Logan Kilpatrick (head of developer relations)08 February 2024

Inside OpenAI

7Frameworks

15Insights

Episode overview

OpenAI developer-relations head Logan Kilpatrick explains how the company’s high-agency, high-urgency culture and mission-oriented priorities support rapid product development. He argues that builders should focus on domain-specific products and new AI interfaces, while treating context as the central ingredient in effective prompting. The conversation also covers GPTs, agents, multimodal interaction, enterprise adoption, embeddings, and the internal response to OpenAI’s turbulent leadership weekend.

Key ideas

High agency and urgency are two of the most important traits OpenAI seeks because trusted employees can act directly on customer problems.
Companies building on general-purpose models can create defensibility through specialized use cases, proprietary domain knowledge, and purpose-built interfaces.
Prompt quality depends heavily on supplying relevant context about the user, goal, subject, desired output, and source material.
Custom GPTs let non-developers package instructions, files, built-in tools, and external APIs into reusable, domain-specific assistants.
AI products should move beyond basic chat interfaces toward multimodal canvases, asynchronous agents, and data-grounded answers that complete meaningful work.
OpenAI prioritizes mission alignment and API reliability, while revenue and adoption serve as intermediate proxies for compute and better models.
Future models may be faster and more capable, but builders should view them as improved tools for enduring problems rather than magical replacements for product strategy.
The new embeddings models were presented as cheaper, more multilingual infrastructure for grounding answers in an organization’s own knowledge.

Transcript available · source text is retained privately and is not published

Frameworks in this episode

Productivity5 steps

Context Is All You Need Prompting

Treat the model as a brilliant stranger with zero context, and supply what a colleague would already know.

Read →

Leadership4 steps

High Agency, High Urgency Hiring Filter

Hire for two traits only — people who see a problem and go, and people who go now.

Read →

Mindset3 steps

Measure in Hundreds

If your unit of measurement is one hundred attempts, five failures means you have effectively tried zero times.

Read →

Innovation4 steps

The Better Tool, Same Problems Lens

Every model leap gets normalised within months — build for the boring future, not the euphoric one.

Read →

Leadership4 steps

The Constrained-Resource Headcount Test

When the bottleneck isn't people, each new hire is a net productivity loss unless they uplevel everyone.

Read →

Strategy5 steps

The Platform Encroachment Test

Build where the platform's mission says it will never go — the general layer is not yours to own.

Read →

Strategy4 steps

The Proxy Goal Ladder

Every metric you chase is a proxy — climb the ladder to the mission before you optimise it.

Read →

People & resources mentioned

Attributed to the moment in the episode. Timestamps are approximate.

People · 10

Lenny RachitskyMentionstoday we've got another very special compilation episode something I've been pulling on more and more with the podcast and the newsletter
“today my guest is Logan kpatrick”
▶ Watch at ~0:30approx.
Logan KilpatrickMentionsLogan is head of developer relations at open aai where he supports developers building on open AIS apis and J GPT
“Logan is head of developer relations at open aai where he supports developers building on open AIS apis and J GPT”
▶ Watch at ~0:30approx.
Dan ShipperMentionsDan is the co-founder and CEO of Every which is a company that is at the very bleeding edge of what is possible with AI.
“a huge thank you to Dan shipper and Dennis Yang for some great question suggestions”
▶ Watch at ~1:30approx.
Greg BrockmanMentionsthere's everybody has uh and had and continues to have like such deep trust in in Sam and Greg and our leadership team
“there's everybody has uh and had and continues to have like such deep trust in in Sam and Greg and our leadership team”
▶ Watch at ~4:30approx.
Sam AltmanMentionsthere's everybody has uh and had and continues to have like such deep trust in in Sam and Greg and our leadership team
“there's everybody has uh and had and continues to have like such deep trust in in Sam and Greg and our leadership team”
▶ Watch at ~4:30approx.
Dennis YangMentionsI have this good friend uh his name's Dennis Yang he works at chime
“I have this good friend uh his name's Dennis Yang he works at chime”
▶ Watch at ~16:00approx.
Tim FerrissCoinedCited for his fear-setting framework and as an influence on Paul Millerd's five-episode podcast experiment.
“Tim Ferris suggested this really good idea that I've been stealing”
▶ Watch at ~22:00approx.
Tyler CowenMentionshe has this awesome blog marginal Revolution and he's really good at sharing insights from research papers
“in the style of Tyler Cowan who I think is the best interviewer”
▶ Watch at ~22:30approx.
Bill GatesMentionsI think that J Leno is a greater philanthropist than Bill Gates
“if you saw the recent Sam and and Bill Gates interview”
▶ Watch at ~41:00approx.
Sal KhanCoinedthe one room Schoolhouse by Salon incredible
“the one room Schoolhouse by Salon incredible”
▶ Watch at ~59:30approx.

