Startups Ideas You Can Now Build With AI

The conversation opens with excitement about the revolutionary capabilities of new AI models, particularly highlighting the massive context window now available with Gemini 2.5 Pro.

This introduction sets up the central theme that we're at an inflection point where previous startup ideas that couldn't work before are suddenly viable because of AI breakthroughs.

Timestamp: [00:43-01:08]

Harj shares his personal experience with Triple Bite, a recruiting startup he ran for almost 5 years, to illustrate how AI is transforming previously challenging business models.

He explains that the excitement back then stemmed from applying marketplace models to recruiting, but they faced significant challenges. Triple Bite's approach required building a curated marketplace that evaluated engineers and provided detailed data about candidates.

The complexity of their operation required a three-sided marketplace: companies hiring engineers, engineers looking for jobs, and contracted engineers to conduct the interviews - making the business model extremely challenging.

Timestamp: [01:08-02:39]

Harj explains how AI, particularly code evaluation models, has transformed what's possible in the recruiting space.

He highlights Meror as a hot AI startup following a similar concept to Triple Bite - a marketplace for hiring software engineers - but with a crucial difference enabled by AI.

This technology advantage allowed Meror to quickly expand beyond engineers to analysts and other knowledge workers - something that would have taken Triple Bite years since they would have needed to rebuild their labeled datasets for each new category.

This represents a fundamental shift in what's possible, making the recruiting startup space much more exciting than it was five years ago.

Timestamp: [02:39-03:44]

Garry builds on Harj's insights, suggesting broader implications for marketplace businesses across industries.

He also points to existing two-sided marketplaces like Duolingo that are now "under fire" as they begin to replace human interactions with AI, specifically using AI for language conversation partners.

The takeaway is clear: founders should be examining virtually any marketplace and asking how LLMs might transform its structure and operations.

Timestamp: [03:44-04:15]

Harj discusses the psychological barriers to entering spaces where previous startups have failed despite significant investment.

He notes that recruiting startups like Triple Bite ($50M raised) and Hired (over $100M raised) collectively attracted hundreds of millions in funding but "overall as a category did not do particularly well."

He emphasizes that founders need to push through cynicism from investors who have lost money on similar ideas in the past.

Timestamp: [04:15-05:10]

Garry draws a parallel to Instacart's success despite the cautionary tale of Webvan's massive failure in online grocery delivery.

What made the difference was a fundamental technology shift: the widespread adoption of smartphones enabled a mobile marketplace that wasn't possible during Webvan's era.

This historical example reinforces why the current moment with AI is so exciting - all the walls in the "idea maze" have shifted, creating new pathways to success in previously challenging spaces.

Timestamp: [05:10-05:45]

The conversation deepens the Instacart/Webvan comparison, highlighting how fundamental technology shifts enable previously failed business models to succeed.

This pattern is repeating with LLMs transforming recruiting companies and numerous other business categories. The key insight is that breakthrough technologies don't just improve existing models - they fundamentally change what's possible.

Timestamp: [05:45-06:10]

The discussion shifts to how focusing on specific pain points within larger markets can create valuable opportunities, using the example of Apriora, a YC-funded company.

The speakers highlight that technical screening is a significant pain point for engineering teams, where:

By focusing specifically on this one challenging aspect of the recruiting process, Apriora found success with large companies.

Timestamp: [06:10-06:58]

Harj explains how AI allows companies like Apriora to expand their target market by enabling more sophisticated evaluations.

The key advancement is that AI-powered products can now perform more nuanced evaluations that work for senior candidates as well.

This capability significantly expands the addressable market for technical screening tools.

Timestamp: [06:58-07:36]

The conversation transitions to education technology, where hyperpersonalization has been a persistent challenge.

Harj expresses excitement about the longstanding dream of personalized education finally becoming possible.

He notes that while the internet has made learning easier, we've never truly had personalized learning or "a personalized tutor in your pocket" - until now with AI.

Timestamp: [07:36-08:30]

The speakers highlight two YC-funded companies successfully applying AI to education:

First, Revision Dojo helps students with exam preparation.

