When Google unveiled its latest Gemini model last week, the internet erupted. Multiple outlets reported that OpenAI was feeling the pressure. According to The Information, a leaked internal memo from Sam Altman warned employees of “rough vibes” and “economic headwinds” in the wake of Google’s performance gains. The memo, reportedly written last month, contrasted sharply with Altman’s public trillion-dollar ambitions and cautioned that OpenAI’s revenue growth could slow to single digits by 2026. And one viral tweet amplified the moment even further, pointing out Google’s structural advantages in plain language: the world’s data, its own chips, enormous cash reserves, and distribution to billions of users across YouTube, Search, Gmail, Maps, and Android.
Whether every detail was precise or exaggerated almost did not matter. The narrative caught fire because it tapped into the storyline people love to watch: Google versus OpenAI, Gemini versus GPT, and the latest round in the heavyweight fight for AI leadership.
But while the tech world argued over who was winning the week, institutional finance saw something very different. Banks, credit funds, and large asset managers were not celebrating Google’s surge or predicting GPT-5’s comeback. They were asking a more strategic question: Why would we bet our underwriting infrastructure on one model at all?
A few weeks before this latest release, I made this exact point on stage at the Private Markets AI Summit in New York, speaking to a room full of private credit managers, private equity investors, and LPs. I was sharing the stage with senior executives from BlackRock, Mubadala, and Carta, and the message resonated across the panel. The real risk is not choosing the wrong model. The real risk is choosing any single model at all. “If the frontier shifts every quarter,” I told the audience, “why would you hardwire your underwriting process to one engine and hope it stays on top?”
That moment mattered because anyone actually deploying AI in private markets already feels the volatility. And it is one of the core reasons we intentionally built F2 as an LLM-agnostic, agentic platform designed around the workflows private markets investors use every day rather than the model that happens to be popular.
Back in April, writing in these pages, I argued that general-purpose LLMs were not enough for private credit. As I wrote then, “Financial analysis is a different beast. It is multi-step, math-heavy, and intolerant to hallucination.” That remains true. But what has become even clearer since that column is how fast the model landscape is shifting and how dangerous it is to build an entire investment machine on a single frontier model.
A year ago, GPT-4 looked unassailable. Then Anthropic surged with Claude Opus. Meta surprised the ecosystem with Llama 3. X released Grok and began iterating faster than expected. Now Google has reasserted its claim to the frontier using data scale, chip infrastructure, capital, and distribution advantages no competitor can match.
Tech observers see a leaderboard.
Financial institutions see a risk profile.
They operate on multi-year adoption cycles. AI models release breakthroughs on timelines measured in months. That mismatch is no longer a theoretical concern. It is structural.
I know this because I lived the old world. In my private equity days, I was the analyst buried in Excel at 3 AM trying to tie out chaotic data rooms and model deals that had a 90 percent chance of dying. It was unscalable, and it was a tax on the entire asset class. When I later founded Arc and eventually built F2, I was not chasing AI hype. I was trying to fix the workflows I wished existed back then.
One of the earliest lessons we learned was straightforward. Relying on a single model is a structural disadvantage. We benchmarked general-purpose LLMs like GPT and Claude on private credit workflows such as leverage calculations, net debt, cohort analysis, and Excel parsing. Their performance varied unpredictably from task to task. Finance is not a single problem. It is a chain of specialized problems that no single model handles perfectly.
But the deeper issue is competitive, not technical.
A private credit fund that ties its underwriting stack to one model is anchoring itself to a moving target. Even if that model remains perfectly competent, the moment another model becomes materially better in reasoning, extraction, or numerical analysis, the fund locked into a single-model architecture slips behind peers who can adopt the frontier instantly.
Their workflows do not break.
Their relative performance declines.
And in private credit, relative performance determines outcomes.
Funds using LLM-agnostic systems qualify borrowers faster, diligence more deals in the same amount of time, and produce richer memos grounded in cleaner data. These advantages compound. In a hypergrowth market where LP dollars flow toward managers who demonstrate analytical leverage, a fund running yesterday’s model eventually generates lower returns than peers who evolve with the frontier.
Once that gap opens, it widens every quarter.
This is why institutional finance is moving toward vertical, LLM-agnostic AI platforms. Not because it is trendy, but because it is economically rational.
Instead of choosing sides, lenders are adopting multi-model systems that continuously evaluate the entire frontier including Gemini, GPT, Claude, Llama, and Grok. Each workflow step is routed to the model that performs best for that specific task. Extraction may go to one engine. Reasoning to another. Forecasting to a third. Memo synthesis to a fourth.
This approach does not care who won the week.
It cares which model produces the most accurate output right now.
The most powerful part of this architecture is that every model release, including Google’s latest, becomes an upgrade. The LLM wars do not destabilize the system. They enhance it.
The future of financial automation will not be determined by whichever model tops the benchmarks this month. It will be determined by the firms building systems that can absorb that volatility without being defined by it.
Banks do not need Gemini to beat GPT.
They need underwriting platforms that make the question irrelevant.
In a world where AI innovation moves at quarterly speed and competitive advantages compound faster than ever, the winners in private credit will be the ones who embrace multi-model, vertical AI and the discipline, precision, and scalability it unlocks.
The new Gemini model may have dominated the headlines. But for anyone building the future of underwriting, the real lesson is simple. Do not pick a champion. Build an architecture that ensures you never have to.
And for those of us who once spent entire weekends reconciling broken spreadsheets and messy data rooms, that future cannot come soon enough.