Resources · 37

AppleMentionscompany · Steve Jobs, Steve Wozniak, and Ronald Wayne
“before open aai Logan was a machine learning engineer at Apple”
▶ Watch at ~0:30approx.
NASAMentionscompany
“and advised NASA on their open source policy”
▶ Watch at ~0:30approx.
HexMentionssoftware · Hex Technologies
“this episode is brought to you by hex”
▶ Watch at ~1:30approx.
WhimsicalMentionssoftware · Whimsical
“this episode is brought to you by whimsicle the iterative product workspace”
▶ Watch at ~3:00approx.
tldrawMentionssoftware · tldraw
“this company called TLD draw I don't know if you've seen TL draw”
▶ Watch at ~8:30approx.
Rabbit R1Mentionsproduct · Rabbit Inc.
“the rabbit R1 I don't know if you've seen that but it's a consumer Hardware device”
▶ Watch at ~8:30approx.
HarveyMentionssoftware · Harvey AI
“it's actually like Harvey I don't know if you've seen Harvey but it's this legal AI use case”
▶ Watch at ~10:30approx.
OpenAIUsescompany
“people can do things like fine-tuning and build all their own custom you know UI and product features on top of that”
▶ Watch at ~11:00approx.
OpenAI APIUsessoftware · OpenAI
“companies who are and have some domain specific knowledge and they're really excited about that problem space like they can go and do that”
▶ Watch at ~12:00approx.
GitHub CopilotMentionssoftware · GitHub
“GitHub probably has a bunch of really great studies they published around like co-pilots”
▶ Watch at ~14:30approx.
ChimeUsescompany
“he built a GPT that helps write ads for Facebook and Google”
▶ Watch at ~16:00approx.
DALL-EMentionssoftware · OpenAI
“we already kind of do this in the context of Dolly”
▶ Watch at ~21:00approx.
OpenAI Prompt Engineering GuideRecommendswebsite · OpenAI
“we actually have a prompt engineering guide um which folks should go and check out”
▶ Watch at ~24:30approx.
GPT StoreMentionssoftware · OpenAI
“there's this whole store now basically it's the whole App Store that you guys have launched”
▶ Watch at ~26:30approx.
GPTsMentionssoftware · OpenAI
“people can go and like chat with essentially a custom version of chat gbt”
▶ Watch at ~27:00approx.
CanvaMentionscompany · Melanie Perkins, Cliff Obrecht, and Cameron Adams
“canva has the top GPT currently”
▶ Watch at ~29:00approx.link ↗
ZapierRecommendssoftware · Zapier, Inc.
“all of the stuff that zappier has done with gpts is like the most useful stuff that you could imagine”
▶ Watch at ~29:30approx.
RunwayMentionscompany · Runway
“the CEO of a company called Runway built this thing called Universal primer”
▶ Watch at ~30:00approx.
Universal PrimerRecommendssoftware
“built this thing called Universal primer which helps you learn it's described as learn everything about anything”
