

AI-native agencies can deliver those outcomes faster and at higher quality than traditional agencies, because the AI handles the bulk of the actual work. But humans remain essential – to establish the context through direct client relationships, and to review, refine, and validate the output before it goes to the client.

The feedback loop is also important: direct client work generates real performance data, and that data feeds back into improving the AI platform's algorithms – progressively raising quality and reducing the manual labor required over time.

AI agencies are a trend – one recognized not just by individual observers, but by Y Combinator.

So: the direction that matters right now is building an AI agency instead of a conventional software startup, even one that uses AI. The constraint worth thinking through isn't finding a domain – it's identifying one where AI can handle 80% of the actual work and where the value of that 20% human review layer is obvious enough to justify a premium over software subscription pricing.