A series of interviews on

the mechanics of business and real-world applications of machine intelligence

CTL is a series of interviews with executives at the largest global businesses. We go behind the scenes, understand how these businesses operate, and explore the most interesting applications of AI.

We have a strict no-buzzwords, no hand-waving policy. It's about near-term tactical ROI, and how we get there.

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The law firm that's built a large and successful software business
With David Wakeling, Partner & Head of Markets Innovation Group, Allen & Overy
David Wakeling incubated a software product to help banks restructure contracts after the financial crisis, realizing the scale of work made it infeasible to do manually with lawyers. 10 years later, Allen & Overy has dozens of different software products managed by an interdisciplinary team of lawyers, software engineers, and data scientists. They're also one of the most advanced adopters of LLMs, where they're finding significant business value.
Scaling decisions & improving the quality of decisions -- a deep dive on data science & automation in insurance
With Luca Baldassarre, PhD & Lead Data Scientist at Swiss Re
Historically, much of the automation/augmentation in insurance has occured in consistent, structured types of risk (e.g. auto). At a reinsurer like Swiss Re, risk is complex, inconsistent, and global in scale; LLMs are a major unlock. And this is just a small, small part of the conversation -- the opportunities in insurance are immense.
The legal justice argument for GenAI to exist in law, and a tour of the existential disruption it's introduced
With Maura Grossman
What happens to evidence in the world of deepfakes? Does the NY Times have a reasonable complaint about OpenAI training on their content? How do you prevent someone maliciously generating cases at scale? Maura practiced law for 17 years, was a pioneer in eDiscovery, and is now a professor of computer science, and is incredible well-read and thoughtful on all these big questions.
The governing trends in cybersecurity
With Amol Kulkarni, Chief Product Officer at CrowdStrike
Amol takes us through CrowdStrike's journey from $6 million in ARR in 2014 to $2.5 billion today. Over that time, we talk through how threats have changed, how buyers mindsets' have changed, and where the opportunities are for new startups today in the face of consolidation. And as it relates to AI, we speak about CISOs concerns related to ChatGPT, the AI driven attacks we're already seeing, and where LLMs are most likely to play a role in security products.
Reducing defense procurement workflows from 3 months to 30 minutes
With Bonnie Evangelista, US Department of Defense
A significant portion of inefficiency in government contracting can be attributed to the writing & processing of long-form documents. Internal buyers from the four branches must write these documents to describe their needs and vendors must write them to respond. One forward-thinking group has already found promise in resolving this inefficiency with LLMs.
Reinventing Zapier for the LLM era
With Mike Knoop, Co-Founder & Head of AI at Zapier
LLMs are already transforming Zapier's business. They're seeing 100x productivity improvements on internal processes that drive user acquisition, deflecting a large volume of customer support requests by improving the handling of user-facing error messages. But most interesting is that Zapier views LLMs as the "escape hatch" UX paradigm that will enable it to grow from 10M users today, to 100M users over the next decade.
Building the "holy grail" investment research platform
With Michelangelo D'Agostino, VP of Machine Learning at Tegus
Few companies are better positioned to benefit from LLMs than Tegus; the company has 60k transcripts of calls between investors and experts, each many pages long and rich with interesting information. Michelangelo outlines several interesting product ideas, many of which are replicable in other scenarios.
Data-driven experimentation to achieve the upper limit of sales team performance
With Ben Rubenstein, Co-Founder of SetPoint, Opcity, and Yodle
Ben had two successful exits, both enabled by taking data-driven sales team & process optimization to the extreme. New technologies in the world of AI have moved the max optimization limit much further forward, and it's now possible to take Ben's ideas to their full potential.
LLMs in cybersecurity... old problems solved, new problems created
With Dave Palmer, GP at 1011 Ventures & Co-Founder at DarkTrace
There are two major themes governing the cybersecurity market: a labor shortage in security talent, and phishing being the single largest threat vector. In a few different form factors, LLMs can start to fully close automation loops and solve this labor problem. Meanwhile, LLMs will amplify the scale and strength of phishing attacks, introducing a whole new set of problems. The opportunity set in both categories is immense.
Inspecting mega-scale infrastructure
With Sam Tukra, Senior ML Researcher at Shell
A pipeline going down even 1% of the time means tens of millions of dollars in losses and serious health and safety concerns. And just one refinery can be 1000s of acres in size, so the scale over which inspection must happen is enormous. The availability of data to build models to run these inspections is a key bottleneck. New approaches in the areas of self-supervised learning show promise in solving this problem.
Connecting OEMs with their customers
With Jon Cooper, Operating Partner at Atlas Innovate
Manufacturers of industrial equipment want a direct relationship with their customers to improve post-purchase customer experience and capture services revenue. Offloading monitoring data, using anomaly detection, and providing in-context maintenance information through LLM-driven agents is now possible. OEMs can leverage this new technology the build the relationships they want.
Democratizing complex software
With Karl Mosgofian, CIO of Gainsight
Customers churn from software products due to lack of value. Even if the product is valuable, it doesn't matter if users don't know how to use it. Software vendors train 30 users at start, only 2 know how to use it after a year. Large language models bring value to the other 28.