Thoma Bravo Is `Model Agnostic,' Says Boro

Thoma Bravo says it is model-agnostic on AI and maintains relationships with OpenAI, Anthropic, and Google, highlighting a flexible vendor strategy. Seth Boro also discussed AI cybersecurity and deployment costs, underscoring practical implementation challenges rather than a specific financial event or new quantitative disclosure.

Analysis

This is less about model choice and more about procurement power shifting toward buyers with scale. When a major PE platform is model-agnostic, the implicit message to the market is that the “winner-take-most” layer may sit above the model, in workflow integration, security, and distribution rather than raw frontier performance. That is mildly negative for any single-model moat narrative and relatively positive for platform software vendors and cloud infrastructure that can monetize across model providers.

The second-order effect is that cybersecurity becomes the gating function for AI deployment budgets. Enterprises are increasingly willing to pay for guardrails, data loss prevention, identity controls, and auditability before they expand tokens spent on inference; that should support security vendors with AI-native messaging and hurt pure-play model vendors if pricing pressure intensifies. Over the next 6-18 months, the most important variable is not benchmark superiority but whether deployment costs fall fast enough to move AI from pilot to production at scale.

For GOOGL, the setup is nuanced: a model-agnostic market reduces the risk that any one competitor’s model leap becomes durable, but it also means Google can compete on price/performance and distribution without needing to “win” the frontier race outright. The contrarian read is that consensus may be overestimating the speed of commoditization; enterprise buyers still care about latency, data residency, and integrated stack economics, which favors hyperscalers. In other words, the near-term winner is not necessarily the best model, but the cheapest trusted full-stack deployment.

The main risk to the thesis is a step-change in model quality or a security breach that freezes AI rollouts for a quarter or two. On the upside, if security concerns are solved and inference costs continue to fall, enterprise adoption could re-accelerate sharply into 2025, benefiting the infrastructure layer first and the application layer later.

Key Decisions for Investors

• Long GOOGL on a 3-6 month horizon: model-agnostic procurement lowers single-model winner risk and preserves Google’s ability to win on distribution, stack integration, and pricing; favorable risk/reward if enterprise adoption broadens.
• Pair trade: long GOOGL / short a basket of premium-valued pure-play AI model beneficiaries over 2-4 months; thesis is that model differentiation compresses faster than currently priced while hyperscalers capture the spend.
• Long cybersecurity exposure via high-quality platform names over 6-12 months; use the AI deployment cycle as a demand tailwind for identity, data protection, and governance tools as enterprises move from pilots to production.
• Avoid paying up for standalone model narratives until evidence shows durable pricing power; if token prices fall another 20-30% over the next two quarters, margin assumptions for model-centric names likely reset lower.