A few weeks ago, Sam Altman walked into a Y
\nCombinator event and made the kind of offer that gets Silicon Valley talking.
\nOpenAI, he reportedly said, would provide $2 million worth of OpenAI API tokens
\nto every startup in the current YC batch, in exchange for future equity through
\nan uncapped SAFE agreement. The money was not cash, exactly. It was compute. In
\nthe AI economy, that distinction matters less than it once would have. For many
\nyoung companies, access to models and inference capacity is quickly becoming as
\nimportant as access to cloud hosting, software tools, or even employees.<\/p>\n
The easy way to understand the announcement is
\nas a market-share strategy. There is a market-share arms race happening now as
\neach platform tries to lock-in as many first-time users as they can. OpenAI
\nwants the next generation of startups building on OpenAI. Anthropic wants them
\nbuilding on Claude. Google wants them building on Gemini. Meta, Microsoft,
\nAmazon, and others all understand that the early habits of builders can harden
\ninto long-term dependency. <\/p>\n
Once a startup builds its product
\narchitecture, customer workflows, engineering talent, and business model around
\na particular platform, moving away becomes expensive. That was true in the
\ncloud era. It was true in mobile and in enterprise SaaS. It will almost
\ncertainly be true in artificial intelligence.<\/p>\n
But AI introduces a more complicated question
\nthan traditional platform lock-in. A startup building on Amazon Web Services
\nteaches AWS something about usage patterns, cost structures, and infrastructure
\ndemand. A company building an iPhone app teaches Apple something about consumer
\nbehavior and app categories. Those forms of learning matter, but they are still
\nmostly indirect. The platform sees where users go, how much they consume, and
\nwhich categories become popular. It does not necessarily participate in the
\ncreation of the product itself.<\/p>\n
AI platforms are different. When a startup
\nbuilds an AI-native product, the platform is often embedded in the product\u2019s
\nreasoning process, customer interactions, workflow design, software
\ndevelopment, and operational logic. The model may help write the code, shape
\nthe interface, answer the customer, summarize the legal document, structure the
\nsales process, analyze the industrial sensor data, or recommend the next
\nfinancial decision. In that environment, the platform is not merely hosting the
\ncompany\u2019s product. It is helping the company think.<\/p>\n
I believe that is the much bigger story
\nunderneath the Altman announcement. This is not a column about whether Altman
\nor OpenAI is doing anything wrong. The announcement simply gives us a useful
\nopening into a new category of business risk that every AI-native company will
\neventually face. When an intelligent platform helps you create value, what does
\nthe platform get to learn from that process? And if it learns enough, what
\nprevents it from offering some version of your company\u2019s core capability as a native
\nfeature later?<\/p>\n
If an AI platform was considering which new
\nfeatures to prioritize in the future – mining YC startups for ideas and
\nknow-how would seem strategic.<\/p>\n
\t\t\t\t\t\t\t
For the past generation, technology companies
\nhave lived inside a familiar bargain. Startups build on large platforms because
\nthe platform gives them leverage they could never create on their own. Apple
\ngave mobile developers access to distribution. Amazon gave merchants access to
\ne-commerce infrastructure. Google gave websites access to search traffic. AWS
\ngave startups instant access to global computing infrastructure that would have
\ncost millions to replicate.<\/p>\n
That bargain created enormous value. It also
\ncreated recurring anxiety. Any company that builds on someone else\u2019s platform
\nknows the platform owner may eventually move into adjacent markets. A popular
\nthird-party feature can become part of the operating system. A successful
\nmarketplace seller can find itself competing with a private-label product. A
\nsoftware tool that once filled a gap can become unnecessary after the platform
\nreleases an update. The phrase \u201cplatform risk\u201d exists because this pattern has
\nrepeated often enough to become a standard consideration in startup strategy.<\/p>\n
It is important to note that the traditional
\nplatform bargain usually preserved one important boundary. The platform could
\nsee that a startup was succeeding, but it often had to acquire the company,
\nhire the team, or reverse-engineer the product to fully capture the underlying
