Friends,

When you hear the word “pivot,” what comes to mind? Traditionally, that term is applied to small startups iterating and experimenting in a quest for product-market fit. Shopify, for example, began life as an online store for snowboarding gear. Or YouTube, which started as a dating service – users uploaded videos talking about their ideal partner in the hopes of meeting their match. The platform’s slogan was “Tune In, Hook Up.”

Though early-stage companies are no strangers to pivoting, the truth is that even the world’s largest businesses must undergo significant changes to remain on top. Not all bear the abrupt torque of a pivot, but they amount to the same thing: an enterprise embarks on a transformation and becomes a different business. Around 2012, Microsoft began its metamorphosis into a cloud behemoth – a radical corporate renewal. Six years later, fellow giant Apple started making a concerted push into services, diversifying revenue away from hardware. In the 2022 fiscal year, the App Store, Apple Music, Arcade, Fitness, and TV delivered $78 billion for Tim Cook’s firm. 

Often, these shifts are precipitated by technological change. That might be the emergence of cloud computing, as in Microsoft’s case, or the hazy promise of the metaverse, which Facebook has swerved to meet. The stakes are high in such instances; these are “bet the company” moments: catch the wave at the right time and reap the rewards; miss it and die. 

This year marks the definitive beginning of a new, innovation-driven shuffle step: the “AI pivot.” As large language models (LLMs) demonstrate their remarkable potential, large tech companies are racing to avoid obsolescence and capitalize on fresh momentum. Adobe, Canva, Discord, and Notion are among the established players using AI to add new functionality to their platforms. Meanwhile, Alphabet is in the midst of converting itself from a company that uses AI into an AI company. (And, of course, Microsoft is getting into the act once more, thanks to its links with OpenAI.)

We can understand the changes these businesses make in the abstract, but what does committing to a pivot like this really mean? How does a mature, highly-valued enterprise shift its center of gravity, wrap itself around a new, artificially intelligent engine? What changes in product strategy, cost structure, and competitive dynamics does such a transformation invite? 

To answer these questions, and many others, we sat down with Intercom co-founder and Chief Strategy Officer Des Traynor. While all the businesses mentioned thus far are interesting case studies, Intercom is a particularly apt focus for this investigation. 

Firstly, it is an undoubtedly established business. Since its founding in 2011, the customer support platform has attracted $241 million in capital from leading investors like Kleiner Perkins and reached annual revenue of $200 million by some estimates. Reconfiguring an entity of this magnitude is not simple. 

Secondly, and most importantly, Intercom is uniquely sensitive to the modern AI renaissance. As co-founder Traynor outlines in this interview, customer support is perhaps the most “target-rich” environment to apply generative models like GPT-4 – these technologies excel at talking, reasoning, and providing answers. Such conversational abilities have traditionally been the province of human agents. As their jobs change, the software they use must evolve, too.

Rather than deny disruption, Intercom seems hell-bent on meeting it. In March, Traynor’s firm debuted Fin, a customer service bot powered by GPT-4. Though in its early days, Fin is seeing “insane levels of demand,” according to the Intercom executive. It’s purportedly capable of resolving 50% of customer queries. Fin is just one part of Intercom’s “top to bottom” reinvention as an AI company, a shift that could power the firm to new heights but that introduces real risks and financial ramifications. Founders, operators, and investors interested in the AI revolution will find plenty of lessons in Traynor’s perspective.

Note: This conversation has been edited for readability and clarity. 

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PS – A small note before getting into today’s piece. The Generalist will be OOO for the next three weekends as Ali and I log off for our wedding ceremony and honeymoon! (Long-time readers will know we’re technically already hitched – but we haven’t done the whole shindig.) We’re super excited. Of course, I also can’t wait to come back refreshed for the second half of the year. There’s a lot more I’m looking forward to learning about together!

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Intercom’s AI Evolution

Actionable insights

If you only have a few minutes, here’s what investors, operators, and founders should know about Intercom’s AI evolution.

• Working with LLMs. The hard part of working with an AI model like GPT-4 isn’t telling it what to do; it’s telling it what not to do. Intercom co-founder and Chief Strategy Officer Des Traynor highlighted this as one of the key difficulties in shipping a new AI chatbot, Fin.  

• Rabid demand. According to Traynor, Fin has seen “insane demand” since its March debut. Companies are enticed by a chatbot capable of resolving 50% of queries, freeing up support teams to focus on thornier issues. 

• Changing margins. Relying on providers like OpenAI costs money. That means companies building on top of AI models must keep cost in mind, especially when operating at Intercom’s scale. Traynor’s firm has had to reassess its shipping strategy to accommodate this cost structure shift. 

• Upside-down. The customer support segment is being turned “upside down,” according to Traynor. The advent of reliable, broadly intelligent AI bots like Fin means that teams are increasingly shifting toward a “first and last” model – answering a question for the first time and last time. This is a small example of how the broader support industry will likely be disrupted.

