Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others...
How We Incubate and Launch New Products With AI - Ep. 40 with Danny Aziz, Brandon Gell
Over the last few months at Every, we’ve:
Launched two AI products
Acquired tens of thousands of users
Released a new incubation in private alpha
The weird thing is: We’re a media company with < 10 full-time employees, and we’re mostly bootstrapped.
That’s not how things are supposed to work in startups.
When we started our product incubation arm six months ago, many people told us it wouldn’t work: divided focus, not enough money, and the biggest one—it would be too hard to find talented people to run the products we build.
Yesterday, we proved out one of the biggest risks to our strategy: We launched a brand-new version of our AI product Spiral (https://spiral.computer) with Danny Aziz as GM—who left a $200K salary to join us.
The question is: Why? Why did he join us, and why is the model working when it “shouldn’t” be?
That’s why I invited Danny and Brandon Gell, Every’s head of Studio, on the show. We get into the details of Every’s business model, what makes our flywheel turn, where each of us sees ourselves one year from now, and what happens when you mix media, software, and AI under one roof.
This is a must-watch for anyone who wants to build a business on their own terms, and have a lot of fun while doing it.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:08
All about Spiral, the tool we recently launched: 00:02:15
Why Danny left a $200,000 salary to work at a bootstrapped media company: 00:04:06
How we do a lot of things well at Every: 00:10:33
What makes Every’s flywheel turn: 00:14:44
The kind of people who fit right in at Every: 00:17:11
How Every is differentiated from a standard VC-backed startup: 00:23:25
How Danny found his way into the world of startups: 00:36:11
The tech industry’s affinity for potential over experience: 00:46:43
Where each of us sees ourselves in the next one year: 00:52:38
Links to resources mentioned in the episode:
Danny Aziz: @DannyAziz97
Brandon Gell: @bran_don_gell
Try Spiral here: https://spiral.computer/
More about Every’s product incubation arm: https://every.to/p/introducing-every-studio
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1:00:47
His GPT Wrapper Has Half a Million Users—And Keeps Growing - Ep. 39 with Vicente Silveira
Everyone told Vicente Silveira that his startup—a GPT wrapper—would fail.
Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store.
Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast.
Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building.
A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round.
I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into:
Why staying small and specialized is a bigger advantage than you think
The power of building with your early adopters
Why lean startups are better positioned than frontier AI companies to create radical solutions
When a growing startup should think about raising venture capital
The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents
We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements.
This is a must-watch for small teams building profitable companies at the bleeding edge of AI.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: (00:00:35)
AI PDF’s story begins with an email to OpenAI’s Greg Brockman: (00:02:58)
Why users choose AI PDF over ChatGPT: (00:05:41)
How to compete—and thrive—as a GPT wrapper: (00:06:58)
Why building with early adopters is key: (00:20:49)
Being small and specialized is your biggest advantage: (00:27:53)
When should AI startups raise capital: (00:31:47)
The emerging role of humans who will manage AI agents: (00:34:53)
Why AI is different from other tech revolutions: (00:45:25)
A live demo of an agent integrated into AI PDF: (00:54:01)
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1:03:25
How to Win With Prompt Engineering - Ep. 38 with Jared Zoneraich
Prompt engineering matters more than ever. But it’s evolving into something totally new:
A way for non-technical domain experts to solve complex problems with AI.
I spent an hour talking to prompt wizard Jared Zoneraich, cofounder and CEO of PromptLayer, about why the death of prompt engineering is greatly exaggerated. And why the future of prompting is equipping non-technical experts with the tools to manage, deploy, and evaluate prompts quickly.
We get into:
His theory around why the “irreducible” nature of problems will keep prompt engineering relevant
Prompt engineering best practices around prompts, evals, and datasets
Why it’s important to align your prompts with the language the model speaks
How to run evals when you don’t have ground truth
Why he believes that the companies who have domain experts to scope out the right problems will win in the age of gen AI
This is a must-watch for prompt engineers, people interested in building with AI systems, or anyone who wants to generate predictably good responses from LLMs.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:08
Jared’s hot AGI take: 00:09:54
An inside look at how PromptLayer works: 00:11:49
How AI startups can build defensibility by working with domain experts: 00:15:44
Everything Jared has learned about prompt engineering: 00:25:39
Best practices for evals: 00:29:46
Jared’s take on o-1: 00:32:42
How AI is enabling custom software just for you: 00:39:07
The gnarliest prompt Jared has ever run into: 00:42:02
Who the next generation of non-technical prompt engineers are: 00:46:39
Links to resources mentioned in the episode:
Jared Zoneraich: @imjaredz
PromptLayer: @promptlayer, https://www.promptlayer.com/
A couple of Steven Wolfram’s articles on ChatGPT: What Is ChatGPT Doing … and Why Does It Work?, ChatGPT Gets Its “Wolfram Superpowers”!
