The Business Case for Giant Context

J
John Doe
|
Jan 15, 2024
|
5 min read
#react
#javascript
#frontend
A screenshot of a software landing page with a dark purple and blue gradient background and white text.
Screenshot of Giant Context (added retrospectively)

Autonomous marketing automates the department, not the tool. Why a marketing agency, almost pure labor, is where AI's disruption bites first.

A marketing agency sells work its clients cannot do themselves. It runs the sites, the emails, the posts, and the campaigns for a roster of businesses. It pays for that work almost entirely in people. Payroll is half to two-thirds of what an agency takes in [1], the largest cost it carries. After it, the average agency keeps about 13 cents on the dollar [2]. Giant Context is what I am building to change that number. It is autonomous marketing. You hand it a client's documents. It produces and runs that client's marketing on its own.

kept per dollar — the average agency margin

0%

billable utilization in 2024, a five-year low

$0

loaded cost of a five-person team, per year

The ceiling

An agency grows by adding clients, which means adding people. The work is billed against hours. A producer has only so many. Utilization, the share of an agency's paid time that is actually billable, ran under 70% across the industry in 2024, a five-year low [3]. One account handler carries fewer than ten clients before the service starts to slip [3]. Every new client on the roster pulls in more salary. The margin that was thin to begin with gets thinner as the shop scales. The largest agencies, the ones with the most clients, keep the least of all [2].

Growth is the number one thing agencies say they need [4]. It is also the thing the old model punishes. To take on more work, an agency hires. The hire is the cost. The cost is a person, because the tools were never what produced the marketing. A person did.

Hundreds of thousands a year

Now put the production into software. Most of what a producer makes, the posts, the pages, the emails, the ad copy, is exactly the kind of work these systems are good at now. Automate the bulk of it and the cost that scales with the roster comes off the books.

The arithmetic for a small shop is plain. A five-person production team, two writers, a social manager, a designer, and an SEO specialist, costs about $460,000 a year fully loaded [5]. Automate 60 to 70% of what they make and the agency saves $280,000 to $325,000 a year.* A fifty-client shop with a larger team saves $450,000 to $520,000 [5].

Production roleBase salary
Automate 60 to 70%$280,000 to $325,000 saved / year
Base$357,300
Fully loaded (about 1.3x)~$464,000 / year
Graphic designer$61,300
SEO specialist$70,000
Social media manager$70,000
Writer$78,000
Writer$78,000

The salaries are sourced [5]. Labor is half to two-thirds of what an agency spends [1]. There are about 41,000 marketing agencies in the United States, 88% of them under 50 people [6]. The same cost structure sits under almost all of them.

* The agency savings figure assumes a standard fully-loaded-cost multiplier and that most production work automates. Both are conservative. Even at half the automation, a small agency still saves six figures a year.

The lane no one is in

Agencies are not waiting to be sold on AI. Better than nine in ten are already using it or trying it [7]. The question is what they are buying. On one side are the platforms that run an agency's many clients, the sub-accounts and the billing and the dashboards. GoHighLevel and Vendasta lead there. The AI in them is an assistant on top of a fulfillment shell. On the other side are the content tools, Jasper and Copy.ai, that write well but cannot keep one client's work separate from another's. Both leave a person in the seat, deciding what to make and approving each piece. The agency still bills the hour. The hour still has a person in it.

What none of them does is the whole job at once. That job is one system that keeps every client separate, works from each client's own facts, and produces and publishes the marketing without a person driving it. The strong-AI tools are single-client. The multi-client tools have weak AI. No one has put the two together. The best-funded newcomer, Peec AI, raised $21 million late last year to help brands surface in AI search, not to run an agency's roster [18]. No one has funded the intersection either.

The timing is not subtle. Late last year the two largest agency holding companies merged in a deal worth more than thirteen billion dollars, cut four thousand jobs, and named AI as the reason [10]. The industry press is already calling the billable hour dead. The agencies that move to the new model instead of defending the old one are the ones that come through the repricing.

Automate the work, not the tool

The agency's cost was never the software. It was the people. Automate the work, and the roster stops adding salary.

This is why the platform sells the outcome and not a better tool. A tool competes for the software budget. The whole marketing-software market is about $550 billion a year worldwide [9]. A system that does the work competes for the labor budget, the trillions that businesses and their agencies spend on the people who get work done. Foundation Capital put the market for services and labor that software has never reached at about $4.6 trillion [8], many times the size of the software pool. An agency is where that shift bites first, because an agency is almost pure labor. Take the labor out of delivery and you have not discounted the service. You have rebuilt what it costs to provide.

What I am building

So that is what I am building. Not a content tool an agency runs. Not a shell that runs its clients with weak AI stapled on. One system that reads each client's own material until it understands the business, decides what marketing that client needs, makes it, and puts it out. It holds every client separately and works from each one's facts, so an agency runs a whole roster from one place and each client gets its own marketing, not filler about some business in the same trade.

