Save the date! #AlphaSummit 2026 Oct 5-7 | The Glasshouse, NYC Join @AlphaSenseInc to see the future of AI and market intelligence. Community, connection, and game-changing content await. Pre-register here: events.alpha-sense.com/alpha…

Huge advancement here with our new multi-agent GenSearch that leverages qual quant tools, monitors proactively and generates real work products. We’re building an AI SuperAnalyst that learns your specific workflows. Check it out!

Solving the market debate Will people pay for AI Overlay of token consumption vs net new ARR growth at AlphaSense for your read-through… 😮

Milestone moment for @AlphaSenseInc 🎉 🎤 Hosting our first-ever #AlphaSummit Oct 6–8 in Brooklyn w/ keynote speaker Nick Saban customer execs from finance, tech, pharma, and other sectors. Don’t miss out - join us! events.alpha-sense.com/alpha…

This is the point where AlphaSense hits escape velocity. After a massive effort iterating, a few weeks ago we quietly launched fully AI-hosted Expert Calls. Feedback has been incredible. AlphaSense created a self-reinforcing flywheel where context from our system feeds a smarter AI-host. You’ll be amazed at the industry context, follow up questions etc - that come from having the context of all the prior calls synthesized by our Deep Research. A few years ago AlphaSense acquired an early but exciting business called Stream. They had a built in flywheel of buyside analysts hosting calls with experts – in exchange for charging only cost for the call, the call was transcribed, reviewed by a professional compliance team, and published in a library. This content set truly grew like wildfire – with ARR up >20x since acquisition. Then, a year ago we acquired Tegus – the leader / gold standard in the space. >50% of the midas list hosts their calls on Tegus. I am actually continually shocked at the caliber of investors hosting calls. All of a sudden AlphaSense Tegus grew to a library >200k, on pace to hit 9k / month shortly. Then came AI – LLMs have flipped from dumb to smart and can now easily make sense of the library. That then becomes a smarter set of tokens to feed in to the system → which then leads to better calls (by both clients and AI) → which leads to better information in the platform —> which leads to better context … Have no fear – the AI led calls will be separately labeled and investor led will continue to grow rapidly (just like Twitter – some people love the game [100x more love consuming]). But our search system essentially has the mind of the market and can now automatically fill the gaps. We’re in Alpha testing on externalizing the system to let clients host their own AI-led Expert Calls – reach out if you’d like to test.

Testing Deep Research: $UNH and the Medicare Crisis Hedge funds, overall, have been "slow adopters" of large language models. Early LLMs had poor numeracy, limited citations and a tendency for rampant hallucinations...not great attributes of a trustworthy tool for institutional equity analysis. Candidly, while I started a webinar series (The Cutting Edge) to explore AI tools in the investment process in the fall of 2024, up until about ~2 months ago the counsel I would give those who would ask is "pay attention, but no need for meaningful process re-engineering", with the view that the cognitive load & disruption to shifting an already established investment process to one that was AI-augmented was, on balance, not worth the hassle & the risk of erroneous analysis. And for all the talk about augmenting the investment process with AI, outside of few forward thinking "hackers" that pulled together some useful systems, most agreed with me that the tools were not ready for showtime. Over the last 1-2 months, that view has shifted. I see more and more areas of the research process where I believe these tools should be used now. A couple things clicked for me. First, "recursive prompting" has made it much easier to build useful prompts that generate useful responses. I personally use Claude Sonnet 4 to build prompts, and this allows me to speak like most finance people do (a few grunted sentences into the context window). It's so easy. And the output is so good. For example, Claude decided on its own to priorities these primary sources (agree). And built this "Special Instructions" section to avoid speculation & seek diverse sources. The dimensions that are generated from this recursive prompting structure allow me to ask a question like a normal human being, generate a ~6 page structured prompt, and get an extremely good result. I tested this out on a situation where I have some sense of what good looks like. From 2010-2021 I was a healthcare services analyst, and a damn good one. I initially hated covering healthcare services (the first investor conference I went to it felt like they were speaking Greek). But I soon learned that there was real alpha in understanding the intricacies of how these businesses work and in arbitrating the various debates that would arise (impact of minimum MLRs, exchange dumping, risk corridors, Stars scores, etc.). I loved it actually, it felt like putting together a complicated puzzle. The work was *incredibly labor-intensive*. But the half-live of investible insights in healthcare services is short. Recently, I will occasionally get a question on a name I used to know cold: United Healthcare ($UNH). In building Fundamental Edge (and raising 3 sons), I haven't had the time (or resources) to stay super current in the space. So I don't know the name so cold anymore. (it has been cut in half in recent months...a truly shocking outcome). I figured I would spend some time this Sunday evening running my recursive prompt playbook through four Deep Research tools (ChatGPT, Gemini, Claude & AlphaSense) to get "up to speed" on the $UNH situation. 1) I want to understand in depth what has happened & how we got here 2) I want to consider the reaction function menu of both companies & regulators 3) I want to quantify those scenarios, then compare that to what is baked into the stock, i.e. what is "UNH ex-MA" floor assuming 0% margin for the MA business. 4) I want to activate my antennae for what to look for from a catalyst path perspective..."if this happens, that is a sign CMS gets it and is supporting this industry" or "if this happens, MA is cooked and will see a prolonged retrenchment". Then, in the seat, I'm dog after a bone looking for those clues. To do this correctly, I'm updating all my HMO models, my HMO data sheet, catching up on the last 8 call & investor conference transcripts for the MA companies, then doing a deep (and super nerdy) reading stack of CMS reports, MedPac reports, Kaiser reports, etc. And speaking with a dozen experts, the key sell-side analysts, and representatives from the companies (multiple times). Out of all of this, a mosaic of insight starts to form, and that mosaic informs really one quantitative output: 2028 MA revenue & margin (and the trajectory to that point). This is what I think most silicon valley types don't get about stock research. Most stock situations can really be boiled down to 1 or 2 differentiated insights. Everything else in the process is an (important) wrapper around those insights. A standalone AI-summary of a 10-K or a mediocre AI-slop company primer doesn't really do anything useful. And accelerated pathway to deeper insight, with more conviction, on a differentiated view of the key driver of the business - now we are talking. AI can be marginally helpful if the tools can help build the wrapper with more speed & reliability, but they can be truly game-changing if they can both accelerate the speed and enhance the quality of insight & differentiation on the key drivers of the business. To me, this is what these tools are becoming. Don't take my word for it. Try this out on your own. 1) Identify an investment debate 2) Ask an LLM (I prefer Claude Sonnet 4) to create a detailed prompt from your plain-English question 3) Run it through 3-4 Deep research wrappers The reports I have sitting here are mind-blowing, to be honest (except Gemini. Gemini sucked, and Claude Deep Research was "meh"). But the combination of reading the ChatGPT report and AlphaSense report had me honestly feeling pretty up to speed (with the caveat that I have a decade of context to fall back on). It's not the ending point of the research process, by any means, but with these reports I feel ready to start getting on the phone and doing my field research. The scary part is ChatGPT 03 with Deep Research, in this instance, is not integrating any of my own research. If I could pipe in models, data sheets, mgmt meeting notes, sell-side research, expert network transcripts...it's scary how good this can become, and in short order. Enough to take me out of the skeptic camp, into the converted. I highly urge you to try it out for yourself (and let me know your thoughts).

