A Formula 1 team has roughly 800 to 1,000 people. Toyota, the world’s largest automaker, employs over 375,000. Yet an F1 team designs, machines, and assembles a car that is in every meaningful engineering dimension more advanced than anything rolling off Toyota’s production lines. More precise. More optimized. More aggressively engineered. Built to tolerances that production manufacturing does not attempt.
The F1 team does not achieve this by being a smaller version of a car manufacturer. It achieves it by being a fundamentally different kind of organization. Every person on the team is operating at the edge of their capability. Every process is designed to eliminate waste. Every tool in the shop exists because it directly contributes to making the car faster. There are no departments that exist for historical reasons. There is no organizational overhead maintained because “that is how it has always been done.” The entire operation is a precision instrument pointed at a single objective: performance on race day.
This model is now available to software engineering teams, and AI is the reason.
What the Factory Model Actually Costs
Most enterprise software organizations are structured like car manufacturers, not F1 teams. They have large headcounts organized into layers: junior developers, mid-level developers, senior developers, tech leads, engineering managers, directors, VPs. Each layer adds coordination overhead. Each layer adds communication latency. Each layer adds interpretation loss as requirements pass from the people who understand the business problem to the people who will implement the solution.
The factory model made sense when the bottleneck was implementation capacity. When you needed a hundred developers to ship a product in a reasonable timeframe, you needed the organizational infrastructure to coordinate a hundred developers. Project managers to track work. Scrum masters to run ceremonies. Middle management to align priorities across teams. Quality assurance teams to catch bugs that cross team boundaries. The overhead was the cost of operating at scale, and it was accepted because there was no alternative.
The overhead is not trivial. Studies of large engineering organizations consistently find that individual developers spend 30% to 50% of their time on activities that are not writing, reviewing, or designing code: meetings, status updates, context switching between projects, waiting for dependencies, navigating internal processes. In a 200-person engineering organization, that is the equivalent of 60 to 100 full-time engineers producing nothing. Their salaries are paying for coordination, not creation.
This is not a criticism of the people in these roles. They are doing necessary work within the system they operate in. The criticism is of the system. The factory model requires a coordination tax that scales superlinearly with headcount. Double the team size and you more than double the coordination cost, because the number of communication paths grows quadratically while the output grows linearly at best.
The F1 Alternative
An F1 team operates on the opposite principle. Instead of scaling headcount to increase output, it scales capability per person to increase output. Every team member operates with specialized tools, deep expertise, and precisely designed workflows that maximize the value of their contribution.
When an F1 team needs a new component, they do not add fifty people to a production line. They give a small team of specialists access to advanced manufacturing capabilities, simulation software, materials science databases, and precision machining equipment, and that team produces a component that a factory of hundreds could not.
The parallel to AI-native software engineering is direct.
A senior engineer equipped with AI agents, operating within a well-designed specification and quality framework, can now produce output that previously required a team of five to ten. Not by working harder, not by cutting corners, but by using tools that eliminate the manual labor of implementation while preserving the human judgment that determines what gets built and whether it is correct.
This changes the math on team size. If one person can do the implementation work of five, a team of five can do the work of twenty-five. That team of five does not need three layers of management, two project managers, and a Scrum master. It needs a clear objective, the right tools, and the autonomy to execute.
Machining Your Own Parts
One of the most distinctive characteristics of an F1 team is that they manufacture their own components. They do not order parts from a catalog. They design exactly what they need, then machine it in-house to exact specifications. If a car needs a bracket with specific aerodynamic properties, specific weight constraints, and specific structural requirements, the team designs that bracket and produces it. No compromise with off-the-shelf options. No waiting for a vendor to customize a standard part.
This is precisely what AI enables for software teams.
The traditional enterprise software stack is built from catalog parts: SaaS products that solve generic versions of your specific problem. Your workflow does not quite match the tool’s assumptions, so you build workarounds. Your data model does not quite fit the vendor’s schema, so you write transformation layers. Your reporting needs do not quite align with the product’s dashboards, so you export data and build reports elsewhere.
