Chris Pedregal + Sam Stephenson: Making Meetings More Effective with Granola

Michael Mignano welcomes viewers to Generative Now, a show where he speaks with founders building AI-powered tools. In this episode, he interviews Chris Pedregal and Sam Stephenson, co-founders of Granola, a note-taking app that uses AI to compile and summarize meeting notes.

"Welcome to Generative Now. I am Michael Mignano, I'm a partner at Lightseed."

The conversation takes place at Granola's headquarters in London, where the founders have recently set up their new office space. There's some lighthearted discussion about Sam personally carrying plants into the office after a 4 a.m. visit to a plant market.

"Sam just carried all the plants in himself."

Timestamp: [00:02-01:15]

Chris shares how he began his entrepreneurial journey three years ago after leaving Google. Within a week of quitting, he started experimenting with GPT-3's instruct version and was immediately impressed by its capabilities.

"This was three years ago. I quit Google, I knew I wanted to do a startup in London, I didn't know what I was going to do. Within a week of quitting Google I started playing with GPT3 the instruct version of it that had just come out and was blown away."

Chris realized he needed a co-founder with specific skills - initially thinking he needed someone who could train models (a view he later changed) and someone who could design AI-native interfaces. This search led him to explore "tools for thought" forums online.

"Oh man there's a bunch of new UI that's going to have to be designed like that's AI native. So I need someone who's really thoughtful at that."

Timestamp: [01:15-01:52]

When asked to explain what "tools for thought" means, Chris elaborates on how humans are fundamentally toolmakers and how our capabilities are limited by the tools available to us.

"Humans are toolmakers, right? One of the things that sets us apart from animals... what we are able to do is really limited by the tools we have available to us."

He traces the evolution of these cognitive tools from written language to mathematical notation, explaining how each advancement has expanded human cognitive capabilities. For instance, modern numerical systems allow for more complex calculations than Roman numerals did.

"If you're using Roman numerals you can only do so much math in your head, whereas if you use what are Arabic numbers... you can do much more complicated stuff with paper and pencil."

Chris sees AI as "the ultimate turbocharger of tools for thinking," which led him to connect with Sam through an online tools for thinking meetup group - without even meeting in person first.

"I didn't even meet him, I just saw his profile and I sent him an email being like 'Hey do you want to grab a beer sometime?' And somehow he said yes."

Timestamp: [01:52-03:24]

Sam explains that from the beginning, both founders shared a strong belief that AI would transform the landscape of productivity tools. They recognized that either existing tools would need to completely reinvent themselves or new players would disrupt the market.

"We were both very aligned from the beginning on like AI is going to change the landscape of the tools we use and either the tools that exist right now are going to have to change everything about what they do or new people are going to come in and take over."

The founders were determined to focus on a specific, painful user problem rather than building technology for its own sake. They spent time "wandering around" and talking to people about their work challenges to find a genuine pain point.

"You want to be able to picture a person in your head and picture them struggling with a thing so that you can kind of make the tool that solves the struggle."

Through these conversations, they identified a universal frustration that emerged repeatedly: the administrative burden that follows meetings.

Timestamp: [03:24-04:38]

Sam describes how they discovered that people whose jobs revolve around meetings consistently struggle with the "pile of follow-up work" generated by each conversation.

"People whose job revolves around meetings and talking to people - every time you have a meeting with somebody that meeting tends to create a kind of pile of follow-up work."

This work ranges from simple tasks like writing up notes and sending follow-up emails to more complex actions such as updating multiple fields in a CRM or triggering workflows. The founders recognized these administrative tasks as universally disliked busy work.

"It's all kind of menial work that isn't what you get energy from in your job."

Sam explains that this pain point seemed perfectly suited for AI intervention, even if the technology wasn't fully ready when they started. This insight led them to begin developing a tool that could be present during meetings and eventually handle much of this administrative burden.

"We started pushing on how can we make a tool that is in your meeting that eventually will be able to help you do a lot of this kind of menial work that happens around meetings."

