Agentic GTM and Product Engineering: From 30 to 3

It used to take 30 people to run a GTM or product department. Today, 3 is enough. That is not a forecast. That is my daily work.

I am building my 10th company right now. In the first nine, I built marketing, sales and product with big teams: hire, onboard, coordinate, hope. Today I do it differently. A few professionals plus a team of AI agents that work around the clock. I call this Get Multiplayer: how humans and AI agents run a department together. Three instead of thirty.

What you will take away:

• Why AI made execution cheap, and what the real bottleneck is now.
• How Get Multiplayer works on both sides: GTM and product.
• Three concrete ways to put it into your company.

My thesis: Anyone still building a GTM or product department with 30 people is building expensive, slow and without a system. The lever is no longer in execution. It is in leadership and context, when ten tools run at the same time.

🧨 The problem: execution got cheap, the bottleneck moved

Two years ago, execution was the expensive part. Write a sales sequence, research a market, build a landing page, review a pull request: all hours, all heads, all salary.

AI flipped that. An agent writes the sequence in minutes. Researches a hundred accounts overnight. Builds the first pull request while you sleep. Execution now costs almost nothing.

That moves the bottleneck. When ten agents run at once, the question is no longer "who does the work". The question is: who leads them, and on what context do they work? Ten machines without leadership produce garbage ten times faster. That is garbage in, garbage out, just faster and at scale.

This is where it breaks. Generic AI does not know your code, your Jira or your business. It produces plausible smoke. So every agent needs a shared foundation: the business and code context of your company, prepared so agents can work with it. I call this the Context Engine. It is the magic ingredient, not the model.

🛠️ The solution: Get Multiplayer on two fronts

The pattern is the same on both sides of the company. Good product, but no system that scales with it. I call this state stuck in the middle: after product-market fit, before scale. The product works, but the foundation of processes and data is missing.

On the GTM side, the result is Autonomous GTM. A tech company runs its go-to-market with 3 people and 10 agents, as if it were a team of 30. Here is how I build it:

1. Measure first, do not guess. Where is the real bottleneck? Positioning, pipeline, channels.
2. One central system instead of three truths. A clean CRM, one pipeline, one positioning in a single sentence.
3. Agents by bottleneck priority, not by hype. Three to five productive agents that run even when you are gone for a week.

On the product side, the result is Autonomous Product Management. Product decisions on real context, with AI agents, instead of gut feel. Today the knowledge is scattered: in Jira, in PRDs, in stories, in the heads of your senior devs. An agent that knows this context turns from a code monkey into a sparring partner. It launches the first PR agent, builds the test gap map, shows where it is safe to ship and where it is not.

Both sides share the same foundation. The Context Engine is the bracket. Applied to GTM, it lives as software that works like Jira for GTM teams. Applied to code and product, it sits inside an agent that studies your code. One idea, two applications.

The difference in daily work is concrete. Building GTM used to mean: hire a head of marketing, an SDR team, a sales engineer, and see a first result after six months. Today I point three agents at the bottleneck and see in weeks whether the positioning lands. Scaling a product team used to mean: more devs, more tickets, more coordination. Today a senior dev leads a team of agents and decides what ships. People do not become less important. They become more important, because they lead instead of execute.

🤖 The three ways: learn, deploy, have it built

There are three ways to the same result. You do not have to take all of them. You have to know which one fits your phase.

• Learn: gtm.science is an open-source framework for GTM teams. In live cohorts you install Autonomous GTM hands-on in your company. You learn it yourself and keep it in-house. Best if the whole team joins.
• Deploy: teklens.ai is software that joins your product team, as AI agents. It knows your code, your Jira and your business. This is my 10th company, I am building the team right now.
• Have it built: Pedalix builds both systems inside your company, done for you. A small team plus AI agents. The result is a running system, installed in your company.

The difference from the market: an agency makes you dependent, a system sets you free. The agency delivers fast, but the knowledge stays outside. When it leaves, the knowledge leaves with it. I build a system that stays. I leave, the system stays.

🎢 What remains

✅ What works. With three professionals and a team of agents, you play big without hiring big. That is reality today, not a pitch. Proof from my work through Pedalix: over 100 percent revenue growth supported at Emporix, ROI in under 2 months at Xorlab.

❌ What does not work. Agents on an empty foundation. Without context and without leadership, you get faster garbage, not results. Anyone who thinks AI does the work magically on its own will be disappointed.

⚠️ Warning. The bottleneck is not the tool. The bottleneck is you, if you let ten machines loose without a plan. Leadership and context first, then the agents.

Used to be 30, now 3. The reason is not that AI can do everything. The reason is that execution got cheap, and leadership plus context tip the scale. If you work at the intersection of product, GTM and AI and want out of "stuck in the middle", that is your lever. Founder to founder.