The obvious way to bring both sides together is to allow distillation for open weights models by American companies.

Meta being back in the open weights game is a very big deal. There have been 3 major training (SFT/RL) periods since the ChatGPT moment: 1. 2023 when the closed models weren't good enough for many use-cases and weren't enterprise ready (ZDR, security, latency, hyperscaler support). AlphaSense was big on fine-tuning (including on Llama 2/3) in this period and it helped us launch features like Smart Summaries at sufficient reliability. But as closed models accelerated (Sonnet 3.7, GPT-4o and especially reasoning models like o1) the market including AlphaSense re-focused around the labs to power features like Generative Search and Deep Research (still maintained custom models for narrower data processing and ranking use-cases). 2. 2025 when the labs released their fine-tuning endpoints and a number of startups hyped these offerings. This was always a flash-in-the-pan and we didn't take it seriously. 3. Right now. The ecosystem around training is fully here with data generation, RL infra and American open weights models all feeding off each other. Once again at AlphaSense, we see a massive opportunity to increase the quality, reliability and efficiency of our AI offerings by training custom models. In particular we are very focused on models for Search - specifically context collection across our qualitative and quantitative tools and data which continues to be the biggest bottleneck and opportunity for progress in AI in Finance.

This is correct and we are already seeing glimmers of recursive self-improvement from internal use accelerating SuperAnalyst development. But @AlphaSenseInc we are taking this idea even further. In Finance, a growing share of our workload leverages recurring structures: supply-chain relationships, channel check signals, and the key debates around companies, industries, and cross-sector themes. Today, an agent reconstructs these from hundreds of documents, per user, per query, at run-time. Once you see agent output as a knowledge asset, a strategic question follows immediately: why wait for the request? We're deploying agents that pre-compute them as first-class, queryable datasets: supply-chain graphs assembled across filings, transcripts, and expert calls; channel-check syntheses coordinated by AI interviewers; key-debate ledgers maintained per ticker with the bull and bear evidence attached, updated as new documents land. Our SuperAnalyst starts from the pre-computed structure and spends inference only on the delta, each new question, new document, new angle leading to better consistency, lower latency, and cleaner citations as side effects. Just like standardized fundamental data helped analysts avoid extracting the same data manually from unstructured company disclosures, we are now structuring the next major layer of analysis that everyone has been doing independently.

Standard RAG tools break down exactly when questions get complex. Not a retrieval failure, an architecture one. Filtering and synthesizing are different cognitive tasks; forcing both into one pass kills quality. Read more: alpha-sense.com/resources/pr…

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