• No bolt-ons. Companies being disrupted by AI can’t take half measures, per Traynor. Simply “bolting on” a few generative features – as many startups have chosen to do – isn’t enough to separate oneself from the chasing pack. Intercom is choosing to reinvent itself from top-to-bottom, becoming an AI-native business.

Given Intercom’s product, I imagine you’ve been interested in AI for some time. What’s the company’s journey with AI been like?

We’ve been building with AI since 2018. We started with our “protocol resolution bot,” which answers conversations like Fin does today. The biggest difference is that you have to train it: you tell it which screenshot to use, which videos to surface, what part of the product to deep-link to. It’s still a wildly popular product for some of our customers, who can get to 60-70% with it. You’d call it old school today, but it was pretty bleeding-edge until last November. 

ChatGPT has been a big shift. Unlike our product resolution bot, you don’t have to train it – it understands language from the very beginning. It’s also extremely good at the basics of a conversation. You can have a long, multi-threaded conversation with it. If you asked our old bot, “Do you have an Android app?” it could have answered that question. But when you think about going through question-and-answers and then asking a follow-up like “What about Android?” a different level of contextual understanding is needed. 

The newer GPT models have made it a lot easier to inject AI in many more places throughout our product. Fin, our new GPT-4-powered bot, is an example of leveraging these new conversational capabilities.

How did Intercom think about implementing generative AI?

What worked well for us was to start by focusing on a relatively safe area of the business to test this technology out. I think other companies should follow that framework – look for a part of the business where expectations aren’t super, such that even if it’s only marginally useful, it’s still useful.

For Intercom, that meant starting on the support agent side – the people answering queries – not the customer side. We used new AI models to summarize conversations that agents could expand or shrink as they liked. It was useful but low risk. If the AI mis-summarized something – it’s ok. These early experiments helped us understand what was possible and laid the groundwork for Fin.  

What was the vision for Fin?

We wanted to build a superior, more established version of our resolution bot that could hold all types of conversations but also had the capacity to get specific. We also needed to withhold hallucinations as much as possible – they’re quite dangerous for customer support. You can imagine a scenario where a customer asks an AI bot a question, the AI bot makes up an answer, and then the conversation disappears. In that case, the business may never know what happened, and the customer might go on to take all kinds of incorrect actions because of the bot’s bad advice. You really have to be careful. 

It’s funny, but a lot of the work we did on Fin was getting it to not do things. The capabilities of a lot of AI models are great, but they have an inflated sense of their knowledge. We needed to tame that tendency so that Fin understood its confidence level when answering. Sometimes it should say, “I’m not sure, but here are two articles that might help,” other times, it should say, “I know the answer – here you go.” Most crucial was getting Fin to learn when to say, “I have no clue, I’m going to pass you over to a human agent and they’ll take it from here.” Most of the research we did focused on helping it make that hand-off.

GPT-4 unlocked a lot. The newer model gave us far better performance when it came to Fin understanding when it doesn’t have a good answer to give.

How did you set those boundaries for Fin?

Our competitors would love to know a few of the steps we take. I respect The Generalist’s reach, so I can’t share too much.

At a high level, we do a lot of classic machine learning to understand the queries Fin receives so that it knows when it should and shouldn’t be looking for an answer. We’re essentially setting the operational framework the LLM is living in, priming it with some context on the customer query, and then providing the anatomy of a potential answer.

How do you partner with OpenAI?

We’ve been aware of each other for a while. I knew OpenAI’s CTO Greg Brockman from his Stripe days – there’s always been a kinship between Stripe and Intercom. I think the folks at OpenAI were also aware of what we were doing with our resolution bot – it’s a natural application for AI in a target-rich environment. 

The first feature we released based on OpenAI technology was in January, but we worked with predecessors of ChatGPT before that point. As OpenAI’s grown up over the last six months and started having commercial relationships, we’ve deepened our partnership. We spend a lot of money with them; we build a lot of proprietary technology that sits on top of their API. That’s why you’ll see us featured on their marketing website. I think we’re a pretty strong use case of what things you can build on top of OpenAI.

Keep reading

In case you missed it

Looking for a little extra analysis while we’re away? Here are a couple of deep dives we recently published. Dig into the stories of Vanta and Watershed, two extremely fast-growing, high-performing startups.

• How Vanta Grew Through the Downturn. The $1.6 billion trust management platform has used a venture winter to widen its lead. Led by Christina Cacioppo, Vanta has built a war chest, added senior talent, made ambitious product moves, and acquired a promising startup. This piece is a case study of using difficult circumstances to your advantage.

• Watershed: A Climate Pragmatist. The $1 billion carbon accounting platform has attracted investment from Sequoia and Kleiner Perkins. Its greatest strength is its practical, market-driven approach to the climate crisis. Jump in to learn more about the sustainability software space and the companies vying to be its leader.

Puzzler

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All hail Bruce G, the fastest to solve last week’s conundrum. He was joined by Adam Z, Gary J, Shashwat N, Joshua K, Anita R, Chris S, James D, and Radhika S in deciphering this puzzler:

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The answer? Teeth. Rather figurative.

Sending you all my very best, and see you in June!

Mario