\nknowledge. That friction is a really important distinction. <\/p>\n
For decades, acquisitions served as one of the
\nmain ways large technology companies converted external entrepreneurial
\nexperimentation into internal product expansion. Entrepreneurs explored
\nmarkets. Startups discovered product-market fit. Big companies watched, waited,
\nand then bought the winners.<\/p>\n
That model was not merely predatory, as
\ncritics sometimes frame it. It was also a functioning part of the innovation
\neconomy. Startups took risks that big companies were often too slow, too
\nbureaucratic, or too cautious to take. Venture investors funded those risks
\nbecause the upside included not only an IPO but also the possibility of
\nacquisition by a larger platform company. The acquirer gained technology,
\ntalent, customers, intellectual property, and hard-won market knowledge. The
\nstartup and its investors received compensation for creating that value.<\/p>\n
In other words, the startup ecosystem became a
\ndistributed research and development system for the technology industry. Large
\ncompanies did not need to invent everything internally because entrepreneurs
\nwould explore hundreds of possible futures on their behalf. The important
\neconomic point is that when the startup succeeded, the platform usually had to
\npay for the privilege of absorbing the most valuable knowledge.<\/p>\n
AI may weaken that boundary.<\/p>\n
When a startup builds on an intelligent
\nplatform, knowledge that previously stayed inside the company begins leaking
\nthrough ordinary use. The AI platform can observe prompts, workflows, task
\nsequences, customer needs, failure modes, reasoning patterns, and
\ndomain-specific processes. It may see not only that a new product category is
\nsucceeding, but how that category actually works. That does not mean the
\nplatform owns the startup\u2019s intellectual property. It does not mean the
\nplatform is deliberately appropriating ideas. But it does mean the economics of
\nlearning have changed.<\/p>\n
In the internet era, a platform might see
\ntraffic. In the AI era, a platform can participate in workflow. And that
\ndifference is profound.<\/p>\n
\t\t\t\t\t\t\t
Imagine a YC startup building a legal
\nassistant for small businesses. Another builds an AI tool for construction
\npermitting. Another automates customer onboarding for regional banks. Another
\nhelps manufacturers interpret machine data from factory floors. Each founder
\nbelieves they are discovering a valuable niche. Each team spends months
\nrefining prompts, chaining models together, collecting customer feedback,
\nidentifying edge cases, and turning messy human expertise into repeatable
\ndigital processes.<\/p>\n
From the founder\u2019s perspective, these are
\nseparate companies pursuing separate markets. From the platform\u2019s perspective,
\nthey may become a map of emerging demand. Across hundreds or thousands of
\nstartups, the platform begins to see where entrepreneurs are spending time,
\nwhere customers are willing to pay, which workflows recur across industries,
\nand which AI capabilities are not yet native to the model but probably should
\nbe.<\/p>\n
Again, this is not an accusation. It is an
\nincentive structure. Every major AI platform is in a race to become more
\ncapable, more useful, and more deeply embedded in the economy. The platforms
\nthat attract the most developers and companies will gain the most exposure to
\nreal-world problems. That exposure is valuable because the next frontier of AI
\nis not simply producing better general answers. It is learning how work
\nactually gets done.<\/p>\n
AI employment<\/p>\n
For an AI platform, usage is not only revenue.
\nUsage is education. This is where an employment analogy becomes useful.
\nCompanies have long understood that people who help create business value may
\nalso create future competitive risk. Employees learn strategy, customer
\nrelationships, product plans, technical methods, pricing models, trade secrets
\nand internal processes. Contractors and software development partners may gain
\naccess to source code, design files, proprietary workflows, and market
\ninsights. That does not make employees or contractors untrustworthy. It simply
\nmeans that the relationship involves access to economically valuable knowledge.<\/p>\n
So businesses developed legal frameworks to
\nmanage that reality. Employment agreements typically include invention
\nassignment provisions, confidentiality obligations, limits on outside work, and
\nrestrictions on using company knowledge to compete directly with the employer.