Fresh update: Token inflection accelerates (Even with 1 day of market holiday) Chain of thought Test time compute 😮

AlphaSense launches its own Deep Research for the web AND your enterprise files — here's why it matters venturebeat.com/ai/alphasens…

AlphaSummit is more than a conference. It’s a front-row seat to the future of GenAI market intelligence with @AlphaSenseInc. You don’t want to miss it this Oct 6–8 in Brooklyn. Secure your spot early → events.alpha-sense.com/alpha…

See @AlphaSenseInc x Cerebras in action. This is what it looks like to get critical business insights 10x faster.

I fucking love CMU. Looking through the course catalog, there's like >25 courses that cover LLMs and topics on the frontier of AI. This is what happens when you give Machine Learning, Language Technology, Robotics, etc their own entire departments, as god intended 🫡

Most important trend in AI

Watch Falcon 9 launch 22 @Starlink satellites to orbit x.com/i/broadcasts/1eaKbgkbV…

We're thrilled to announce two major platform advancements that will revolutionize the way our customers surface market intelligence! AlphaSense Assistant: a generative AI chat experience that transforms how users can intuitively extract insights from hundreds of millions of premium documents. Enterprise Intelligence: a first-of-its-kind offering that delivers AlphaSense’s AI-powered search and summarization capabilities securely atop customers' proprietary organizational knowledge. As we look to the future, we're excited about how innovations in #generativeAI will help us keep our customers ahead of their markets. Learn more about how Enterprise Intelligence and Assistant can help you create more tailored views of your markets: alpha-sense.com/blog/news/en…

Today, we are incredibly proud to announce a $150M Series E funding round led by the iconic @bondcap, bringing our valuation to $2.5 billion. You can learn more about this milestone and why BOND believes in AlphaSense here: prnewswire.com/news-releases…

What is the economic potential of generative #AI? How will AI shape technology and society? Get these questions answered in this exclusive Stream from @AlphaSenseInc featuring Eric Schmidt and David Solomon. bit.ly/3ORb4iE

Earnings season begins this week, and our #AI-generated summaries cover the most important topics discussed during the calls. Smart Summaries will help you save time while providing: ✅ Higher accuracy ✅ Verifiable information ✅ Trustworthy content ✅ Private data Our Smart Summaries tool benefits from over 10 years of investment in AI specifically developed for market intelligence! Learn more about how Smart Summaries work: alpha-sense.com/blog/product… Watch video: youtu.be/c2vMCeEGiiM

Join us for AlphaSense Advantage 2023, our inaugural Market Intelligence Summit. This two-day virtual event offers a unique opportunity to learn firsthand how business analysts, competitive intelligence professionals, business leaders, and executives across the financial services and corporate markets navigate significant challenges in their industry! 📅 October 4 - 5 📍 Virtual | Free to attend #ASAdvantage2023 Save your spot now ⤵️ advantageregistration.alpha-…

We spoke to Chris Ackerson about how AlphaSense introduced generative AI to its AI stack — and how the company intends to expand its product into a full-on AI personal assistant. bit.ly/42yYDf9 #AI #ArtificialIntelligence #LLMs

Former Group Head at $GOOGL (8 years) Cloud & AI at Google Research - On the verge of a market explosion. Public AI cloud spend today :$10B sees it :$100B - Google could have launched ChatGPT-like tech 2 years ago - LLMs are only one use case of sequence-to-sequence AI