AlphaSense CFO Samantha Greenberg on Wall Street, A.I. Pricing and a Possible IPO
Observer ·
AlphaSense CFO Samantha Greenberg says the company is absorbing significant A.I. costs as agents take over 99 percent of research on its platform.
A.I. agents now handle 99 percent of the research on AlphaSense’s platform, roughly 2 billion annualized actions against 20 million by people. Samantha Greenberg, who joined AlphaSense as CFO in April , tells Observer the company is “absorbing significant A.I.-related costs” so customers have time to see the value before they roll it out more widely.
Nearly six months after Greenberg arrived, AlphaSense announced on Oct. 6 that it had added more than 10,000 investment-banking users in the third quarter and that 18 of the world’s top 20 investment banks, and more than 100 banks in all, now use its platform. Its annual recurring revenue (ARR) from investment banks grew 75 percent year over year, per the same announcement. AlphaSense is also pushing its A.I. tools beyond research and into more of the work bankers do. The same day, it launched the next generation of SuperAnalyst , an A.I. agent that researches, runs calculations and writes its own code to produce financial models and board presentations.
Greenberg, who now oversees AlphaSense’s capital-markets strategy and investor relations, used its platform for more than seven years as an investor before she joined. “It’s a product I love,” she told Fortune on her first day as CFO. Before her CFO jobs at Mint House and ID.me , she invested in tech and consumer companies for nearly two decades at Goldman Sachs , Citadel and John Paulson’s hedge fund, Paulson & Co., where she made partner. In 2016, she launched her own fund, Margate Capital Management , with $130 million in seed money .
She answered Observer’s questions about pricing, new capital and a possible IPO.
This Q&A has been edited and condensed for clarity.
Observer: AlphaSense exceeded $600 million in ARR in the first quarter, and The Information reported in July that it had passed $700 million . What’s driving the growth right now? Is it mostly new customers, existing customers spending more or newer products such as SuperAnalyst?
Samantha Greenberg: Our growth is driven by both new customer acquisition and expansion within our existing customer base. We’ve grown to support more than 8,000 enterprise customers while maintaining net revenue retention above 125 percent, reflecting how existing customers are expanding their use of the platform.
With the launch of SuperAnalyst, our always-on A.I. agent, we’re enabling customers to delegate complex, end-to-end workflows to A.I. agents that can continuously monitor information and update their work as conditions change.
How are investment banks expanding their use of the platform? Are they rolling it out more broadly across firms, adding teams gradually or buying additional products?
We’re seeing investment banks expand their use of AlphaSense beyond traditional research and integrate A.I. more deeply into their everyday workflows. That includes deal sourcing, client meeting preparation, pitch deck generation, financial modeling and due diligence.
As agents do more of the work, does that change how you think about pricing the product?
Absolutely. As A.I. agents take on more complex workflows, traditional user-based pricing becomes less reflective of the value customers receive. We’ve introduced consumption-based elements into our pricing model, allowing customers to purchase credits tied to specific research workflows and outcomes.
The scale of this shift is significant. Platform-wide token consumption has also reached an annualized run rate of 54 trillion tokens, up five times compared to early 2026.
We’re also being intentional about monetization, absorbing significant A.I.-related costs to give customers time to experience the platform’s value before expanding adoption. Ultimately, we see an opportunity to align pricing more closely with tailored outcomes rather than simply access to software.
The company raised $350 million in June at a $7.5 billion valuation . Where is most of that money going now?
We’re focused on accelerating innovation across our A.I. platform and proprietary content while continuing to expand internationally. The $350 million funding gives us the flexibility to invest aggressively in these areas while maintaining financial discipline.
We’re investing across our full technology stack, from proprietary content and data to A.I. orchestration and workflow automation, to ensure customers can confidently delegate complex work to A.I.
Is an IPO something the company is actively preparing for? If not, what would need to change before that becomes a serious conversation?
I can’t comment on IPO timing. That’s ultimately a board decision based on market conditions.
We’re continuing to build the business with the financial discipline and operational rigor expected of a public company while investing in innovation and international expansion. We see the public markets as a natural potential next step in our long-term growth journey, but our priorities today are execution and creating lasting value.
As AlphaSense moves deeper into investment banking, what do you see as its strongest advantage over established financial-data platforms such as Bloomberg and newer general-purpose A.I. tools?
Our strongest advantage is that we understand the unique needs of financial institutions, where trust, accuracy and security are essential to making high-stakes decisions. That’s why we’ve built AlphaSense specifically for financial workflows, combining premium, vetted content, including broker research and expert interviews, with A.I. capabilities designed around the use cases bankers rely on every day.
Unlike general-purpose A.I. tools, our platform grounds its outputs in trusted financial and business intelligence, with citations that allow users to verify the underlying sources.
AlphaSense says work that once took days can now be completed in under an hour, including pitch decks and financial models. What are banks actually doing with that saved time? Are they handling more deals, changing the work analysts do or seeing the benefit somewhere else?
The biggest benefit is giving bankers more time for work that requires human judgment and client relationships. By automating research, financial modeling and presentation preparation, AlphaSense allows analysts to focus on evaluating opportunities, pressure-testing assumptions and developing recommendations. It also gives teams the capacity to pursue more opportunities and conduct deeper analysis without proportionately increasing manual work.
Is there anonymized usage data you can share that shows what adoption looks like inside a bank—time saved, frequency of use, workflows completed or expansion after an initial rollout?
In a recent SuperAnalyst beta deployment, a user at one of the world’s top two investment banks used the platform to analyze Anthropic ’s gross-margin profile, producing a detailed Excel workbook in minutes rather than the days of manual research and modeling that work would traditionally require.
A Fortune 100 professional services organization reduced client meeting preparation time by 83 percent in its first month using AlphaSense, generating millions of dollars in productivity savings.
More broadly, AlphaSense users have generated more than 1 million slides across more than 100,000 decks.