They note that Revision Dojo has attracted "a lot of DAUs and a lot of power users," indicating strong product-market fit.

The second example is Adexia, which creates tools for teachers to grade assignments - addressing another pain point similar to the technical screening issue in recruiting.

These examples demonstrate how AI can transform both learning and teaching by automating tedious aspects of education.

Timestamp: [08:30-09:33]

The conversation concludes with an observation about the adoption patterns of these new educational technologies.

This raises a question about how to bring these innovations to the public education system, where they may be needed most.

This highlights a broader challenge beyond the technology itself - how to ensure innovations reach the institutions and populations that could benefit most from them.

Timestamp: [09:33-09:45]

• AI, particularly large language models with massive context windows, is enabling previously failed startup ideas to finally become viable
• Recruiting marketplaces that required complex human evaluation can now leverage AI for faster, more scalable assessment
• Three or four-sided marketplaces can potentially be simplified to two-sided marketplaces with AI replacing human intermediaries
• The pattern resembles how mobile technology enabled Instacart to succeed where Webvan failed - fundamental technology shifts create new possibilities
• Focusing on specific pain points within larger markets (like technical screening) can be an effective entry strategy
• AI enables more sophisticated evaluations that expand addressable markets beyond entry-level candidates
• Personalized education - the "tutor in your pocket" dream - is finally becoming possible through AI
• Solutions that eliminate tedious work that professionals hate (like grading for teachers) represent significant opportunities
• Institutional adoption challenges remain, with private schools moving faster than public education systems

Timestamp: [00:43-09:45]

Companies:

• Triple Bite - Harj's recruiting marketplace startup that raised ~$50M
• Hired - Competitor to Triple Bite that raised over $100M
• Meror - Current AI-powered recruiting startup mentioned as a hot company
• Webvan - Failed online grocery delivery company from the dot-com era
• Instacart - Successful grocery delivery company that succeeded where Webvan failed
• Duolingo - Language learning app mentioned as potentially replacing human interaction with AI
• Apriora - YC-funded company building AI agents for technical interview screening
• Niantic - Company where one of the speakers previously ran engineering teams
• Revision Dojo - YC-funded company creating personalized exam prep solutions
• Adexia - YC-funded company building AI tools to help teachers grade assignments

People:

• Harj - YC partner who previously ran Triple Bite
• Garry - YC partner participating in the discussion
• Nico - YC GP mentioned as having funded Apriora
• Jared - YC partner mentioned in relation to Adexia

Technology Concepts:

• LLMs (Large Language Models) - Core AI technology enabling new startup possibilities
• Million token context window - Referenced as a capability in Gemini 2.5 Pro
• Labeled data sets - Mentioned as previously required for machine learning models
• AI codegen models - Specifically referenced for code evaluation capabilities
• Hyperpersonalization - Described as the "holy grail" for edtech

Timestamp: [00:43-09:45]

The conversation shifts to a crucial question about AI-powered startups: do better products automatically get better distribution?

This raises a fundamental business challenge - even with revolutionary AI capabilities, startups still face the age-old challenge of gaining users and market share.

Timestamp: [09:50-10:17]

The discussion turns to the changing economics of AI and how this impacts business models.

The speakers observe promising trends that could eventually make AI more accessible and affordable:

This cost trajectory suggests exciting possibilities for consumer applications that have previously been cost-prohibitive.

Timestamp: [10:17-11:21]

As AI costs decrease, the speakers suggest we might see a revival of the freemium business model that was popular in the Web 2.0 era.

OpenAI is cited as an example already implementing this approach, as is the education startup Study with 2DS, which is seeing significant success.

The speakers express optimism that as costs continue to decrease, this model could enable massive scale.

Timestamp: [11:21-12:15]

The conversation highlights Speak as an example of an EdTech company that was ahead of the curve on personalization.

The company focused intensely on personalizing language learning and found initial traction in Korea among English learners. When GPT-3 and 3.5 were released, they recognized the opportunity and doubled down.

This example illustrates how companies with the right foundational thesis can leverage AI advancements to accelerate their growth.