Each SaaS product is like an off-the-shelf part. It approximately fits. The approximation is close enough that buying was always more rational than building, because building was prohibitively expensive.
AI has moved the economics of custom fabrication below the economics of off-the-shelf compromise. A team that can specify exactly what it needs and have AI agents implement it in days is no longer constrained to choose from the catalog. It can machine its own parts. And custom parts that fit exactly are superior to catalog parts that fit approximately.
We are seeing this with clients across industries. An enterprise that was paying $400,000 annually for a combination of SaaS tools that each solved 70% of its actual need now has a custom platform that solves 95% of its need, built in weeks, maintained for a fraction of the licensing cost. The platform fits their workflow instead of forcing their workflow to fit the platform.
Why Size Becomes a Liability
In the factory model, size is an asset. More people means more output, at least up to the point where coordination costs overwhelm productivity gains. In the F1 model, size is a liability.
Every additional person on a team adds communication overhead, coordination latency, and alignment cost. In a five-person team, there are ten communication paths. In a twenty-person team, there are 190. In a fifty-person team, there are 1,225. Each path is a potential source of miscommunication, delay, and misalignment.
Large teams also suffer from diffusion of responsibility. When fifty people are responsible for a product, no individual feels the full weight of ownership. Bugs get filed instead of fixed. Architectural decisions get deferred to committees. Quality becomes someone else’s job. The organizational structure that was supposed to enable productivity instead enables avoidance.
F1 teams do not have this problem because they cannot afford it. When the team is small, every person’s contribution is visible. Every person’s failure is consequential. Ownership is not a cultural value that gets reinforced in all-hands meetings. It is a structural reality that emerges from having nowhere to hide.
AI-native engineering teams operate the same way. When a team of five is producing the output of a traditional team of twenty-five, each member is operating at high leverage. Their decisions matter more. Their judgment is more consequential. Their contribution is more visible. This attracts a different caliber of engineer and produces a different quality of output.
The Pit Crew Analogy
During a race, an F1 pit crew changes four tires and makes mechanical adjustments in under two seconds. Twenty people performing a choreographed sequence with sub-second precision. This is only possible because every movement has been designed, practiced, and optimized. There is no improvisation. There is no ambiguity about who does what. The process is a precision instrument.
The analog in AI-native engineering is the specification-driven workflow. When the specification is precise, the quality gates are defined, and the agent orchestration is established, delivery becomes a choreographed sequence. The specification goes in. The agents execute. The quality gates validate. The output ships. The human judgment happens at the decision points: what to build, whether the output meets the standard, what to adjust. The execution between decision points is automated and repeatable.
This is fundamentally different from the traditional development process, where each feature delivery is a semi-improvised exercise in requirements interpretation, technical decision-making, implementation, debugging, and manual testing. That process has the variability of a garage mechanic changing a tire with a hand wrench. The F1 pit stop has the precision of a designed system.
Designing the system takes investment. You need precise specifications, automated quality gates, well-configured agent workflows, and team members who understand how to operate within the system. But once the system is designed, it produces results at a speed and consistency that improvised processes cannot match.
The Talent Equation
F1 teams do not hire average mechanics and train them up. They hire the best people they can find, give them the best tools in the world, and create an environment where excellence is the baseline expectation. The team’s competitive advantage is not headcount. It is the quality of its people multiplied by the quality of its tools.
AI-native engineering teams work the same way. A mediocre engineer with AI tools produces mediocre output faster. A great engineer with AI tools produces exceptional output at a pace that was previously impossible. The leverage that AI provides amplifies the difference between good and great rather than equalizing it.
This means that the talent strategy for an AI-native team is fundamentally different from the talent strategy for a factory model team. The factory model tolerates a wide range of skill levels because the system is designed to produce consistent output from variable inputs. Junior developers can contribute meaningfully because the work is decomposed into small, well-defined tasks that do not require deep judgment.
The F1 model cannot tolerate mediocrity because every team member is operating at high leverage. A specification that is vague produces bad agent output across an entire feature. A quality gate that is poorly designed lets defects through to production. An architectural decision that is wrong creates technical debt that compounds across the system. The consequences of poor judgment are amplified by the speed of AI-assisted delivery, not buffered by layers of review and management.