▶ Watch at ~30:00approx.
ArcadeMentionssoftware · Arcade
“arcade is an interactive demo platform that enables teams to create polished unbrand Demos in minutes”
▶ Watch at ~31:00approx.
Assistants APICoinedsoftware · OpenAI
“the assistance API that we released for Dev day”
▶ Watch at ~35:00approx.
SlackUsessoftware · Slack Technologies
“opening is such a slack heavy culture”
▶ Watch at ~40:30approx.
Google DocsUsessoftware · Google
“I'm a Google Docs customer and like I love using Google Docs”
▶ Watch at ~42:00approx.
GPT-4 Technical ReportCoinedpaper · OpenAI
“if folks have looked through the gbg4 technical report that we released back in March when gbt 4 came out”
▶ Watch at ~48:00approx.
GPT-4Mentionssoftware · OpenAI
“qb4 came out the the consensus from the world is everything is different”
▶ Watch at ~49:00approx.
GPT-5Mentionssoftware · OpenAI
“gbt 5 is like surely going to be extremely useful”
▶ Watch at ~49:00approx.
GPT-4 TurboCoinedsoftware · OpenAI
“updated gbt 4 Turbo model um updated the preview model that we released at Dev day”
▶ Watch at ~52:30approx.
OpenAI Embeddings v3Coinedsoftware · OpenAI
“the big thing is this is the third generation embedding model”
▶ Watch at ~53:00approx.
LennybotCoinedwebsite · Lenny Rachitsky
“lenot lot.com check it out”
▶ Watch at ~53:30approx.
Visual ElectricRecommendssoftware · Visual Electric
“I'll give a shout out to a product I'm not an investor but I know the founder called visual electric.com”
▶ Watch at ~57:00approx.
The One World SchoolhouseRecommendsbook · Sal Khan
“the first one it's one that I read a long time ago and came back to recently is the one room Schoolhouse”
▶ Watch at ~59:30approx.
Why We SleepRecommendsbook · Matthew Walker
“the other one is uh that I always come back to is why we sleep”
▶ Watch at ~1:00:00approx.
Gran TurismoRecommendsfilm · Neill Blomkamp
“I watched with my family recently over the holidays this Grand Turismo movie”
▶ Watch at ~1:00:00approx.
Manta Sleep MaskRecommendsproduct · Manta Sleep
“it's called like man Manta sleep or something like that it's a weighted sleep mask and it feels incredible”
▶ Watch at ~1:01:30approx.
WAOAW Sleep MaskRecommendsproduct · WAOAW
“my favorite is called the wow waha sleep mask uh W like o a w AO A W”
▶ Watch at ~1:02:00approx.
ChatGPTRecommendssoftware · OpenAI
“what you need to do is actually just go to chat chat. open.com and try the stuff out”
▶ Watch at ~1:05:30approx.
Lenny's PodcastMentionspodcast · Lenny Rachitsky
“you can subscribe to the show on Apple podcast Spotify or your favorite podcast app”
▶ Watch at ~1:07:30approx.