Timestamp: [04:38-05:07]

Michael notes that while building great product experiences around AI seems obvious now, this wasn't always the conventional wisdom. He recalls that just 12-18 months ago, there was skepticism about companies building at the "app layer" of AI, with critics dismissing them as mere "wrappers" on large language models like GPT-4 or Claude.

"For a lot of people in the AI community in tech in VC in startups that was not obvious to many people a year ago... there was all this talk about 'oh if you're building at the app layer of AI you're just a wrapper on GPT4.'"

Michael observes that sentiment has completely reversed, with substantial excitement now surrounding the app layer. He asks the founders why they think this shift has occurred.

Chris identifies three key factors that changed industry perspectives:

1. The rapid improvement of large language models made it more practical to use existing frontier models rather than train custom ones.

"These models just kept getting better and better faster and faster and it became very clear that it just made way more sense to just use the best frontier model out there than try to train your own thing."

2. The prohibitive cost and difficulty of training custom models meant only a few large companies could realistically do so.

"Super hard and expensive to train your own model so it's going to be a couple big shops that are going to do that."

3. The recognition of where specialized applications provide value versus general-purpose AI.

"Low frequency use cases that are maybe non-critical are going to be eaten up by the general agents... If you are doing something that really matters like professional tooling where your performance really matters and you want to optimize for that use case, then bespoke tools that are optimized for that are going to be way better."

Chris points to companies like Cursor (with its recent high valuation) and WindSurf (recently acquired) as examples of successful application-layer companies that are "just wrappers" on frontier models but deliver tremendous value through their specialized implementations.

Timestamp: [05:07-07:44]

• Early adoption of frontier AI models like GPT-3 can provide a founding advantage in building new tools
• The "tools for thought" concept frames AI as the next major evolution in human cognitive enhancement
• Successful AI products focus on specific, painful user problems rather than technology for technology's sake
• Meeting follow-up work is universally disliked yet necessary, making it an ideal target for AI assistance
• The industry has shifted from skepticism about AI application layers to recognizing their value
• Specialized AI tools provide more value than general-purpose AI for professional, high-frequency use cases
• Building great software experiences around AI models is challenging and valuable, contrary to early dismissals of "wrapper" apps

Timestamp: [00:02-07:44]

Companies:

• Google - Chris's previous employer before founding Granola
• Granola - The AI-powered meeting notes app founded by Chris Pedregal and Sam Stephenson
• Lightspeed - Venture capital firm where host Michael Mignano is a partner
• Cursor - AI-powered development tool mentioned as having received high valuation
• WindSurf - AI application recently acquired, mentioned as an example of successful "wrapper" app

People:

• Michael Mignano - Host of Generative Now and partner at Lightspeed
• Chris Pedregal - Co-founder of Granola, former Google employee
• Sam Stephenson - Co-founder of Granola, met Chris through tools for thought forum
• Paul - Friend of Chris mentioned as teaching him about humans as toolmakers

AI Concepts:

• GPT-3 - Early large language model that Chris began experimenting with after leaving Google
• GPT-4 - More advanced language model mentioned in context of the "wrapper" debate
• Claude - Anthropic's large language model mentioned alongside GPT-4
• App Layer - Level of AI implementation that builds applications on top of foundation models
• Tools for Thought - Concept describing technologies that enhance human cognitive capabilities

Timestamp: [00:02-07:44]

Chris discusses how the AI tooling market operates like a pendulum, swinging between different trends, but believes that professional tools that significantly improve productivity will always have value.

"Professional tooling has always been a thing right, and if it makes you 10, 20, 30, 50% better at your job, like that's going to always have a lot of value and economic value."

Sam explains that when they started Granola during the early GPT-3 era, real-time transcription had just become available via APIs but wasn't great quality. This created a strategic challenge for the team.

"We had tons and tons of conversations about what should we be investing our time in because some stuff is just going to keep getting better without us doing anything."

The founders developed a framework for deciding what to work on: distinguishing between areas that would improve naturally with advancing AI capabilities versus problems that required their specific innovation. This approach helped them focus their limited resources on areas where they could create unique value.