Timestamp: [12:15-13:21]

The discussion shifts to how AI enables value-based pricing, drawing parallels between enterprise and consumer markets.

This same principle can apply to consumer products, particularly in education.

The key insight is that AI can transform the perceived value of the product:

This value transformation means companies might not need massive scale to build substantial businesses.

Timestamp: [13:21-14:39]

The conversation turns to the importance of building defensible business moats, even with AI-powered products.

In educational contexts, this might mean integration with platforms like Clever for authentication. The speakers emphasize that simply incorporating AI isn't enough.

They note that OpenAI itself is supportive of startups building on their API:

However, they also acknowledge OpenAI's growing interest in applications, evidenced by hiring the former Instacart CEO.

Timestamp: [14:39-15:35]

The conversation addresses the curious phenomenon of large tech companies not fully leveraging AI capabilities in their products.

The most striking example mentioned is Apple's voice assistant:

This observation sets up the discussion for the next topic on platform neutrality.

Timestamp: [15:35-16:09]

The final section of the conversation advocates for platform neutrality as a necessary condition for innovation in AI.

The speaker draws parallels to previous tech policy battles:

Windows is cited as another example where government intervention created space for competition:

The speaker argues this created the conditions for Google's rise:

They conclude by suggesting similar principles should apply to voice assistants on smartphones:

Timestamp: [16:09-17:37]

• Better AI products don't automatically gain distribution; startups still need effective go-to-market strategies
• The cost of AI intelligence is decreasing rapidly, potentially enabling free or freemium consumer AI products
• As AI reaches human-level quality in specific domains, it enables value-based pricing similar to human services
• Consumer AI businesses can achieve significant revenue with smaller user bases by charging premium prices for truly valuable capabilities
• Successful AI companies still need traditional defensible moats: brand, switching costs, and integrations
• OpenAI aims to be platform-like for startups while also exploring direct applications
• Major tech platforms are still underutilizing AI capabilities in their core products
• Platform neutrality (like net neutrality) may be necessary to create fair market conditions for AI innovation
• Previous government interventions in tech (browser choice, search engine choice) created conditions for competition
• Voice assistant choice on smartphones represents a current area where platform neutrality is lacking

Timestamp: [09:50-17:37]

Companies:

• OpenAI - Mentioned as potentially becoming a trillion-dollar company and their approach to the API and application markets
• Speak - EdTech company focused on personalized language learning that was founded before LLMs and thrived with their arrival
• Duolingo - Referenced as the dominant language learning app that Speak was competing against
• Study with 2DS - Education startup mentioned as having success with AI-powered learning
• Instacart - Mentioned because OpenAI hired their CEO for applications
• Clever - Educational technology platform mentioned for authentication integration
• Google - Referenced in discussions about search engines, platform neutrality, and Google Assistant
• Apple - Mentioned regarding Siri and platform control

People:

• Sam Altman - Cited for his perspective that "it's not enough to drop AI in it, you still have to actually build a business"

Technology Concepts:

• LLMs (Large Language Models) - Core technology enabling new product capabilities
• Freemium/Premium Model - Business approach from Web 2.0 era potentially making a comeback with AI
• Platform Neutrality - Concept advocated for AI similar to net neutrality for internet
• Net Neutrality - Historical comparison to current platform neutrality needs
• Model Distillation - Process of transferring knowledge from larger to smaller models
• MAUs (Monthly Active Users) - Metric mentioned regarding Speak's success

Historical References:

• Web 2.0 - Referenced regarding freemium business models
• Internet Explorer dominance - Cited as a moment when government intervention enabled competition
• Windows browser choice - Example of government-mandated platform neutrality

Timestamp: [09:50-17:37]

The conversation opens with an interesting observation about the surprising disparity between Google's Gemini and OpenAI's ChatGPT usage despite Google's technical capabilities.

This gap appears especially puzzling given that YC's internal work has found Gemini 2.5 Pro to be highly competitive:

The speakers note that this technical parity hasn't translated to consumer awareness or adoption, despite Google already having billions of users through their existing products.

Timestamp: [17:42-18:17]

The discussion continues by exploring the advantages of being first to market in the AI space.