\nContractor agreements and software development contracts clarify who owns the
\nwork product, who owns new inventions, and whether the vendor can reuse what it
\nlearned elsewhere. These documents exist because the law eventually caught up
\nwith a practical business truth: when multiple parties collaborate to create
\nvalue, ownership and competitive boundaries must be defined before the
\nrelationship breaks down.<\/p>\n
Now companies are forming similarly intimate
\nrelationships with AI platforms, but the legal framework has not caught up.<\/p>\n
\t\t\t\t\t\t\t
The modern AI platform does not fit
\ncomfortably into any familiar business category. It is not merely a vendor,
\nbecause vendors usually perform defined services within a contractual scope. It
\nis not merely a software tool, because tools do not reason through strategy,
\ngenerate product ideas, write code, or interact with customers in natural
\nlanguage. It is not an employee, because it has no legal personhood, no duty of
\nloyalty, and no independent contractual capacity. It is not a partner, at least
\nnot in the traditional legal sense, because most companies do not negotiate
\nmutual obligations with the model itself.<\/p>\n
And yet, functionally, AI systems are
\nbeginning to perform elements of all these roles.<\/p>\n
This is why the recent habit of calling AI
\nagents \u201cemployees\u201d is more than a cute metaphor. Some companies now describe
\nagents as digital workers. Others place them on org charts. Executives talk
\nabout managing teams composed of humans and AI systems. The language may be
\nahead of the law, but it captures a real shift in how work is being organized.
\nIf an AI agent is helping draft proposals, write software, analyze customers,
\nnegotiate logistics, or design new products, then it is contributing to enterprise
\nvalue in ways that once belonged exclusively to employees and contractors.<\/p>\n
The problem is that companies are often
\ntreating AI like software in the contract while treating it like labor in the
\nworkflow.<\/p>\n
That mismatch will become increasingly
\ndifficult to sustain. If a human employee contributed to a company\u2019s product
\nroadmap and then used that knowledge to launch a directly competing business,
\nthe employer would immediately look to the employment agreement. If a software
\ndevelopment agency reused proprietary code or customer workflows to build a
\ncompeting product for another client, the hiring company would look to the
\nmaster services agreement. But when an AI platform learns from thousands of
\nsimilar interactions and later offers a native feature that overlaps with a
\ncustomer\u2019s business, the legal answer is far less obvious.<\/p>\n
Founders should not wait for courts to resolve
\nthese questions years after the economic damage is done. They should begin
\nasking them now, at the moment they choose which platform will become part of
\ntheir company\u2019s operating system. <\/p>\n
Who owns AI-assisted inventions? Can
\nproprietary workflows be used to train future models? May a platform provider
\nuse customer-specific interaction data to develop competing products? Should
\ncustomers have the right to restrict competitive use of their business
\nprocesses? Does a startup have any claim when its novel workflow becomes
\ngeneralized into a future platform capability? These are not abstract law
\nschool hypotheticals. They are the practical questions that will define the
\nnext era of AI commercialization.<\/p>\n
\t\t\t\t\t\t\t
The answer is not to avoid AI platforms. That
\nwould be like refusing to use cloud computing because Amazon also sells
\nproducts. The leverage is too great, and the competitive penalty for abstaining
\nwill be too severe. Startups, corporations, governments, universities, and
\nnonprofits will all use AI because the technology expands human capacity in
\nways that are too powerful to ignore.<\/p>\n
The answer is to recognize that AI licensing
\nagreements will need to evolve. Today, most AI contracting focuses on data
\nprivacy, training rights, security, compliance, ownership of outputs,
\nindemnification, and service reliability. Those are important issues, but they
\nlargely treat AI as software. They do not fully address what happens when an
\nintelligent platform participates in the creation of new business processes,
\nproducts, and intellectual property. They do not fully address the deeper
\neconomic relationship forming between companies and intelligent platforms. <\/p>\n