"A lot of the game for us has been picking our battles and knowing what to innovate on and what to just wait for it to get better."

Timestamp: [07:49-09:18]

When asked about specific features they intentionally delayed, Chris shares several examples of their strategic prioritization.

"The obvious one to me was language support. When we launched, it immediately was like the most requested thing for Granola was to support multiple languages."

After spending a week investigating language support, they realized it would require a month-long project to create a good interface for selecting languages. Instead of making this substantial investment, they decided to wait for the underlying transcription technology to improve naturally, as many companies were already incentivized to solve this problem.

Similarly, they initially faced context window limitations that restricted Granola to 30-minute meetings. Rather than building complex chunking solutions, they waited for language models to improve their context windows naturally.

Chris also explains their approach to retrieval augmented generation (RAG), noting that as context windows expand, they can sometimes get better results by simply including more information rather than building sophisticated retrieval systems.

"If you put your engineering hat on, you're like 'that's wrong, we should engineer this,' but AI breaks intuitions. Sometimes you're like, it's very imprecise, we're putting all this stuff in there, but these models are smarter and more intuitive than we expect."

Timestamp: [09:18-11:25]

Michael asks about business models for AI app companies, noting many simply charge for "credits" that primarily cover API costs to language models. Sam explains their approach is more traditional than it might appear.

"I think a lot of the way we think about it is probably not too different like pre or post AI. I think ultimately we're trying to make a tool that's valuable enough that a company will give us money for it."

Rather than just monetizing AI access, Sam emphasizes creating value through network effects - making Granola more valuable as more people in a team use it, creating a repository of organizational knowledge that becomes increasingly valuable over time.

"Granola gets better the more people in your team are using it, and it becomes this valuable repository in and of itself. I think that's a thing that we have a lot of signal that companies will pay good money for."

Chris acknowledges they're in an interesting moment in history - a "land grab" where new AI-enabled products are emerging while the costs of running these products are expected to decrease dramatically in the coming years.

"It's going to be cheap to run Granola two years from now, but it's quite expensive to run now, but there's a lot of user demand. So what do you do in that kind of situation?"

The founders believe they must build for the future rather than optimizing for today's constraints, which makes their approach capital intensive in the short term. Their financial forecasts assume AI costs will decrease over time, with some capabilities (like transcription) eventually becoming commoditized while others (like powerful document creation and chat) might require staying on the frontier of AI capabilities.

Timestamp: [11:25-15:01]

Michael notes that Lightspeed was an early investor in Granola and reflects on watching the company grow from zero lines of code to their current product. He mentions their launch on May 22nd (nearly a year ago) and how it seemed to achieve "instant product market fit," which he describes as extremely rare.

"I remember the launch moment, 22nd May, we're coming up on a year... that launch moment was amazing. It felt like almost instant product market fit, which is so rare, never happens."

Sam explains their deliberate approach to product development in the months leading up to launch. The team recognized early that Granola's success would depend on creating natural, effortless user interactions that help people extract what they care about from meetings.

"The first six, nine months of Granola were just experiment after experiment after experiment, trying things to figure out what that might be. We'd build a thing, put it out in the world, be constantly talking to new users and watching how they react to it."

This period involved gradually adding complexity as they tested ideas, then cutting back and streamlining once they found a core interaction that worked - the approach where users type notes at the end of a meeting and Granola expands upon them.

The team maintained a strong focus on building a daily habit for users. They created a visualization called the "dot plot" that showed individual user engagement day by day, helping them honestly assess whether people were consistently using the product or just occasionally trying it.

"We were in closed beta for a year, and we had about 150 people that we had onboarded by the time we decided to launch, and we had manually onboarded all of them at that point."

Sam reveals that despite the successful launch, he personally didn't feel the product was ready, and credits Michael with pushing them to launch.

"I didn't really think it was ready when we launched it. Like, Mike pushed us to launch."