The speakers suggest that being perceived as the leader can be more important than having objectively superior technology:

This observation points to the challenges larger tech companies face in competing with more focused AI startups, even when the larger companies have technical parity or advantages.

Timestamp: [18:17-18:46]

The conversation turns to the challenges big tech companies face in effectively integrating AI into their products.

Despite having strong underlying models, the speakers express frustration with how these capabilities are implemented in consumer products:

The speakers suggest that the issue may stem from organizational complexity rather than technical limitations.

Timestamp: [18:46-19:21]

The discussion delves into specific organizational issues at Google that may be hampering their AI efforts.

This confusing product strategy creates friction even for developers trying to use Google's AI:

The speakers attribute this to Google's corporate culture, which differs significantly from startups:

Timestamp: [19:21-20:18]

Using a Game of Thrones analogy, the speakers highlight Google's potential advantage in the AI race through their custom hardware.

This hardware advantage could be decisive in making AI more affordable and accessible:

The speakers suggest that while other AI labs have been limited in deploying large context windows due to costs, Google's custom Tensor Processing Units (TPUs) give them a unique capability to overcome this barrier.

Timestamp: [20:24-21:02]

The conversation briefly touches on OpenAI's strategic focus on computing infrastructure.

This observation suggests that OpenAI recognizes the strategic importance of hardware in the AI race and is likely developing their own capabilities to compete with Google's TPU advantage.

Timestamp: [21:02-21:26]

The discussion turns to the classic business challenge known as the innovator's dilemma, as it applies to Google's search business.

The speakers suggest that making this kind of radical transition requires a certain type of leadership:

They note that Mark Zuckerberg has demonstrated this type of bold decision-making by renaming Facebook to Meta, showing a willingness to make radical strategic pivots despite short-term costs.

Timestamp: [21:26-21:56]

The conversation shifts to criticize Meta's approach to integrating AI into its messaging platforms.

The implementation is described as invasive and poorly designed:

The speakers note that the AI's capabilities don't justify this intrusive approach:

They compare this to previous controversial product launches at Facebook:

Timestamp: [21:56-22:52]

The discussion continues with observations about Meta AI's puzzling limitations, particularly its inability to access Facebook's core social data.

This critique highlights how Meta has failed to leverage its unique data advantage - the social graph - in creating an AI assistant that would have a clear differentiation from competitors.

Timestamp: [22:52-23:26]

The conversation references an essay by YC partner Pete Koomen that analyzes Google's problematic approach to AI integration in Gmail.

One key insight from the essay concerns system prompts versus user prompts:

The system prompt is described as what's "imposed upon the user," limiting customization and resulting in rigid, formal outputs:

The speakers praise the blog post itself for its interactive nature:

Timestamp: [23:26-24:53]

The discussion concludes with the speakers identifying a potential startup opportunity inspired by Pete Koomen's interactive blog post.

They jokingly describe it as "AI-Posterous", and while one speaker mentions they don't have time to build it themselves, they offer it as a free idea:

This highlights how the YC partners are constantly thinking about new startup opportunities that emerge from observing gaps in the current technology landscape.

Timestamp: [24:53-25:08]

• Despite technical parity or superiority, Google's Gemini models have dramatically lower consumer usage than OpenAI's ChatGPT
• First-mover advantage creates an "intangible moat" in AI, where being perceived as the best can outweigh actual technical superiority
• Big tech companies are struggling with AI integration, creating products that underperform despite strong underlying technology
• Google's organizational structure leads to competing AI products (Gemini vs. Vertex Gemini) from different internal teams
• Google has a potentially decisive hardware advantage with TPUs that could dramatically reduce AI costs and enable larger context windows
• The "innovator's dilemma" prevents Google from replacing search with AI chatbots despite technical capability, as it would sacrifice revenue
• Meta's AI integration in messaging platforms feels invasive and poorly designed, reflecting leadership's "objectively optimal" product approach
• Meta AI fails to leverage the company's key advantage (social graph data), limiting its usefulness and differentiation
• Google's Gemini integration in Gmail lacks customization options by not allowing users to modify system prompts
• Interactive AI-powered blogging platforms represent an emerging opportunity for startups

