INTRODUCTION
Most technology startups being built today are, in some measure, AI-assisted. A founding team may use AI tools to write part of its codebase, train or fine-tune a model on its own data, or ship a product whose core feature is an AI output delivered to users. Each of these is now ordinary, and each carries legal and commercial consequences that are easy to defer and expensive to unwind. Our view is that a startup should treat these as build-time decisions, settled while the product is being designed, rather than diligence-time surprises discovered when an investor or acquirer asks the hard questions.
The considerations below are the ones we see matter most in practice
1. Ownership of AI-Generated Output and Code
The first question is deceptively basic: who owns what the AI produces? Where a team uses an AI coding assistant to generate significant parts of its product, or an AI system to generate content, designs or other output, the ownership position is not automatic. The relevant issues are:
- Authorship and subsistence. Copyright protection typically depends on human authorship, and purely machine-generated output may attract weak protection or none, which affects what the startup can later claim to own and license.
- Tool terms. The terms of service of the AI tools used will often govern who owns the output and on what conditions it may be used commercially, and these vary sharply between providers.
- Provenance of training and code. Where an assistant may reproduce material derived from third-party code or content, the startup needs comfort that its product does not carry embedded infringement risk.
The practical step is to know, for every AI tool in the build, what its terms say about ownership and commercial use, and to document the startup’s own chain of title so that its core asset is genuinely its own.
2. Data Rights and the Lawful Basis to Train
For any startup that trains or fine-tunes a model, the value sits in the data, and so does the risk. The questions to answer before training begins are:
- Source and permission. Where did the training data come from, and does the startup have the right to use it for this purpose, whether by ownership, licence, or user consent?
- Personal data. Where the data includes personal information, the Nigeria Data Protection Act 2023 applies, which means a lawful basis, purpose limitation, and the data-subject rights that come with it must be respected from the outset.
- Downstream use. If customer or user data is used to improve the model, the customer contracts and privacy notices must actually permit that use, rather than being contradicted by it.
Retrofitting consent and lawful basis after a model is trained is difficult and sometimes impossible. The cleaner path is to build the data-rights position, and the record of it, at the point of collection.
3. Third-Party Model Dependence and Supply-Chain Risk
Most startups do not build foundation models. They build on top of them, through the APIs and platforms of a small number of providers. That dependence is efficient, but it is a supply-chain exposure that should be understood:
- Continuity. What happens if the provider changes its pricing, deprecates the model version the product relies on, or withdraws access entirely?
- Terms and use restrictions. Provider terms may restrict certain uses, disclaim liability for outputs, or reserve rights that conflict with the startup’s own commercial promises to its customers.
- Concentration. A product architected around a single provider inherits that provider’s outages, policy changes and commercial priorities.
The mitigations are commercial and architectural as much as legal: understanding the provider’s terms, avoiding single points of failure for critical functions where feasible, and ensuring the startup’s own customer commitments do not promise more than its upstream dependencies allow.
4. Liability for AI Outputs
When an AI feature is put in front of users, the startup becomes answerable, in some measure, for what it produces. The exposure takes several forms:
- Accuracy and reliance: Where users rely on AI output to make decisions, inaccurate, misleading or harmful output can generate claims, particularly in regulated or advice-adjacent contexts.
- Infringement and unlawful content: Output that reproduces protected material or breaches another law does not cease to be the startup’s problem because a model produced it.
- Allocation: The customer terms of service should set out, clearly and lawfully, the intended use of the AI feature, its limits, and how responsibility for reliance on its output is allocated.
The point is not to disclaim everything, which is neither possible nor good practice, but to be deliberate about the role the AI feature plays and to align the product’s promises, its disclaimers and its actual behaviour.
5. Confidentiality and Inputs
A quieter risk is what the startup, and its own users, put into third-party AI tools. Where confidential business information, customer data or trade secrets are entered as prompts into an external system, the startup needs to know how that provider handles inputs, whether they may be retained or used for training, and whether that treatment is compatible with the startup’s confidentiality obligations to its own customers. This is both an internal-policy question, governing what staff may put into which tools, and a contractual one, governing what the startup promises its customers about the handling of their data.
6. Regulatory Direction and Governance
AI-specific regulation is developing rather than settled, in Nigeria and globally, and a startup does not need a heavyweight compliance function to prepare for it. What it needs is a light but real governance posture:
- A record of decisions. Knowing which models and tools are used, on what data, and under what terms, so the position can be explained rather than reconstructed.
- Transparency where it matters. Being clear with users where AI is materially involved in a decision or output that affects them.
- Adaptability. Building the product and its contracts so that emerging obligations can be accommodated without re-engineering the core.
A startup that can show it has thought about these questions is in a far stronger position with regulators, enterprise customers and investors than one that has not.
CONCLUSION
AI-assisted development is now more common and the startups that handle it well are the ones that answer them early. The recurring theme across all six considerations is the same: ownership, data rights, dependence, liability, confidentiality and governance are cheap to get right at the design stage and costly to fix under the pressure of a funding round, a customer’s security review or a dispute.
For a founder, the practical takeaway is to keep a clear record of the AI tools and data the product depends on, read the terms that govern them, align the product’s customer promises with what those terms actually allow, and treat the startup’s own intellectual property and data position as the asset it is. This groundwork is what turns AI-assisted development from a latent liability into a defensible advantage.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
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