AlphaSense

AlphaSense is the AI platform behind the world’s most important decisions, from Wall Street to corporate boardrooms. (Not investment advice)

[AlphaSense @AlphaSenseInc]

Today, AlphaSense launches SuperAnalyst, our next-generation agent powering high-value financial and strategic workflows.

For 15 years, we've built the trusted intelligence system for the investors and operators making the biggest bets in business. Now SuperAnalyst takes it further, operating 24/7 to plan, execute, monitor and update complex workflows using AlphaSense intelligence, customer context and purpose-built AI infrastructure.

Trusted by the world's largest enterprises, SuperAnalyst brings together domain-specific AI, our Context Graph and purpose-built Skills to execute workflows at superhuman speed, grounded in trusted intelligence. Read the full announcement: https://t.co/CTxSGepmYj

[AlphaSense @AlphaSenseInc]

We are just 1️⃣ week away from our CEO and Founder Jack Kokko speaking on the Main Stage @TomorrowXSummit: featuring many of the most prominent CEOs across AI and TMT. Jack will be joined by fellow CEOs and Founders from @GoldmanSachs, @cerebras, @MicronTech, @Dell, @AMD, @intel, @nvidia and many more.

We will have much more to share from this unique moment next week as it addresses the future of investment research. Stay tuned for more to come! https://t.co/lNME7B3qth

[AlphaSense @AlphaSenseInc]

With labor tight and costs rising, more firms are turning to humanoid robots. But where is the value accruing?

As hardware commoditizes, it is shifting to software and data, the robot's brain. Data sharing is the catch. Learn what the experts are saying in our new Outlook: https://t.co/kpfDUFmBFW

[AlphaSense @AlphaSenseInc]

If you are interested in more interviews like this one, make sure to follow us! Stop second-guessing. Whether you're tracking market shifts, exploring new opportunities, or need clarity on complex decisions, our extensive transcript library and custom expert calls give you the competitive edge you need. ⬇️
https://t.co/hodpDgEONt

[AlphaSense @AlphaSenseInc]

Interview with an industry expert on how AI is changing the way software gets built ($MSFT, $ANT, $GOOGL):

- The expert says Copilot use has grown sharply this year because people now trust AI far more than they did in 2024, when the expert would have written code by hand rather than spent hours checking a model’s output. Models have matured so much that the expert now hands them a proper spec and only validates a few things, pointing to $ANT's Opus and Fable models as well ahead on software intelligence.

- According to the expert, employees are now fully using AI to build and keep asking for more credits, because with nobody interested in writing code by hand, running out of budget leaves them idle. Each person gets a monthly budget for Copilot and Claude, while Gemini works on a flat fee with automatic throttling. Unused credits flow to others, and extra use goes through leadership approval. The expert expects this spend to double within a year and possibly reach four times today's level in about three years, if it starts producing revenue.

- According to the expert, the return from AI shows up directly in how fast features reach customers. When a client needs a new product capability, the work normally passes through a number of units, which takes many months. With tools like Claude and Copilot, the same team can hand over the architecture and requirements and have features built, tested, documented, and submitted for review in days, cutting delivery time sharply.

- The expert emphasizes that $GOOGL has tried to challenge GitHub, building its own coding environment around Gemini, but is nowhere close to displacing it. GitHub has been the source of truth for code for many years, and the volume of code and solved problems committed there is unmatched, which the expert compares to the gap between Chrome and Bing, where people go to the leader for a reason. The expert also highlights that Copilot works as an interface connecting many models, including those from $ANT, $GOOGL, and open-source providers, so developers are not tied to a single one.

- The expert sees Claude Code and Claude Cowork as well ahead of any other agentic system for software engineering, mainly on accuracy and on integration at speed and scale, and his main advice to the Copilot team is to copy that approach. He believes GitHub is better placed than anyone to close the gap, since codebases across the globe already sit there, so a Claude-like agentic experience in the Copilot desktop app, even at a higher price per license, would be easier to adopt than bringing in Claude, which still has to connect to GitHub. GitHub could also offer the same capability across models beyond $ANT's.