Timestamp: [17:42-25:08]

Companies:

• Google - Discussed extensively regarding their Gemini AI models, TPUs, organizational challenges, and search business
• OpenAI - Referenced for ChatGPT's dominant usage compared to Gemini despite Google's technical capabilities
• DeepMind - Mentioned as one of the Google entities developing an API for Gemini
• GCP (Google Cloud Platform) - Noted as another Google division with its own separate Gemini API
• Microsoft - Mentioned for Windows Copilot, described as inferior to OpenAI's offerings
• Meta (Facebook) - Discussed regarding their AI integration in WhatsApp and the "blue app" (Facebook)
• WhatsApp - Messaging platform where Meta AI was described as intrusive
• YC (Y Combinator) - Referenced for their internal work evaluating AI models

People:

• Zuck (Mark Zuckerberg) - Discussed as an example of a founder CEO willing to make radical strategic changes
• Sam (Altman) - Mentioned regarding his role as "CEO of compute" at OpenAI
• Pete Koomen - YC partner and former Google PM who wrote an essay critiquing Google's Gemini integration

Technology Concepts:

• Gemini Pro & Gemini 2.5 Pro - Google's AI models discussed throughout
• TPUs (Tensor Processing Units) - Google's custom AI hardware described as their "dragons"
• Large context windows - Capability enabled by advanced hardware like TPUs
• System prompt vs. user prompt - Distinction in AI interfaces, with system prompts typically controlled by companies
• Vibe coding - Interactive coding used in Pete Koomen's blog post
• AI-first blogging platform - Startup idea proposed at the end of the segment

Cultural References:

• Game of Thrones - Used as an analogy where Google is compared to Daenerys Targaryen with TPUs as "dragons"
• Innovator's dilemma - Business concept referenced regarding Google's challenge in transitioning from search to AI

Timestamp: [17:42-25:08]

The conversation begins with a reflection on the "tech-enabled services" boom of the 2010s, a category of startups that aimed to combine software with operational services.

The speakers note that this approach emerged from an influential concept in the industry:

They explain the core premise behind these businesses:

This approach promised to capture more value than pure software companies:

Timestamp: [25:15-25:24]

One of the speakers shares their firsthand experience running Triple Bite, which exemplified the tech-enabled services approach in the recruiting space.

The operational complexity went beyond just software:

The speaker notes that while Triple Bite achieved impressive growth by recruiting industry standards, it fell short compared to pure software startups:

Timestamp: [25:24-27:29]

The conversation identifies the central problem that doomed many tech-enabled services companies: poor gross margins.

The speakers explain how this issue created a destructive cycle:

However, this approach wasn't sustainable in the long run:

The speakers reference other examples that encountered similar challenges:

Timestamp: [27:29-28:13]

The conversation references how one entrepreneur learned from the mistakes of tech-enabled services.

This experience led to a broader industry realization:

Timestamp: [28:13-28:43]

One speaker explains that the problems with low-margin businesses extend beyond just financial considerations to how they affect a company's focus and operations.

Drawing from personal experience at Triple Bite:

This operational complexity distracted from the core challenge:

The speaker contrasts this with the advantages of high-margin businesses:

This focus issue explains why many full-stack startups hit growth ceilings:

Timestamp: [28:43-29:43]

The conversation briefly touches on WeWork as an extreme example of the full-stack approach with poor margins.

The speakers note WeWork's creative but ultimately unsuccessful attempt to position itself as a technology company:

This reference to WeWork's controversial financial metric underscores how some companies tried to obscure their fundamental margin problems through financial engineering.

Timestamp: [29:43-30:01]

The conversation takes an optimistic turn as the speakers suggest that AI is creating a second chance for the full-stack model.

The key difference is how AI changes the operational economics:

Timestamp: [30:01-30:25]

The discussion uses legal services as a case study for how AI is transforming what's possible in full-stack businesses.

The speakers highlight a current YC company in this space:

They predict how this approach will eventually evolve:

Timestamp: [30:25-31:09]

The speakers reflect on how the current wave of AI is transforming knowledge work in a way that enables new full-stack approaches.

