One of the most consistent questions we receive from founders is some variation of: "We want to build a global company from day one, but we are not sure how to structure ourselves to do that." It is a genuinely important question, and the answers are more nuanced than most founders initially expect.
In this article, we draw on our experience working with 28+ portfolio companies across 16 countries to share practical guidance on the three most consequential decisions AI founders face when building a global company: legal and entity structure, international hiring, and data compliance architecture.
Entity Structure: Where to Incorporate and Why
The default choice for AI companies seeking US institutional venture capital is to incorporate as a Delaware C-Corporation. This remains sound advice in 2025, particularly if your primary investor and customer markets are in North America. Delaware C-Corps are well understood by US venture funds, facilitate clean cap table structures, and offer predictable governance frameworks that institutional investors require.
However, for founders genuinely building a global company — with meaningful operations, customers, or hiring in Europe from the outset — the picture is more complex. Several of our European portfolio companies have adopted a holding company structure: a Delaware C-Corp at the top of the entity stack, with wholly-owned operating subsidiaries in Germany, France, the Netherlands, or Estonia depending on where they are hiring and selling.
Estonia deserves special mention. The Estonian e-Residency program, combined with the country's progressive startup legislation, makes it exceptionally attractive for digital companies with distributed teams who want to operate within the EU regulatory framework from day one. We have three portfolio companies with Estonian operating entities, and all three have found the setup faster, cheaper, and more flexible than alternatives.
For AI companies with significant operations in Asia-Pacific, a Singapore holding structure — with wholly-owned subsidiaries in the relevant operating markets — is typically the most efficient approach. Singapore offers strong IP protection, a sophisticated financial infrastructure, and clear pathways to operating in Indonesia, Vietnam, and other high-growth ASEAN markets.
International Hiring: Speed vs. Compliance
The tension between hiring speed and employment compliance is one of the most underappreciated challenges for early-stage global AI companies. The instinct for most founders is to hire the best person for each role, regardless of where they live. That instinct is correct in principle but requires thoughtful execution in practice.
For companies without local entities in a target hiring market, employer of record (EOR) services provide an efficient compliance pathway. Services like Deel, Remote, and Oyster HR allow companies to hire employees in 100+ countries without establishing a local entity — the EOR acts as the legal employer, handles local payroll, benefits, and compliance, and charges a per-employee monthly fee that typically ranges from $500 to $700.
EOR services work well for 1 to 3 employees in a given country. Once you reach 4 or more employees in a jurisdiction, the economics and strategic rationale for establishing a local subsidiary typically become compelling. Local entities also enable you to bid on government contracts, qualify for R&D tax credits, and credibly claim local market presence to enterprise customers who care about vendor domicile.
One critical hiring consideration for AI companies specifically: technical talent with expertise in ML infrastructure, LLM fine-tuning, and AI safety is globally distributed but highly concentrated in a handful of talent clusters. The ability to hire from Cambridge, Zurich, Montreal, and Singapore — not just San Francisco — is a genuine competitive advantage for companies that build the infrastructure to support it.
Data Compliance Architecture: Design for Regulation, Not Reaction
For AI companies handling personal data — which is effectively all of them — building a compliant data architecture from the beginning is dramatically cheaper and faster than retrofitting compliance after the fact. We have watched multiple portfolio companies spend $500K+ in legal and engineering costs remediating data architectures that were never designed with compliance in mind. Do not let this happen to you.
The three regulatory frameworks that matter most for global AI companies in 2025 are GDPR (EU and UK), CCPA/CPRA (California and expanding), and the EU AI Act (recently entered into effect). A well-designed data architecture can be compliant with all three simultaneously, but it requires intentional decisions at the schema design stage.
The most important principles for compliant AI data architecture are: data minimization (only collect what you actually need), purpose limitation (only use data for the purposes disclosed), geographic data residency controls (ensure EU personal data can be isolated and processed within EU jurisdiction), and audit trail completeness (maintain records of all processing activities sufficient to respond to regulatory inquiries or data subject requests within required timeframes).
Intellectual Property Strategy for AI Companies
The IP landscape for AI companies is evolving rapidly, and founders need to be thoughtful about what they can and cannot protect. Core model architectures, training methodologies, and specific techniques used to fine-tune foundation models for domain-specific applications may be patentable as technical inventions, provided they represent a genuine technical advance over prior art.
For most early-stage AI companies, the most defensible form of IP is not patents but rather proprietary training data, domain-specific ontologies, and the accumulated model improvements that result from real-world deployment and feedback loops. These assets are difficult to replicate quickly, improve compounding returns over time, and are well-understood by institutional investors as sources of durable competitive advantage.
Practical First Steps
For founders who are pre-seed or early in their seed fundraise, the most important global infrastructure investments to make immediately are: engage a startup-specialized law firm with international expertise, open banking relationships in your primary operating markets, choose a payroll and HR platform that supports international hiring from day one, and implement a data classification framework before you touch your first customer's data.
None of these steps requires significant capital. The total cost of setting up proper global infrastructure at the seed stage is typically $25K to $50K — a fraction of the cost of remediation after the fact, and a prerequisite for the international enterprise customers who will be critical to your growth story at Series A. Build for global from the first day of your company's existence, not the first day of your international expansion.