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1:02:08
How Notion Cofounder Simon Last Builds AI for Millions of Users - Ep. 37 with Simon Last
This episode is sponsored by Notion. I’ve been using Notion to manage my professional and personal life for almost 10 years. As a company, they pay attention to the craft and ideas underlying the software they build, and that comes through in the experience of using Notion every day. If you’re a startup, get up to 6 months of Notion Plus with unlimited AI—worth up to $6,000—for free by going to https://ntn.so/every, selecting Every in the drop-down partner list, and using the code EveryXNotion.
Notion cofounder Simon Last told me everything he’s learned from integrating AI into a platform that has over 100 million users.
Simon likes to keep a low profile, even though he’s the driving force behind Notion AI, one of the most widely scaled AI applications in the world.
In his first-ever podcast interview, we get into:
What he would build if he started Notion from scratch today with AI
How to get high quality and reliable results from AI at scale
The future of human creativity in a world with machines that think
This is a must-watch for anyone interested in building reliable AI products at scale.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:57
How AI changes the way we build the foundational elements of software: 00:02:28
Simon’s take on the impact of AI on data structures: 00:10:07
The way Simon would rebuild Notion with AI: 00:13:05
How to design good interfaces for LLMs: 00:23:39
An inside look at how Notion ships reliable AI systems at scale: 00:28:22
The tools Simon uses to code: 00:35:41
Simon’s thoughts on scaling inference compute as a new paradigm: 00:38:16
How the growing capabilities of AI will redefine human roles: 00:49:10
Simon’s AGI timeline: 00:50:28
Links to resources mentioned in the episode:
Simon Last: @simonlast
Notion AI: https://www.notion.so/product/ai
The AI code editor Simon uses: Cursor
OpenAI’s definition of AGI that Simon ascribes to: https://openai.com/charter/
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55:50
How Union Square Ventures Built an AI Brain for Venture Capital - Ep. 36 with Matt Cynamon
Union Square Ventures is building an AI operating system to support their investment team.
But it’s not what you think: It’s a constellation of AI tools that captures and synthesizes the firm's collective wisdom. It’s evolving every day, and Matt Cynamon is the mad scientist in charge
Matt calls himself a “regular” at USV. In practice that means he’s responsible for running experiments with AI for the firm. As an inherently curious person with the professional obligation to tinker, he’s built a suite of tools for the firm, including:
The Librarian, a chatbot trained on around 15,000 articles from USV’s blog
Portfolio Tracker, a GPT that analyzes the investments made by the firm
Meeting Notes, a tool that makes it possible for team members to interact with meetings
I sat down with Matt to talk about how AI is enabling him to bring his ideas to life as a generalist, get demos of the tools listed above, and exchange notes on all the other projects he has in the works at USV. We edit actionable insights extracted by an AI from meetings at USV and prepare them to be posted on the firm’s X handle live on the show. We even try out an art project at USV’s office called The Dream Machine, which generates art from conversations. Here’s a link to the episode transcript.
This is a must-watch for anyone interested in riding the AI wave by learning how to ship useful products quickly.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: (00:00:52)
How Matt became in charge of everything AI at USV: (00:01:56)
How AI empowers generalists to be creators: (00:06:22)
The Librarian, a chatbot trained on everything USV has published: (00:10:41)
Portfolio Tracker, an AI tool to track USV’s investments: (00:21:09)
The AI projects that Matt has in the pipeline at USV: (00:27:21)
Meeting Notes, USV’s AI note-taking tool: (00:34:33)
Prompting AI to generate a post for USV’s X handle: (00:44:57)
Why it’s important to diversify ownership over data: (01:00:20)
The Dream Machine, AI that generates images from conversations: (01:03:20)
Links to resources mentioned in the episode:
Matt Cynamon: @mattcynamon
Union Square Ventures: @usv, https://www.usv.com/
More about the AI tools at USV: https://www.usv.com/people/the-librarian/, https://www.usv.com/writing/2024/02/ai-aesthetics/
The X post generated live on the show: https://x.com/usv/status/1847354782941663523
Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves.
For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest.
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