Rethinking Unit Economics for the Physical AI Era

Those in finance, and even those who just touch it, often hear the term "unit economics." The number of times I have had to explain unit economics of the business is staggering. And quite often, the unit economics took a different shape in the conversation with investors, consultants, lawyers, and other professionals. This diversity of ways to look at the concept of unit economics prompted me to sit down and think through what unit economics actually is, why it is used so frequently in finance, and what is different about it in the Physical AI space.

What is unit economics?

Unit economics is a thing or an activity that generates value for its user. A share of that value is captured by a company as revenue. Delivering that value to the user requires the company to deploy resources that either exceed the captured share of value (generating a loss per unit) or, what companies hope for, fall significantly below that share in value (generating profits per unit). It's not the same as the gross or net profit you see in financial statements, as it doesn't account for the noise of overheads, one-time costs, R&D investment, sales force, etc. It's a basic foundational atom of economic value creation at the company that is tied to the nature of the company's product. Different products within the same company will have different unit economics. It might be a commercial transaction processed, a robotaxi ride, a customer acquired, a traffic camera, a nuclear microreactor, an oil well, an AI chatbot query, a subscription seat, a mile of freight delivery, a shipping container; the list goes on. Additionally, the same activity can be looked at from different angles, resulting in different unit economics. For instance, unit economics for commercial freight delivery can be a mile, a truck, or a trip. Regardless of the unit economics a company chooses, consistent usage, appropriate revenue and cost allocation, and their inclusion in actual decision-making are crucial.

Unit economics helps companies analyze multiple questions:

1. Do we deploy resources efficiently?
2. Are we able to capture maximum value from our product?
3. Does scaling hurt or help our profits?

A business with healthy unit economics gets stronger as it grows. A business with broken unit economics loses money faster if there is no realistic plan in place to improve it. The aggregate P&L will eventually tell you which one you have. The beauty of unit economics is that it tells that story ahead of time.

Unit economics of a physical product.

In the classic world of physical goods, the concept of unit economics is quite simple. It's commonly the physical good itself — a car, a microwave, or a goat. The value is derived by taking its consumer price and deducting the marginal cost to make it and deliver it into the hands of the end consumer, including raw materials, direct labor, freight, and other variable and directly attributable costs. The unit is a discrete object created, delivered, and consumed at a point in time. Costs are primarily COGS that scale but likely improve on a per-unit basis with volume. Companies incur other costs that have a primarily fixed nature, such as building a factory, production tools, marketing, and G&A, which need to be recovered through scaling the sale of products with positive contribution margins. Value from the physical good accrues to the company at the moment of a single transaction, where ownership of the good transfers from the seller to the customer.

How did SaaS change things?

Software products are different from physical goods. Software normally comes with sizable upfront development costs to create the software, while directly attributable costs to scale the software and deliver it to end users are virtually zero. Whether software is used by 10 or 1,000 users doesn't drastically change its cost structure. So investors normally expect 70–90% contribution margins from SaaS products. The unit economics in the SaaS world becomes not the piece of software, but instead the human using that software.

The focus of these companies has turned from production to customer acquisition, retention, and upsell. Value from the unit economics of a SaaS product accrues to the company over the lifetime of the customer, not at the moment of sale. The key performance metrics in SaaS are tied primarily to the customer, not the product: LTV, CAC, the LTV/CAC ratio, net revenue retention, magic number, etc. Unit economics with low marginal costs incentivizes companies to aggressively pursue customer growth, making it the key topic of executives' attention.

What happened when Software AI entered the market?

Large language models arrived and reintroduced the thing SaaS had spent a few decades eliminating: a meaningful, recurring marginal cost. Every interaction with Claude or ChatGPT carries a variable cost driven by model consumption of GPU/CPU and energy. Inference cost is frequently the largest variable line that scales with usage, presenting economic friction to increased utilization.

The unit economics in the Software AI world shifted from the customer to the query. Flat-rate subscriptions of a SaaS business break when heavy usage drives the cost per interaction substantially over the flat subscription fee. That's why usage-based and hybrid pricing have returned, to price utilization closer to the cost to serve. More usage no longer means leverage; it means more cost. The discipline shifts from "acquire and retain cheaply" back to "serve profitably."

What is unique about economics of a Physical AI asset?

Physical AI assets include humanoids, autonomous vehicles, warehouse robots, drones, and other machines that perceive the world and act in it to fulfill a task. This class of products is commonly sold as a service: robotics-as-a-service, capacity-as-a-service, or labor-as-a-service. The unit economics that generates value becomes the work performed.

Economic value creation of a Physical AI asset has a hybrid nature; it doesn't fit perfectly into the profile of a traditional physical good or a SaaS product. This makes a Physical AI product unique: it has a recurring revenue stream normally associated with a SaaS product, paired with a capital-intensive production model associated with traditional physical products. There are a few performance metrics that become critical:

• Utilization. Physical AI unit economics is highly driven by uptime, operating hours, and throughput, similar to the logic that governs airlines, hotels, and equipment rentals. Utilization is a variable factor that has massive downstream consequences for unit economics.
• Scale. As production volumes of Physical AI assets increase, Wright's Law kicks in — the more units are built, the cheaper each unit becomes. For companies building Physical AI products, the ability to scale and investment in production methods become critical factors.
• Autonomy. The labor costs of human supervision go into operating costs, dragging margins down. As software capabilities improve, allowing for more autonomous operation, unit margins improve. Scale creates a data flywheel that feeds model training, which in turn increases autonomy.
• Upgrades. When software capabilities improve, those improvements can be sent wirelessly to the entire fleet of Physical AI assets, raising the unit economics of all of them — a dynamic that has no parallel in traditional physical products.
• Ownership and financing. The ownership and financing structure of a Physical AI product can fall anywhere between the traditional sale model and the SaaS subscription model, and each configuration creates very different incentive structures for both seller and customer.

The usual metrics of LTV/CAC or gross margin at the point of sale lose their relevance in favor of asset-level NPV, IRR, cost per unit of output, payback period, utilization rate, and gross operating margins.

What does this mean?

Physical AI doesn't introduce a new kind of unit economics so much as it inherits every kind that came before. It carries the capital intensity of physical goods, the recurring value capture of SaaS, and the comeback of the marginal cost of Software AI. The businesses that win will be the ones that create business models addressing all of these forces at once.

There's a reason why getting the unit economics of a Physical AI product right from the onset matters more than it did in software. When a SaaS company gets its unit economics wrong, it burns cash, learns, and can often pivot its way out. When a Physical AI company gets its unit economics wrong, the mistake is already sunk into hardware architecture, production facilities, and field operations infrastructure. That's why a finance function fluent in the economics of innovation isn't a back-office hire in this space — it must be part of the founding team.