Timestamp: [15:01-17:48]

• Professional tools that significantly improve productivity (10-50%) will maintain economic value regardless of AI hype cycles
• Strategic product development requires distinguishing between what will improve naturally with advancing AI and what requires dedicated innovation
• Sometimes it's better to wait for underlying AI capabilities to improve rather than building complex workarounds
• Language models can produce surprisingly intuitive results when given unstructured information, challenging traditional software engineering approaches
• Business models for AI apps should focus on creating unique value beyond just reselling API access to foundation models
• Network effects and becoming a valuable knowledge repository represent sustainable competitive advantages beyond AI features
• AI startup economics involve a "land grab" where companies must build for future cost structures rather than optimizing for today's constraints
• Successful product development requires continuous experimentation followed by ruthless simplification around the core value proposition
• Building products that become daily habits is crucial for sustainable growth
• Launching earlier than feels comfortable can accelerate learning and growth

Timestamp: [07:49-17:48]

Technical Concepts:

• GPT-3 - Early large language model mentioned in context of when Granola started development
• Real-time transcription - Core technology that had just become available via API when they started Granola
• Context window - AI model limitation that initially restricted Granola to 30-minute meetings
• Retrieval Augmented Generation (RAG) - Technique for selectively feeding relevant information to AI models
• Dot plot - Internal visualization tool used to track daily user engagement with Granola

Business Concepts:

• Network effects - Strategic advantage where Granola becomes more valuable as more people in a team use it
• Product market fit - Business milestone that Granola seemingly achieved quickly after launch
• Beta testing - Granola spent a year in closed beta with 150 manually onboarded users

Companies/Products:

• OpenAI - Mentioned in context of AI app companies essentially reselling their API access
• Lightspeed - Venture capital firm that was an early investor in Granola

Events:

• Granola Launch - Occurred on May 22nd (approaching one-year anniversary at time of recording)

Timestamp: [07:49-17:48]

Sam continues discussing their product launch, revealing that their venture capital investors had been pushing them to launch for nine months before they finally did.

"VC actually been pushing us to launch for about nine months before that. We held them off for nine months."

The founders struggled to see past the product's flaws, focusing on what was wrong rather than what was working. This created an interesting tension between perfection and shipping.

"At that point all we could see were the things that were wrong with it, which is like an interesting lesson right? Because once we put it out in the world, it actually hit a bunch of chords, but we didn't necessarily appreciate the depth of that until we put it out there."

This highlights the classic entrepreneur's dilemma - recognizing when a product is "good enough" to launch versus waiting for perfection, and how actual market reception can differ dramatically from founder expectations.

Timestamp: [17:54-18:17]

Michael asks Sam about the team's design philosophy, particularly how they design for what people actually need versus what they think they need. Sam explains their approach to building software that works in high-stress situations.

"It's really easy to get theoretical about what a user might want... and when you interview users, they can tell you all of their great ideas for the product, and it's really easy to just build what they want because they're asking for it."

Sam explains that they were "paranoid" about designing for the actual context in which Granola would be used - between back-to-back meetings when users are stressed, rushed, and have minimal cognitive bandwidth for learning new software.

"Meetings are a super high stress situation... when you're in a back-to-back meeting where you're maybe 2 minutes past the hour, you're already late for your next meeting, you're trying to make excuses to get off the call, and then you get off the call and you got to rapidly get into the next one as quick as you can."

In these stressful transitions between meetings, users have extremely limited mental capacity to engage with software.

"You have so little brain space for a piece of software at that moment... we just have like this 1% of your brain to play with as people designing a product."

This constraint became a powerful design principle that kept the team focused on simplicity and minimal friction.

"People often talk about how simple Granola is and how it feels nice because of that. I think that's just a function of we really can't put many buttons in front of you when you're in that situation. You don't have the head space for it."

Timestamp: [18:17-20:05]

Michael asks Chris to compare his experience building Granola, an AI-powered company, with his previous experience building and selling Socratic, a company without AI. Chris notes that it's too early to give a definitive answer but shares several observations.

"Ask me that in two or three years, I think I'll have a much better answer."

First, Chris highlights their CTO's emphasis on using AI throughout their engineering process to reduce the amount of code their team writes.

