The primary bottleneck for advancing frontier AI has shifted from pre-training on massive, general datasets to post-training with specialized, high-quality data from human experts.
In the human-generated AI data market, the only defensible long-term competitive advantage is proprietary access to a large, engaged community of experts, not technology or process.
AI models have surpassed the capabilities of human generalists, making subject matter experts in fields like law, accounting, and STEM essential for the next phase of AI improvement.
The current market for high-quality AI training data is supply-constrained, with effectively unlimited demand, allowing providers to sell nearly all the quality data they can produce.
A direct-to-lab business model is superior to working through intermediaries, as it allows for better collaboration and captures more value from the expert network.
18-24 months ago
Lord claims that performance gains from pre-training AI models on internet data began to asymptote, creating the market need for high-quality, expert-led post-training data.
Pre-Launch (Recent Past)
Handshake made the strategic decision to stop providing its expert user data to intermediary data companies and pivot to a direct-to-lab model.
January (Current Year)
Handshake officially launched its new AI data business, with Lord dedicating over 80% of his time to the initiative.
Four months post-launch
The new AI business reportedly achieved a $50 million Annual Recurring Revenue (ARR) run rate.
Present
The AI division is on pace to exceed $100 million ARR in its first year, is working with 6-7 frontier labs, hiring aggressively, and turning down multi-million dollar projects due to being at capacity.
▶The Strategic Pivot to an AI Data BusinessApr 2026
Lord details Handshake's rapid and decisive entry into the AI data market. This involved incubating a new, independent business unit, dedicating over 80% of his own time as CEO, and strategically cutting off intermediary data companies to work directly with AI labs.
This theme demonstrates that established companies with unique assets (in this case, an expert network) can create significant enterprise value by repurposing them, but it requires intense leadership focus and a willingness to disrupt existing business relationships to capture a new, high-growth opportunity.
▶The 'Post-Training' Inflection Point in AIApr 2026
Lord consistently argues that AI development has hit a wall with pre-training on general internet data. He claims the majority of performance improvements now come from post-training processes, which require nuanced data from human experts to enhance model reasoning in specialized fields.
This positions the value chain as shifting from raw data scale to data quality and specificity, suggesting that companies with access to curated, high-expertise communities will hold significant leverage over AI labs in the coming years.
▶Market Dynamics of a Supply-Constrained ResourceApr 2026
Lord portrays the market for high-quality, expert-generated AI data as having 'unlimited demand.' He asserts that the only sustainable moat is not technology but proprietary access to an audience, contrasting Handshake's organic network with competitors' high-cost expert acquisition strategies.
This framing suggests the AI data sector is currently a resource-driven market akin to commodities, where the primary challenge is scaling supply, not finding buyers, making exclusive access to expert labor the most critical factor for success.
▶The Future of Human-in-the-Loop AIApr 2026
Lord predicts that human experts will be indispensable for AI training for at least the next decade, up until the point of Artificial Superintelligence (ASI). While acknowledging the utility of synthetic data in verifiable domains, he maintains it cannot replace human-generated data for complex reasoning tasks.
This long-term view provides a clear investment thesis for Handshake's AI business, framing it not as a temporary bridge technology but as a foundational, decade-long component of the AI development lifecycle.