There has been a long standing debate in Finance AI around "who wins": the foundation labs vs. the finance specific AI platforms (pejoratively, "the wrappers")
The pendulum in asset management has shifted back towards a strong consensus that foundation labs directly are the winners, Claude specifically. "Why work with a wrapper when I can work direct with Claude Enterprise?". And foundation labs building finance RL sandboxes and building big forward deployed engineering teams (and have endless capital) have reinforced that belief "OpenAI is hiring investment bankers, game over..."
My sense is this race isn't over, however.
Imaging you are an asset manager and you hitch your wagon to the Claude Complex:
> Opus 4.6 nerfed in earnings preview season
> Token bill arrives and after the "first hit is free" dynamic abates, your token budget explodes (and once the tool is in investor's hands, it is super hard to control usage). I'm no expert in token economics, but I do know that Uber used to sell $15 rides for $5 and I suspect Claude is selling thousands of dollars of compute for $200/month. This LLM capex burn isn't sustainable forever, and this isn't a sustainable foundation on which to transform your firm's investment process (see same $5 Uber ride now $50).
Perplexity has shown me the future with Perplexity Computer, an agentic multi-model workspace, with access to 19 models that is insanely user friendly. Guess what's not nerfed today? Research context, workflow context & enterprise security are also critical, so not necessarily arguing Perplexity Computer is the winner (they could be), but evidence like this suggests a more thoughtful bet is a multi-model approach that considers the LLM capability cycle (nerfing and leapfrogging) and token economics (using frontier intelligence economically).