This does not mean that AI-native teams are exclusively staffed with twenty-year veterans. It means that every team member needs to operate with the judgment and ownership of a senior practitioner, regardless of their years of experience. Some people develop that capability early. Others never develop it. The F1 model selects for capability, not tenure.
The Economics of Precision
F1 teams are not cheap. The top teams spend $140 to $150 million per year under the budget cap, not counting driver salaries and certain excluded costs. But their cost per unit of engineering output is dramatically lower than a car manufacturer’s, because they produce more advanced engineering with fewer people and more sophisticated tools.
The same economics apply to AI-native engineering teams. A five-person AI-native team is not cheap on a per-person basis. These are senior, experienced engineers with deep domain expertise, equipped with sophisticated tooling and AI infrastructure. Their individual compensation reflects their capability.
But the team’s total cost is a fraction of the twenty-five-person traditional team that produces equivalent output. Five senior engineers at high compensation is still dramatically cheaper than twenty-five mixed-level engineers plus the management, coordination, and infrastructure overhead that a team of twenty-five requires.
The savings are not just in salary. They are in everything that scales with headcount: office space, equipment, licenses, benefits, HR overhead, management time, meeting hours, communication tools, and the invisible cost of coordination latency. Every person you do not need to hire is a person you do not need to manage, align, review, and coordinate with.
This is why the F1 model is not just a nice analogy. It is a competitive strategy. Organizations that adopt it produce better output at lower total cost than organizations running the factory model. The gap widens as AI capabilities improve, because the leverage per person increases while the coordination cost per person in the factory model stays constant.
Building the Team
The practical question for engineering leaders is how to transition from a factory model to an F1 model. The answer is not to fire 80% of your engineering organization. It is to restructure how work flows through the organization.
Start with one team. Take your best five engineers, give them AI tooling and a clear business objective, remove the process overhead, and let them operate as an F1 team. Measure their output against comparable teams operating in the factory model. The data will make the case for broader adoption.
Invest in the specification layer. F1 teams spend enormous time on design before they machine a single part. AI-native teams need to spend comparable time on specification before agents write a single line of code. The specification is where the team’s judgment and domain expertise is encoded. It is the highest-leverage investment.
Build quality infrastructure, not quality teams. F1 teams do not have a separate quality department that inspects finished parts. Quality is built into the manufacturing process: precision tooling, automated measurement, defined tolerances. AI-native teams need the same approach: automated test suites, security scanning, performance benchmarks, and deployment gates that enforce quality structurally rather than relying on manual inspection.
Embrace the discomfort of small teams. There is an organizational instinct to add people when pressure increases. Resist it. Adding people to an F1 team does not make the car faster. It adds weight. Adding people to an AI-native team does not increase output. It increases coordination cost. When the pressure increases, the answer is better tools, better specifications, and better processes, not more bodies.
Redesign career paths. The factory model has clear career paths: junior to mid to senior to lead to manager. The F1 model needs different paths: deeper specialization, broader system thinking, specification mastery, agent orchestration expertise. People need to see a future in the new model or they will resist the transition.
The Competitive Implication
The organizations that adopt the F1 model will systematically outperform those that do not. They will ship faster because they have less coordination overhead. They will ship better because their small teams have higher ownership and accountability. They will ship cheaper because their total cost is a fraction of the factory model’s. And they will attract better talent because the best engineers want to work on high-leverage teams with great tools, not in bureaucratic factories where their impact is diluted by process.
This is not a temporary advantage. It is a structural one. As AI capabilities improve, the leverage of the F1 model increases. Each improvement in agent capability makes the small team more productive without adding coordination cost. The factory model captures the same improvement but dilutes it across its coordination overhead.
The manufacturers that compete against F1 teams on the track do not try to outproduce them with factory methods. They build their own racing teams. They adopt the F1 model because they recognize that the factory model, however effective for mass production, cannot compete in a domain where precision, speed, and engineering excellence determine the outcome.
Software engineering is becoming that domain. The question is whether your organization will build a racing team or keep running the factory.