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Hot Take· 4

Hot Take06:30

Why Logan is grateful the OpenAI crisis happened now, not later

Reflecting on the board drama, Logan says one of his takeaways is gratitude that it happened while the stakes were relatively low. Today OpenAI's collapse would hurt customers who built businesses on it, but on a world scale someone else would continue the march toward general intelligence. Had the same event happened in five or ten years, the outcome could have been far worse.

Customers have built businesses on OpenAI, so a failure would hurt them
On a world scale, another lab would still build the models if OpenAI disappeared
The same crisis five to ten years later could have been much worse
Lower current stakes made this the better time for it to happen

“one of my takeaways was I'm actually very grateful that this happened when it happened”
Logan Kilpatrick · 06:30

“somebody else will build a a model if open AI disappeared”
Logan Kilpatrick · 07:00

#openai#agi#risk

Hot Take08:00

2024 is the year AI moves beyond the chat box

Logan is most excited by new interfaces to AI beyond chat, citing the Rabbit R1 hardware device and TL Draw's infinite canvas. He argues chat is only the predominant interface today, and that humans often make more sense of information laid out on a canvas than listed in a chat thread. He frames 2024 as the year of multimodal AI and of pushing new UX paradigms.

Rabbit R1 and TL Draw's infinite canvas are early examples of non-chat AI interfaces
A canvas the AI fills in with files, videos, and references can beat a chat list
2024 is the year of multimodal AI and new UX paradigms
Chat is predominant today but not the natural end state

“I think like 2024 is the year of multimodal AI but it's also the year that people really push the boundaries of um some of…”
Logan Kilpatrick · 09:30

#ai-interfaces#multimodal#ux#product

Hot Take39:00

At OpenAI, revenue is just a proxy for compute

Logan explains that OpenAI's stated goals are often abstractions for something deeper. Even revenue isn't the real goal — it's a proxy for buying more compute, which buys more GPUs, which trains better models, which advances the mission. He warns that hearing a goal like revenue in a vacuum misleads people into thinking OpenAI just wants to make money.

Stated goals are often intermediate abstractions toward the mission
Revenue is a proxy for compute, which means more GPUs and better models
Hearing 'revenue' out of context misreads OpenAI as just chasing money
The real end goal is achieving the mission, not the metric

“even if revenue is a goal it's like revenue is not actually the goal revenue is a proxy for getting more compute which is then…”
Logan Kilpatrick · 39:30

#openai#strategy#metrics#compute

Hot Take1:06:30

AI won't replace you — humans using AI will

Logan closes on the narrative he endorses: it isn't AI that replaces humans, it's other humans who are augmented by AI tools and thereby more competitive in the job market. He frames now as the best time to learn these tools, since using them makes you more productive and empowered in your work.

It's not AI replacing humans, it's AI-augmented humans out-competing others
AI-augmented workers are more competitive in the job market
Now is the best time to learn these tools
Using AI makes you more productive and empowered in your job

“it's not AI That's going to replace humans it's like other humans that are being augmented and like using AI tools that are like going…”
Logan Kilpatrick · 1:06:30

#future-of-work#ai#careers#advice

Explainer· 3

Explainer13:30

Engineering is the highest-leverage use of AI today

Logan argues software engineering is one of the highest-leverage tasks to apply AI to right now, estimating at least a 50% improvement, especially on lower-hanging fruit. He notes he ships faster himself using ChatGPT and points to GitHub Copilot studies as an analogy for the gains. He also references a Harvard Business School study on the order-of-magnitude efficiency gains for AI users.

Engineering is one of the highest-leverage tasks to apply AI to today
At least a 50% improvement, especially on lower-hanging-fruit tasks
GitHub Copilot studies are a useful analogy for the productivity gains
An HBS study measured order-of-magnitude efficiency gains for AI users

“I think engineering is actually like one of the highest leverage things that you could be using AI to do today and like really unlocking…”
Logan Kilpatrick · 14:30

#productivity#engineering#chatgpt#copilot

Explainer18:30

Prompt engineering is really about giving context

Logan reframes prompt engineering as a fundamentally human act: models, like people, give generic answers when they lack context. The model has human-level intelligence but knows nothing about you, your goals, or what you'll ask, so generic prompts get generic responses. His core advice is that context is the thing that matters most for getting value from a language model.

Models default to generic answers because they're trained to just answer the question
Treat the model as human-level intelligence with zero context about you
Generic outputs come from users forgetting to supply context
'Context is all you need' — the single biggest lever on output quality

“imagine a human human level intelligence but like literally no context like it has no idea what you're going to ask it it's never met…”
Logan Kilpatrick · 20:30

“context is is all you need context is the only thing that matters”
Logan Kilpatrick · 24:00

#prompt-engineering#chatgpt#context#llms

Explainer43:00

Why adding a researcher can slow the whole team down

Logan relays a counterintuitive point from an OpenAI researcher: in a GPU-constrained world, each new researcher can be a net productivity loss for the group. Unless the new hire dramatically uplevels everyone, adding someone on a different research direction forces everyone to share GPUs, slowing all experiments. This is why OpenAI keeps its research team intentionally small, unlike product teams where more engineers means more output.