"Boss, our CTO, who's not here right now - he really pushes us internally to use AI as much as possible. So it's like an active goal to reduce the number of lines our engineers write every day."

This requires intentional effort because established habits can be hard to break, even as the world changes rapidly.

"You actually do need to push people for that because we all have habits... and the world's changing very quickly, so if the org isn't doing that, then you're missing out."

Chris also speculates on how AI might change company structure and team composition. While product development will still require "best-in-class people," other functions might look different.

"In the past we might have built a really big customer success function, where I don't expect us to do that. I expect us to use whatever the best and greatest AI tooling is and we'll still have a great team there, it's just how that team spends their time."

He suggests these teams might function more like engineers, "building systems even though they might not be writing code."

Finally, Chris notes how the level of interest in AI has dramatically changed the startup experience.

"I'm used to startups being like a slog of you fighting so hard to get people to care about what you're doing, and I kind of feel like the rug got pulled out from under me with Granola because we put it out there... and all of a sudden it just started growing and then stuff just started breaking internally because we weren't mentally prepared for that."

Timestamp: [20:05-22:33]

Michael observes that AI companies are forced to move extremely fast, which creates challenges for maintaining quality and "taste" - a term he notes has become somewhat overused in tech. He asks how Granola maintains its reputation for beautiful design and taste while moving at such a rapid pace.

Chris admits there's room for improvement but shares several strategies they use:

"We screen engineers as part of the interview process for product thinking. Can you think from the point of view of a user? When there's a technical problem put in front of you, get to the why of why this is a problem for the user."

This focus on user-centric thinking helps the team make appropriate trade-offs:

"That helps you make the right trade-offs in cutting the scope and really just building the thing that's going to solve the person's problem, not like this beautiful technical masterpiece of an execution."

The team distinguishes between different types of features based on their importance, allowing for different levels of scrutiny:

"There are types of features where once we have good systems set up, the UI of Granola is kind of figured out, you can just ship and iterate and push stuff out very quickly there... and then that way we can reserve our judgment and taking the time to pour over the details on the things that really matter."

Chris explains they're trying to get better at distinguishing between "one-way door" decisions (difficult to reverse) and "two-way door" decisions (easily reversible):

"We're trying to get better at the one-way door versus two-way door. If it's a two-way door, can we just ship changes quickly, see what people think and go from there."

Despite the pressure to move quickly and add features, Chris acknowledges the need to preserve what makes Granola special:

"What people love about Granola is that it's simple, minimal, and gets out of your way. If you add 50 buttons in there with new features, you kind of kill the golden goose. And I think we're figuring out how to find that balance because we do have to move quickly, but we also need to keep the soul of the product intact."

Timestamp: [22:33-24:51]

Michael observes that while Granola is based in London, the product has achieved significant popularity in Silicon Valley and the U.S. tech scene. He asks if this positioning is intentional and what it's like to build a team in London.

Chris confirms this was intentional and explains their hybrid approach:

"Everyone on the team kind of wants to have that like really ambitious classic startup journey, and we just happen to be in London. And that's a pretty beautiful twist on it because you get to be in London but you also kind of get to live the Silicon Valley dream, and that's pretty rare."

He acknowledges the importance of Silicon Valley's startup culture and methodology:

"The reality is most successful tech companies come out of Silicon Valley, and there's a culture and learnings and best practices about how to build a hyperscale tech startup that were kind of invented over there."

While they draw inspiration from Silicon Valley, they leverage London's unique advantages:

"There's amazing talent in London, and it's an incredible, pretty fantastic group of people and perspectives that are here. So I think there's a real big opportunity for us to build a Silicon Valley-style startup but in London with the talent that's here."

Sam adds that being one of the few prominent AI application companies in London provides a strategic advantage:

"At the app layer, there aren't that many kind of buzzy AI app companies in London. There are some pretty impressive ones at the foundation layer, like if the 11 labs all the way back to like DeepMind."