[TomorrowX Summit @TomorrowXSummit]

One week until TomorrowX Summit, with support from AlphaSense (@AlphaSenseInc).

The CEOs, investors, and operators at the center of the AI buildout take the main stage. See you in Austin, November 17-18.

Register at the link in the replies.

[AlphaSense @AlphaSenseInc]

Expert calls monopolize your time: sourcing, screening, scheduling, and the write-up.

Or, you can hand the whole thing to an agent that finds the experts, runs the call, and sends you the transcript and takeaways.

Watch Elizabeth Koskiewicz, Program Manager of AI-led calls at AlphaSense, show how it works.

[michelle brophy @michbrophy]

With the help of @AlphaSenseInc's SuperAnalyst we curated recent expert call content on AI infrastructure.

See our SuperAnalyst dashboard ⬇️

https://t.co/5vwoD6xtVJ

The common thread is that hyperscalers still borrow cheaply and are shifting more of their builds into JVs and leases, while lenders are pulling back from neoclouds.

$NVDA $CRWV $META

[AlphaSense @AlphaSenseInc]

If you are interested in more interviews like this one, make sure to follow us! Stop second-guessing. Whether you're tracking market shifts, exploring new opportunities, or need clarity on complex decisions, our extensive transcript library and custom expert calls give you the competitive edge you need. ⬇️
https://t.co/hodpDgEONt

[AlphaSense @AlphaSenseInc]

Interview with an industry expert on why GPU utilization decides whether to build or buy AI inference ( $AMZN, $MSFT, $NVDA ):

- The expert says GPU compute is not elastic the way CPU compute is. The cloud providers the expert's team works with always want a three-year contract, and adding GPUs in the middle of a quarter takes at least a month of lead time on top of that commitment. Because of the three-year commitment, the team keeps paying for a GPU instance even after turning it down, so it has had to build engineering optimizations to work around the problem, while CPU capacity can simply be bought on spot instances when demand rises.

- The expert sees GPU availability by region as a real challenge, since GPUs are only available in certain regions while customers span hundreds of regions, so routing traffic is difficult, especially when customers have data retention or locality requirements. To manage this, GPU capacity is spread across AWS, Azure, and GCP in selected regions.

- According to the expert, $SNOW works with the three largest cloud providers in a rough 60%, 30%, and 10% split, with AWS clearly dominant. $SNOW is described as one of AWS's largest customers, having announced a $6 billion renewal of its spending commitment on its latest earnings call.

- On hardware, everything is $NVDA, with the mix chosen on price-performance. Today it is mainly A10G, H200, and B200, plus some L40s. Asynchronous batch workloads open up better optimization opportunities, since they favor throughput over latency, so L40s can replace B200S for much better price-performance. The dollar split across SKUs is dynamic and subject to change.

- The expert notes that GPU utilization depends on product usage, which fluctuates a lot. According to him, break-even sits at about 40% average utilization, counting quiet periods like nights and weekends, with business hours running higher. At that level, a company is indifferent between sending inference requests to Bedrock and self-hosting an open-weight model, and anything above it makes self-hosting more favorable, which makes utilization the key lever in the build-versus-buy decision.

[AlphaSense @AlphaSenseInc]

Data centers are racing to double capacity by 2030, but the grid can't keep up.

Interconnection waits of five to seven years, a 57 GW power shortfall, and public backlash against new data center builds are key constraints. But the upside is real for operators who can navigate these complex headwinds to understand where the market is heading.

Read our breakdown of the key data center trends for 2026 and beyond. https://t.co/A5a9LBAdir

[AlphaSense @AlphaSenseInc]

What if every banker on your team could cover 15% more companies?

A major investment bank put AlphaSense through its paces over many months and proved it could increase average coverage by up to 15%, making its bankers much more efficient.

Chris Ackerson sits down with David Weisburd to discuss how AI is changing the analyst role, why vertical AI can outperform general-purpose models in financial research and why proprietary data may become one of the most important competitive advantages in AI.