In a GPU-constrained world, a new researcher can be a net productivity loss
A new person on a different direction means everyone shares GPUs and slows down
The exception is a hire who profoundly uplevels the whole group
OpenAI keeps research small on purpose; product teams scale differently

“if you just add somebody who's going to go and like tackle some completely different research Direction you now have to share your gpus with…”
Logan Kilpatrick · 44:00

#research#gpus#openai#scaling

Story· 1

Story04:00

What the Sam Altman board weekend was like from the inside

Logan describes the shock inside OpenAI when the board removed Sam Altman over Thanksgiving weekend, a week the company had planned as a rare full reset. Despite deep trust in leadership being suddenly tested, what surprised him most was how fast everyone returned to work. He believes many companies would have been derailed for a long time by an event like this.

The changes landed Friday afternoon of a rare company-wide break week
Employees had deep, longstanding trust in Sam and Greg, making it very surprising
OpenAI's transparent culture meant people usually hear about problems early, but not this time
By Monday the team was laser-focused and back to work, not derailed

“I think the thing that surprised me the most was like just how quickly everybody got back to business”
Logan Kilpatrick · 05:30

#openai#leadership#culture#sam-altman

Q&A· 1

Q&A10:00

How to build so OpenAI won't disrupt you

Asked how founders should think about where OpenAI won't go, Logan says OpenAI is focused on very general capabilities like reasoning, coding, and writing. Vertical and domain-specific applications, like Harvey for legal work, are safe because OpenAI's general models will never be as tuned as a specialist's custom build. He warns that anyone building a general-purpose assistant should expect OpenAI to eventually compete there.

OpenAI targets general reasoning, coding, and writing, not verticals
Harvey (legal AI) is a safe example: custom models OpenAI won't match
Expect OpenAI to launch general-purpose agent products (it already does via GPTs)
OpenAI won't build verticalized products like an AI sales agent

“we're not going to launch like some of these like very verticalized products like we're not going to launch like an AI sales agent”
Logan Kilpatrick · 11:30

#product-strategy#startups#openai#moats

Tool· 2

Tool17:00

The private planning GPT Logan built for himself

Logan describes a personal GPT he built to force better quarterly planning. He took best-practice planning tips from an online article, loaded them into the GPT, and now runs his plans through it to generate timelines, metrics, success criteria, and important cross-functional stakeholders. He says it forces him to consider the things people typically miss or are bad at during planning.

Built from planning best-practice tips found in an online article
Generates timelines, specific metrics, and success criteria for a plan
Surfaces cross-functional stakeholders to include in the process
Forces consistency and catches what people usually miss in planning

“the private gbt that I use myself that like helps with some of the like planning stuff for for different quarters”
Logan Kilpatrick · 17:00

#gpts#planning#productivity#workflow

Tool52:30

The new V3 embedding models: cheaper and multilingual

Logan previews OpenAI's third-generation embedding models, the tech behind question-answering over your own documents (like Lenny's own lennybot.com). The big improvements are much stronger non-English performance, opening embeddings to many more languages, and roughly five times lower cost. He notes you can embed about 62,000 pages of text for a dollar.

V3 embeddings power Q&A grounded in your own corpus of knowledge
Non-English performance improved significantly, unlocking many more languages
Roughly five times cheaper than before
About 62,000 pages of text can be embedded for one dollar

“I think now it's like you can embed I'm pretty sure was like 62,000 pages of text for $1”
Logan Kilpatrick · 55:00

“it's like five times cheaper as well which is wonderful”
Logan Kilpatrick · 54:30

#embeddings#openai-api#pricing#rag

Takeaway· 4

Takeaway24:30

Why a smiley face can make ChatGPT answer better

Logan explains the odd prompt tricks that work, like adding a smiley face or telling the model to take a break, because the training corpus is human-to-human communication where those cues signal effort and positivity. He cautions these give only a small lift, on the order of one or two percent, which may be imperceptible on short answers but can matter materially on long generations.