They note that AI capabilities continue to advance:

Timestamp: [31:09-31:35]

The discussion acknowledges that virtual assistant services have been attempted many times before, but with limited success.

However, these previous attempts fell short:

This observation sets up the contrast with what might be possible now with AI-powered assistants that have greater capabilities.

Timestamp: [31:35-31:48]

The conversation concludes by referencing Pete's blog post about system prompts, which leads to a broader discussion about the immaturity of AI tooling and infrastructure.

The speakers highlight that this creates significant startup opportunities:

This insight suggests that beyond full-stack applications, there's a parallel opportunity in building the tools that will enable those applications.

Timestamp: [31:48-32:25]

• The tech-enabled services wave of the 2010s failed primarily due to poor gross margins that made scaling unsustainable
• Full-stack startups required continuous fundraising to offset operational costs, creating dependency on capital markets
• Low-margin businesses suffer not just financially but also from divided focus between operations and core product/distribution
• Operational complexity in full-stack companies created a ceiling on growth compared to pure software businesses
• AI now enables "Full-Stack 2.0" where agents can replace human operations while maintaining software-like margins
• Previous attempts at full-stack models (like Atrium in legal) were limited by the AI technology available at the time
• Current AI capabilities make knowledge work automation possible at a scale and quality that wasn't previously achievable
• Virtual assistant marketplaces have been attempted for 15+ years but never achieved significant success
• The AI infrastructure space remains underdeveloped, with substantial opportunities for tools around evaluation, deployment, and agent building
• Companies that enable users to customize AI behavior (like modifying system prompts) may have advantages in usability and adoption

Timestamp: [25:15-32:25]

Companies:

• Triple Bite - Tech-enabled recruiting company referenced as an example of full-stack approach
• Atrium - Justin Kan's full-stack law firm that didn't succeed with earlier AI technology
• Legora - Current YC-funded company building AI tools for lawyers with rapid growth
• WeWork - Referenced as an extreme example of a full-stack company with poor margins
• Zenefits (ZS) - Mentioned as relying too heavily on sales and customer success rather than software
• Uber - Mentioned as part of the first wave of tech-enabled services companies
• Lyft - Mentioned alongside Uber as part of the first wave
• Instacart - Mentioned alongside Uber and Lyft as part of the first wave
• Exec - Previous virtual assistant marketplace company

People:

• Balaji - Referenced for his influential blog post about full-stack startups
• Justin Kan - Founder of Atrium, the full-stack law firm
• Parker Conrad - Cited for his approach of having engineers do customer support to improve software quality
• Pete - YC partner whose blog post about system prompts was referenced

Business Concepts:

• Tech-enabled services - Business model combining software with operational services that was popular in the 2010s
• Full-stack startups - Companies that own the entire value chain rather than just providing software
• Gross margins - Identified as the critical factor in business sustainability
• Community-adjusted EBITDA - Creative financial metric used by WeWork
• SaaS (Software as a Service) - Contrasted with tech-enabled services as having better margins
• Annual run rate - Metric used to describe Triple Bite's growth ($20-24 million)
• RFS (Request for Startups) - YC's way of signaling promising startup opportunities

Technology Concepts:

• LLMs (Large Language Models) - AI technology enabling the new wave of full-stack companies
• Virtual assistants - AI-powered helpers that can potentially replace human operations
• System prompt vs. user prompt - Distinction in AI interfaces discussed in Pete's blog post
• Evals - Evaluations needed for AI systems to ensure quality and performance

Timestamp: [25:15-32:25]

The conversation begins with a reflection on how perceptions of machine learning operations (ML ops) startups have dramatically changed over the years.

This initial skepticism has been completely reversed by recent developments:

One of the speakers shares their frustration from that earlier period:

This revealed a fundamental market problem:

Timestamp: [32:31-33:45]

The speakers reflect on how the ML ops companies were directionally correct but simply too early for the market.

This observation highlights the critical importance of timing in startup success, where being too early can be just as problematic as being too late.

Timestamp: [33:45-33:56]

The conversation turns to success stories of companies that persisted through the challenging early days of ML infrastructure.