This positioning helps them attract talent that might be spread across many companies in Silicon Valley:

"We're kind of a bigger fish in a smaller pond compared to if we were in Silicon Valley where there's just so much stuff going on there. So we're kind of a magnet for that type of person, so it's probably a bit of a strategic advantage when it comes to hiring."

Both founders acknowledge there are trade-offs with this approach. While they get access to incredible talent in London with less competition, they must work harder to stay current with developments in Silicon Valley.

"It's important for us to stay current, understand what's going on there. It can also be a full-time job to keep up with what's going on in AI, so you want to strike the right balance of keeping your finger on the pulse but don't get distracted because there's so much noise."

When asked about other London-based companies they admire, they mention AI-focused firms like ElevenLabs and Plaine, as well as successful fintech companies like Monzo and Wise. Chris emphasizes that despite being in London, they maintain a global outlook:

"It's too easy if you're in London to think about the UK and to think about Europe, and my general view is that in this AI space that's so competitive, you need to be competitive in the US because otherwise someone will win the US and then you're [cut off]"

Timestamp: [24:51-28:01]

• Founders often struggle to launch products they perceive as imperfect, even when investors see market readiness
• Designing for high-stress contexts (like transitions between meetings) requires extreme simplicity and minimal cognitive load
• AI startups should actively encourage using AI within their own engineering processes to stay at the forefront
• Company structures in the AI era may shift, with traditionally large departments becoming more systems-focused
• AI startups can experience unexpectedly rapid growth compared to traditional startups, creating operational challenges
• Maintaining product quality while moving quickly requires clear prioritization between core and peripheral features
• Distinguishing between reversible decisions (two-way doors) and irreversible ones (one-way doors) helps teams move faster safely
• Building in London while targeting Silicon Valley-style ambition creates a unique advantage for talent acquisition
• Being a prominent AI app company outside of Silicon Valley can make you a "bigger fish in a smaller pond" for recruitment
• Global competitiveness requires focusing beyond local markets, especially winning in the U.S.

Timestamp: [17:54-28:01]

Companies:

• Granola - AI-powered meeting notes app built by Chris and Sam
• Socratic - Chris's previous company that he built and sold before Granola
• ElevenLabs - London-based AI voice technology company mentioned as impressive
• DeepMind - London-based AI research lab mentioned as part of London's foundation model expertise
• Plaine - London company noted for building great user experiences
• Monzo - UK fintech company mentioned as a London success story
• Wise - UK fintech company (formerly TransferWise) mentioned as a London success story

People:

• Boss - Granola's CTO mentioned for pushing the team to use AI extensively in their engineering process

Business Concepts:

• One-way vs. Two-way doors - Decision-making framework for determining when to move quickly (reversible decisions) vs. carefully (irreversible decisions)
• Product thinking - Skills that Granola screens for in engineering interviews, focusing on user perspective
• Lizard brain - Term mentioned by Michael referring to instinctive user needs vs. stated preferences
• Golden goose - Metaphor used to describe Granola's core value of simplicity that could be killed by feature bloat

Locations:

• London - Where Granola is headquartered
• Silicon Valley - Region whose startup methodology and culture influences Granola despite being based in London
• New York - Mentioned as where Chris previously built Socratic

Timestamp: [17:54-28:01]

Michael asks the founders about their ultimate ambition for Granola, beyond being just a note-taking app for people in back-to-back meetings.

Sam explains that other professional categories already have powerful tools that amplify productivity, but people who primarily work through conversations have been left behind:

"Other professional categories have already figured out their power tools that people spend their day in and kind of helps them get their best work done. Designers have Figma or Photoshop, engineers have IDEs like Cursor or VS Code."

He points out that professionals whose work revolves around "people stuff" – like sales, customer-facing roles, management, or investing – haven't had similar tools because the fundamental unit of their work is natural language and conversation.

"Up until now, folks who work in doing people stuff... you've not really been able to have one of these workspaces because the fundamental unit of your work is natural language and conversation, and that's just too squishy for traditional software to deal with."

Sam sees a historic opportunity to create powerful workspaces for these professionals now that AI can understand natural language:

"We are at this exciting point where computers can finally make sense of natural language and organize it, and so I think we have a shot at creating that kind of workspace that people who do people stuff kind of live in, and it amplifies them, makes them work better, work faster."