Adding a smiley face or telling the model to 'take a break' can raise performance
It works because models are trained on human-to-human messages
The gain is small, roughly one or two percent
Likely imperceptible on short answers, but material on long text

“there's a lot of like really small silly things like adding a smiley face increases the performance of the model”
Logan Kilpatrick · 24:30

#prompt-engineering#chatgpt#tips

Takeaway32:00

The two traits OpenAI hires for: high agency and urgency

Logan says if he were hiring five people today, the top two traits he'd look for are high agency and working with urgency. High-agency people don't need to gather fifty people's consensus; they hear a customer problem and immediately start pushing on the solution. He contrasts this with traditional companies that stall by checking with seven different departments.

High agency and urgency are the top two traits he'd hire for
High-agency people act without needing broad consensus
They hear a customer challenge and immediately push on a solution
Traditional companies stall by routing feedback through many departments

“folks are so high agency like they see a problem and they go and tackle it like they hear something from our customers about a…”
Logan Kilpatrick · 34:00

#hiring#culture#openai#agency

Takeaway47:30

GPT-5 will feel normal fast — plan for that, not magic

Logan pushes back on wild GPT-5 expectations. Like GPT-4, it will be an extremely effective tool that solves the same problems better and faster, not a system doing backflips while writing your code. His edge-giving insight: powerful tools become normal very quickly, so planning for rapid user habituation is smarter than assuming the model will change everything.

GPT-5 will be a very effective tool similar to GPT-4, not a magic leap
The same real-world problems remain; you just get a better tool for them
Powerful AI tools become 'very normal very quickly'
Planning for fast habituation is an edge; expecting it to change everything is the wrong framing

“it is just going to be this like very effective tool very similar to gbd4 and it's also going to become like very normal very…”
Logan Kilpatrick · 50:00

#gpt-5#product-strategy#expectations#ai

Takeaway1:03:00

Logan's life motto: measure in hundreds

Logan keeps a note behind his camera that reads 'measure in hundreds.' The idea is that when people say they tried something and it didn't work, they've usually tried a handful of times, which on a scale of hundreds is effectively zero. Everything compounds over many attempts, so if you don't try enough times you'll never succeed.

'Measure in hundreds' is his life motto, kept on a note behind his camera
Five failed attempts, measured against hundreds, is basically zero tries
Success is built on compounding and many attempts
Too few attempts guarantees you never succeed at something

“and it's measure in hundreds I love this idea of measuring things in hundreds”
Logan Kilpatrick · 1:03:00

“if I measure in hundreds the five times that you failed at something youve failed tried zero times”
Logan Kilpatrick · 1:03:00

#mindset#persistence#advice

Key ideas

Frameworks in this episode

Context Is All You Need Prompting

High Agency, High Urgency Hiring Filter

Measure in Hundreds

The Better Tool, Same Problems Lens

The Constrained-Resource Headcount Test

The Platform Encroachment Test

The Proxy Goal Ladder

People & resources mentioned

People · 10

Resources · 37

Insights & moments

◆Hot Take· 4

Why Logan is grateful the OpenAI crisis happened now, not later

2024 is the year AI moves beyond the chat box

At OpenAI, revenue is just a proxy for compute

AI won't replace you — humans using AI will

✶Explainer· 3

Engineering is the highest-leverage use of AI today

Prompt engineering is really about giving context

Why adding a researcher can slow the whole team down

❝Story· 1

What the Sam Altman board weekend was like from the inside

?Q&A· 1

How to build so OpenAI won't disrupt you

✦Tool· 2

The private planning GPT Logan built for himself

The new V3 embedding models: cheaper and multilingual

▲Takeaway· 4

Why a smiley face can make ChatGPT answer better

The two traits OpenAI hires for: high agency and urgency

GPT-5 will feel normal fast — plan for that, not magic

Logan's life motto: measure in hundreds

Hot Take· 4

Explainer· 3

Story· 1

Q&A· 1

Tool· 2

Takeaway· 4