Current AI license agreements spend
\nconsiderable time defining who owns the input and who owns the output. The
\nquestion that I don\u2019t believe is adequately addressed is, \u201cwho owns the
\nlearning that happens in between\u201d?<\/p>\n
The next generation of agreements will need to
\nborrow concepts from employment law, intellectual property law, trade secret
\nlaw, and contractor agreements. We may see AI non-compete clauses that restrict
\nplatforms from using customer-derived knowledge to launch directly competing
\nproducts. We may see workflow ownership provisions establishing that novel
\nbusiness methods developed by a customer remain the customer\u2019s property, even
\nif executed through an AI system. We may see model training restrictions that distinguish
\nbetween general system improvement and the incorporation of proprietary
\nbusiness processes. We may see AI work-for-hire language clarifying ownership
\nof code, content, inventions, and processes created with substantial model
\nassistance.<\/p>\n
Some of this language will sound strange at
\nfirst, just as early software licenses once sounded strange to companies
\naccustomed to buying physical equipment. But legal categories often emerge
\nafter technology changes the structure of economic life. Industrialization
\nforced society to rethink labor laws. Mass media and computing expanded
\nintellectual property law. The internet created new debates over privacy, data
\nownership, and platform liability. AI now presses on all of those systems at
\nonce because it touches labor, invention, licensing, and competition
\nsimultaneously.<\/p>\n
The deeper issue is that intelligence itself
\nis becoming a service. For most of economic history, intelligence was embodied
\nin people. We hired it, trained it, managed it, promoted it, protected it, and
\nsometimes tried to prevent it from walking out the door with the company\u2019s
\nsecrets. Now intelligence can be accessed through an API. It can be embedded
\ninto a product, scaled across customers, updated centrally, and shared across
\nmarkets. That creates extraordinary opportunity, but it also forces us to ask whether
\nour legal frameworks still match the way value is created.<\/p>\n
An employment agreement is not really about
\ndistrust. At its best, it is about clarity. It tells both sides what belongs to
\nthe company, what belongs to the individual, what can be reused, what must
\nremain confidential, and what forms of competition cross the line. The AI era
\nneeds a similar clarity, not because machines deserve employment rights, but
\nbecause companies deserve to understand the boundaries of the relationship.<\/p>\n
\t\t\t\t\t\t\t
The Altman announcement will likely be
\nremembered in the startup world as a clever compute-for-equity offer. That may
\nbe all it turns out to be. For AI-intensive startups, $2 million in API credits
\nis real value. For OpenAI, the deal could create equity exposure to a broad set
\nof promising companies while encouraging the next generation of founders to
\nbuild on its platform. There is nothing inherently wrong with that exchange. In
\nfact, it may prove useful for both sides.<\/p>\n
But the larger significance is not the deal
\nitself. It is what the deal reveals about the direction of the economy. Compute
\nis becoming capital. Platforms are becoming collaborators. Usage is becoming
\nlearning. And the boundary between a tool that helps a company build and a
\nsystem that learns enough to compete with the company is becoming harder to
\ndefine.<\/p>\n
That is why the question sounds playful but is
\nactually serious: should your AI sign an employment agreement?<\/p>\n
My answer is yes, at least in spirit. Not
\nbecause an AI model can sign a document, and not because every platform
\nrelationship is dangerous. The point is that companies need a new class of
\nagreements that treats intelligent systems as active contributors to business
\nvalue rather than passive software tools. If the AI helps create the work,
\nparticipate in the workflow, observe the customer, and refine the product, then
\nthe company using it should know what happens to the knowledge produced along
\nthe way.<\/p>\n
The next great legal frontier in technology
\nmay not be whether AI replaces jobs. It may be whether AI has been quietly
\njoining the workforce all along, without ever signing the paperwork.<\/p>\n
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