Chris expands on this vision, placing it in a broader historical context:

"We're so lucky to be alive at a moment in history where the tools that humans use to think and to do work are being reinvented. I really do think AI is if computers were a bicycle for the mind, AI has a potential to be a jetpack for the mind."

He references early computing pioneers and their vision for how technology could enhance human capabilities:

"It kind of hearkens back to Douglas Engelbart... all these ideas at the birth of computing of what impact it's going to have on society and our ability to do great things that we could never do before. I think computers did do that, and I think now it's the second chapter of that – what are the new heights that we can reach?"

Timestamp: [28:06-31:01]

An audience member named Emily asks about the founders' approach to feedback loops in the early stages of building Granola, particularly how they determined when they had enough data to move forward with decisions.

Chris offers a philosophical perspective that emphasizes intuition over quantitative data in the earliest stages:

"In the early days, it's actually not even that it's like qualitative – it's like you need to go off of your intuitions. I believe that deeply. If you don't fundamentally feel like the product or the need deep down inside, then that's a real problem."

He clarifies that this doesn't mean working in isolation. Instead, he advocates for constant user interaction to develop better intuition:

"I'm not saying go off in a closet and just work in isolation for six months. I think talking to users and people is paramount. You should do it every day basically, but it's not the 'ask people if they want to build faster horses' thing."

Chris explains that by observing users struggle with tasks, founders train their intuition to make better product decisions:

"By spending time with users and watching them try to do stuff and fail, you are giving your mental context – your brain – all this really relevant context so that your intuitions are better honed."

He concludes that looking for quantitative signals is "almost impossible in the early days," suggesting that qualitative insights and refined intuition are more valuable for early-stage products.

Timestamp: [31:01-32:28]

Another audience member asks about Granola's design philosophy for creating the "jetpack for the mind" while avoiding becoming a complex CRM system.

Sam emphasizes their user-centric approach:

"The thing that has served us really well so far is putting the individual user and the particular moment that they're in when they're using Granola above everything else and designing a great experience around that."

He explains that while companies pay for the product, the individual user experience drives product decisions:

"Companies pay for us, but it's not the thing that's driving every product and feature decision. It's make Granola great for the user."

Sam describes two directions they're exploring. First, creating shared context for teams:

"When teams use Granola together, there is a lot of value in having the shared context in one place where you can look at not just the one meeting you had then, but every meeting that your team has had around a specific subject."

This creates new possibilities like analyzing patterns across all sales calls to identify what's working and what isn't.

Chris builds on this, explaining how meeting data provides extraordinary context for AI:

"AI is as good as the context it has, and the UI that lets you do useful things on top of that context. Meetings are freaking incredible because the amount of data in transcripts is nuts."

He sees meetings as just the beginning, with plans to incorporate emails, Slack, and other data sources to enable more powerful use cases:

"I want every VC in the world writing the first draft of their investment memo in Granola because we should be the best tool for that, full stop. Every follow-up email, every strategy document... if you're going to reorg your company, you should do that in Granola because we know the most about what's going on in your company."

Chris shares an example of the potential: a team member built a demo of a "self-writing wiki" for Granola that automatically creates and updates documentation based on meeting content:

"Jim built this demo the other day and it blew my mind... there's so much data in these meetings where he built like a self-writing wiki for Granola. It writes itself and it's always up to date, which is nuts."

Timestamp: [32:28-35:45]

An audience member named Sundeep asks about user preferences regarding having meeting information stored and transcribed, particularly in sensitive contexts like financial services.

Chris responds by framing AI meeting tools as becoming essential in professional settings:

"Tools like Granola are already useful and will be so useful in the future that they will be expected in work situations. I think the private social sphere is a different question, that one's a big question mark to me, but in the work sphere I think it's going to be normal."

He acknowledges the importance of establishing appropriate boundaries:

"For the companies in the space and rest of society, there's a conversation around what are the specifics and how invasive are those tools."

