In our latest episode, we talk with Anshul Gupta, who recently raised $22.5M from Bain Capital and First Round Capital for Actively.

Our favorite takeaways:

* Personalization at scale is actually "laziness at scale": "The amount of emails I got saying 'Anshul, congrats on your recent funding round' followed by a totally unrelated value proposition... if you're looking at thin logic like 'someone raised funding, throw them in a sequence,' that's not working anymore."

* Top performers do less, not more: "When you sit down with the best reps, they're not the ones doing the most volume. If you look at the leaderboard, they may even be in the bottom half from an activity perspective. What we see is they're incredibly targeted and laser-focused."

* Relevance beats volume: The alternative to personalization is "relevance at scale" – going incredibly deep with research, building proper hypotheses based on customer needs, and focusing on quality over quantity.

* AI flips the paradigm: "The revenue orgs in two years will be flipped on their head. Systems of intelligence will, instead of humans asking them what to do, be processing all information and guiding humans where to spend their time."

* AISDRs have limited applications: "If you're a company just starting up with no sales reps and want to get meetings by hook or by crook, AISDRs could work. But fundamentally upmarket, in complex ecosystems with multi-product motions, that model doesn't work."

* The revenue frontier concept: "Rather than constraining headcount and increasing productivity, increase your ambition. Companies are nowhere near their revenue frontier, and imperfect go-to-market execution is a huge contributor to that gap."

* Custom reasoning models: "Ramp has their own Actively reasoning model, Ironclad has their own – trained on the hyper-specific nuances of their products, go-to-market motions, and as those continue to evolve."

What makes Actively different:

* Their focus is increasing rep productivity and the quality of pipeline each human can generate

* They train custom reasoning models specific to each client's business

* They deploy a "forward deployed engineering model" similar to Palantir to customize for enterprise needs

* The system continues to improve through active learning (hence the name!)

What's not working in the market:

"All these volume-based solutions purport to help you send more emails or do more calls. It's all about chasing volume when the top performers are actually doing less but with more focus. That thin logic of 'if someone raises funding, throw them into a sequence' is what's corrupted the concept of personalization at scale."

What do you think about Anshul's take on personalization vs. relevance? Are you seeing "laziness at scale" in your inbox? Have you identified the revenue frontier for your business? 👇

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S1E10: Agent Talk #10 - Anshul Gupta (Actively) - Real Relevance, Not Lazy AI Personalization

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S1E10: Agent Talk #10 - Anshul Gupta (Actively) - Real Relevance, Not Lazy AI Personalization