Watch now ⤵️
https://t.co/I4eWbNcO3v

[Chris Ackerson @cackerso]

Today @AlphaSenseInc, we're launching the SuperAnalyst MCP,  putting SuperAnalyst to work wherever your agents run. Rather than one more data pipe for your agents to brute force there way across, SuperAnalyst MCP allows them to delegate research to the world's best research system - thousands of sources, market-leading search, purpose built AI - so your agent spends its tokens on the problem you actually want solved.

We measure dramatically higher quality at 3x lower cost with ever claim cited to its source down to the sentence.

SuperAnalyst has become the platform for technology teams to automate complex investment research and build institutional grade market intelligence applications. Highly encourage folks to check it out: https://t.co/kKdaAFr3I1

[AlphaSense @AlphaSenseInc]

Your AI agent is only as good as the content it can access.

The fix isn’t a better prompt. It’s an agent built on the latest models and grounded in broker research, expert calls, and earnings transcripts, with every output traceable to its source. Seamus McAvoy, Senior Customer Success Manager, shares the question he hears the most in conversations about AlphaSense.

[AlphaSense @AlphaSenseInc]

Your team is tracking 10 competitors, but only 2 changes will affect next quarter’s pipeline.

Sorting signal from noise doesn’t have to mean a week of manual work. SuperAnalyst, our always-on agent, builds cited battlecards, flags where new evidence contradicts your existing positioning, and keeps context across sessions. That frees CI teams to judge what matters instead of stitching outputs together.

See how our own CI team uses it, from competitor dashboards to seller-ready talk tracks: https://t.co/iL1XAGD2Qd

[AlphaSense @AlphaSenseInc]

👀 Win rates tell you what happened. They won’t tell you what’s coming.

The good news: you CAN see what’s next. At #AlphaSummit 2026, leaders from Salesforce, Pfizer, Mondelez, CAVA, Keurig Dr Pepper, and sweetgreen shared how they look beyond internal metrics, test each signal against other data, and turn confirmed insights into action.

See how corporate teams are putting AI to work, with takeaways from the sessions: https://t.co/DiqJSvAGvv

[Temporal @temporalio]

Spotted in @AlphaSenseInc's agent stack: Temporal coordinating the lifecycle of secure MicroVMs 👀

[Andrew Feldman @andrewdfeldman]

Interesting look under the hood of @AlphaSenseInc's SuperAnalyst.

Lots of thoughtful engineering in how they give agents the tools to do useful research while keeping access tightly controlled.

And when speed matters, they route requests to open-weight models on Cerebras.

Worth a read if you’re building agents:

[AlphaSense @AlphaSenseInc]

When an AI agent escapes the sandbox, the first question should be: who left the door open?

For firms in regulated industries, giving agents broad access to data and tools has meant weighing what they can do against what they might reach. SuperAnalyst runs every session in its own isolated MicroVM, with scoped credentials, a sealed network, and identity checks at each step, so one failed control never becomes a breach.

Lawrence Sica, VP of Product Security, and @chaschwartzz, Staff Software Engineer, walk through each layer of SuperAnalyst’s sandboxing architecture and how they work together to keep agent workloads contained. Read the full article: https://t.co/Ts4jliY5Y0

[Sarah F. Hoffman @sarahfhoffman]

Trust in enterprise-grade AI rose from 72% to 78% this year. Yet 62% of AI decision-makers say they’ve been burned by inaccurate AI output.

For insurance professionals, an inaccurate answer can carry significant consequences.

On Thursday, Nov 5, I’m joining Sina Falaki for a Society of Insurance Research webinar on moving from AI adoption to accountability, including a practical framework for building research workflows you can stand behind.

Register: https://t.co/dXWfLqd9FR

@AlphaSenseInc

[AlphaSense @AlphaSenseInc]

“My team actually came to me begging for AlphaSense.”

Hear Tricia Tolivar, CFO at CAVA, and Katie Fogertey, CMO at Gluck & Co. LLP, sharing how AlphaSense is not just for financial services: law firms and fast-casual restaurants alike are migrating to decision-grade AI. #AlphaSummit