Chris explains how Granola took a less invasive approach from the beginning:

"Granola from the get-go never stored the audio, it only stores the transcripts, which limits how useful we can be but it makes it way less invasive than the other AI meeting bots out there."

He predicts that the privacy conversation will evolve beyond whether meetings are transcribed to focus more on access controls:

"The conversation is going to shift from whether or not something is transcribed to who has access to that transcript. Is it just me? Because lots of meetings I don't want anybody else to have access to that transcript. Is it my team? Is it my company? Is it the world?"

Chris concludes with a metaphor comparing AI meeting tools to the discovery of fire – too useful to abandon, but requiring thoughtful norms:

"It's like someone's discovered fire. No one's going to be like, 'We're not going to use fire, we're not going to heat ourselves or cook food.' It's so damn useful, we're going to use it, but how do we use it in a thoughtful way with good norms that actually minimize potential bad situations for the most upside?"

The episode concludes with Michael thanking the guests and asking listeners to rate and review the show.

Timestamp: [35:45-38:01]

• While designers and engineers have powerful productivity tools, professionals whose work centers on conversations have lacked similar tools until AI made natural language processing possible
• Granola aims to be a "jetpack for the mind" by creating a workspace that amplifies people who work primarily through conversations and meetings
• In early-stage product development, founder intuition and qualitative user feedback are more valuable than quantitative data
• Watching users struggle with tasks provides the context needed to develop better product intuition
• Meeting transcripts contain extraordinarily rich data that can power a wide range of AI applications beyond just note-taking
• The future of Granola involves expanding beyond meeting notes to become the central workspace where people write documents, emails, and other content that benefits from organizational context
• A self-writing company wiki that automatically stays up-to-date based on meeting content represents the type of revolutionary applications possible with meeting data
• AI meeting tools will likely become expected in professional settings, shifting privacy concerns from whether meetings are transcribed to who has access to the transcripts
• Like fire, AI meeting tools are too useful to abandon, but require thoughtful norms and boundaries to maximize benefits while minimizing risks
• Building global products requires thinking beyond local markets from the beginning, particularly focusing on competitiveness in the U.S. market

Timestamp: [28:06-38:01]

Companies/Products:

• Granola - AI-powered meeting notes app built by Chris and Sam
• Figma - Design tool used by designers, mentioned as an example of a professional power tool
• Photoshop - Design tool mentioned as what designers used "back in the day"
• Cursor - IDE (Integrated Development Environment) used by engineers
• VS Code - Microsoft's code editor used by engineers
• Automation Anywhere - Company of audience member Sundeep who asked about privacy
• Lightseed - Venture capital firm that produces the Generative Now podcast
• Pod People - Production partner for the Generative Now podcast

People:

• Jim - Granola team member who built the "self-writing wiki" demo
• Emily - Audience member who asked about early feedback loops
• Sundeep - Audience member from Automation Anywhere who asked about privacy
• Douglas Engelbart - Early computing pioneer referenced by Chris, known for his work on human-computer interaction

Concepts:

• Jetpack for the mind - Chris's description of AI's potential, contrasted with Steve Jobs' description of computers as a "bicycle for the mind"
• Self-writing wiki - Demo showing how meeting data can automatically generate and update documentation
• Faster horses - Reference to the apocryphal Henry Ford quote about not asking customers what they want
• Context window - AI term referring to how much information a model can process at once

Technical Terms:

• Websim/Webui - Referenced tool that generates HTML pages using large language models
• LLM - Large Language Model, the type of AI that powers Granola and similar tools

Timestamp: [28:06-38:01]

Podcast Information:

• Show name: Generative Now
• Host: Michael Mignano, partner at Lightseed
• Production: Produced by Lightseed in partnership with Pod People

Call to Action:

• "If you like this episode please do us a favor and rate and review the show on Spotify and Apple Podcasts"
• "Follow Lightseed at LightseedVP on YouTube, X, LinkedIn and everywhere else"

Future Episodes:

• "We will be back next week with another conversation"

Timestamp: [37:42-38:01]