Why the saving holds

One system across a whole roster beats a pile of tools for the same reason it beats a stack for a single business. Every tool in an assembled setup knows only its own slice. One system works from a single understanding of each client, so the same facts move that client's pages, emails, and posts without anyone keying them in three times. The overhead of wiring tools together goes with them.

The work stays good because it runs on the client's own facts. You hand it the client's material. It produces that client's marketing, not confident filler about some company in the same trade. That is the difference between output an agency can hand a client and output it has to redo. It is what decides whether autonomy is worth anything at all.

The first customer is me. The first thing the platform does is run my own marketing, this site and the blog it will host. The saving from not building and staffing all of that by hand, for my own company, is what makes a one-person build possible at all. If it can run my marketing, it can run an agency's roster.

The margin goes the right way

There is a standard worry about a business like this. The classic version, made by a16z in 2020, is that AI companies run gross margins of 50% to 60% where software companies run 60% to 80% or better [11], for two reasons. Serving each customer burns real computing cost, where serving one more software customer costs almost nothing. And the output usually needs a person in the loop to check it. On that logic an AI business is structurally worse than a software business.

Two facts turn that worry around. The cost of serving a customer here is essentially the cost of the model doing the work, with no person in the loop being paid by the hour. And the cost of the model is falling faster than almost anything else in technology. Epoch AI measured the price to run a given quality of model dropping from $20 per million tokens to 7 cents in about two years, close to 285 times cheaper [12]. When the main cost of serving a customer is compute, and compute is collapsing at that rate, the margin does not thin over time. It widens. The 2020 objection becomes the reason to build now.

That also settles how you pay. You pay for the marketing produced, not for seats or licenses. For an agency running many clients, the platform costs what the work costs. And that cost falls every year, as the model underneath it gets cheaper.

The second theory: the small business

The agency is the first bet. There is a second, bigger one. It is the same automation, pointed straight at the small business that could never afford an agency or a marketer in the first place.

The numbers there are larger and the problem is harder. There are about 35 million small businesses in the United States, roughly 28 million of them with no employees at all [16]. Their owners spend an hour a day or less on marketing and mostly doubt it is working [15]. They buy a stack of six or seven tools and use a fraction of each [13], because the point-tool era sold them the enterprise's software without the enterprise's staff [14]. For a business like that, the platform replaces not a team but the marketer it was never going to hire, whose fully loaded cost runs past $160,000 a year [17].

The value to each business is enormous. The trouble is reach. Selling to 35 million of them one at a time is its own expensive problem, which is the real reason the agency comes first. An agency is a single buyer that feels the saving multiplied across a roster. It carries the small business in as its client. There is no product-market fit yet for either bet. These are two theories. The agency is the sharper one.

The department was the cost

An agency has always sold two things bundled together, the tools and the people who run them. The tools were never the expensive part. The people were. And the people are the part the old model cannot scale. Every client added a little more payroll and left the margin a little thinner.

Automate the work and that stops. The roster grows without the salary growing under it. The agency keeps the relationship, the clients, and the judgment about what good looks like. It stops paying a team to produce what a system now can. Marketing is the first kind of work to go this way, because it is made of a business's own facts turned into content. It will not be the last.

The math points to a large market no one has taken. Autonomous marketing, one system that runs a business's marketing end to end, is the thesis. I am building the platform to prove it.

Sources

1. Agency payroll as a share of revenue: Swydo, 2025.

2. Agency net margins by size: Haus Advisors, 2025.

3. Billable utilization and clients per account: Mosaic, 2024.

4. Growth as the top agency pain: Haus Advisors, 2025.

5. Production-role salaries: Indeed (writer), US Bureau of Labor Statistics, Career.com, Webflow, 2024–2025.

6. Number and size of US agencies: Haus Advisors, 2025.

7. Agency generative-AI adoption: Forrester / 4As, via Marketing Dive, 2024.

8. The services-and-labor market: Foundation Capital, 2024.

9. Marketing-software market size: Grand View Research, 2025.

10. Agency-holding-company consolidation and job cuts: Marketing Dive, 2025.

11. AI vs software gross margins: a16z, 2020.

12. Inference cost over time: Epoch AI, 2024.

13. Martech-stack utilization: Gartner, via MarTech.org, 2023.

14. Buyers wanting one tool / fewer vendors: G2 SalesTech Trends, 2024.

15. Small-business marketing time and confidence: Constant Contact, 2024.

16. US small-business counts: SBA Office of Advocacy, 2024.

17. Median marketing-manager pay: US Bureau of Labor Statistics, 2024.

18. Closest funded new entrant: Peec AI Series A, via Tech.eu, 2025.

Have questions?

If you're interested in my work or Giant Context, contact me!

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The Business Case for Giant Context