The speaker details Replicate's difficult journey:

Their persistence eventually paid off spectacularly:

Timestamp: [33:56-34:37]

Another example is shared of an ML infrastructure company that persisted through challenging early days until the market caught up.

The breakthrough came with a specific technological advance that created their market:

The speaker contrasts this with the earlier, less capable open source models:

Timestamp: [34:37-35:28]

The discussion turns to what lessons can be drawn from these success stories, highlighting the value of intrinsic motivation over market calculation.

One speaker suggests that the common thread is founders following their genuine interests:

This approach sometimes leads to being perfectly positioned when markets suddenly emerge:

Timestamp: [35:28-36:04]

The conversation continues with additional examples of companies that navigated the early ML landscape through pivots or persistence.

Another compelling example is shared in more detail:

Their motivation was purely intellectual:

Like the other examples, their technology wasn't immediately successful:

The founders persisted despite the lack of recognition:

Timestamp: [36:04-37:04]

The speakers draw a parallel between these persistence stories and one of the key figures behind the current AI revolution.

This observation highlights how some of the most transformative technological developments come from deep, sustained intellectual curiosity rather than market-driven development.

Timestamp: [37:04-37:16]

The conversation pivots to how traditional startup advice may no longer be applicable in the AI era.

The speaker explains the historical context for this advice:

This philosophy became formalized in influential startup methodologies:

Timestamp: [37:16-38:06]

The speaker proposes that the AI revolution requires a fundamentally different approach to startups.

Another speaker elaborates on why this approach is particularly effective now:

Timestamp: [38:06-38:49]

The conversation turns to how even successful tech companies are slow to adopt and transform with AI.

The speaker expresses surprise at this slow adoption:

Where AI is being used, it's often in limited, personal capacities rather than strategically:

The disconnect between technological capability and adoption is striking:

Timestamp: [38:49-39:43]

The conversation concludes with an anecdote about AI researchers' surprise at the slow adoption of their revolutionary technology.

The reality was quite different:

This disconnect between technological capacity and actual adoption highlights the opportunity space for founders who can bridge this gap.

Timestamp: [39:43-40:15]

The host wraps up with an optimistic summary of the discussion.

This closing statement encapsulates the core message of the conversation: that we're in an unprecedented period of opportunity for entrepreneurs willing to explore the frontiers of AI technology.

Timestamp: [40:15-40:34]

• ML ops companies from 2019-2020 faced a market timing problem - they were building tools for ML applications that weren't yet viable
• Founders who persisted through the challenging early days of ML infrastructure (like Replicate and Ollama) were eventually rewarded when the market caught up
• Many successful AI infrastructure companies were founded by people following intellectual curiosity rather than market calculations
• The release of specific models (like image diffusion models for Replicate or Llama for Ollama) created tipping points that suddenly validated previously struggling businesses
• Traditional startup advice like "lean startup" and "fail fast" may be less relevant in the AI era than an exploration-based approach
• Even with 100-1000 person tech companies, AI adoption remains surprisingly low, creating opportunities for new startups
• AI researchers and developers are often surprised by how slowly their breakthrough technologies are being adopted in the mainstream
• The current environment offers unprecedented opportunities for founders to create startups around capabilities that didn't exist even a year ago
• Following curiosity and technological exploration is potentially more effective than market-driven customer discovery in the AI era
• The gap between what's technically possible with AI and what most businesses are actually doing represents a massive opportunity space

Timestamp: [32:31-40:34]

Companies:

• Replicate - ML infrastructure company from Winter '20 that persisted through difficult times until image diffusion models created explosive growth
• Ollama - Company that built tools for deploying open source models locally, which took off after Llama's release
• Windsor - Mentioned as having pivoted from ML ops to code generation
• Deepgram - Speech-to-text company founded by physics PhDs that worked on the technology for years before voice agents created demand
• Cursor - AI coding tool mentioned as being newly adopted by some forward-thinking engineers

People:

• Varun - Founder of Windsor who pivoted from ML ops to code generation
• Tiên - Mentioned as participating in a college tour with one of the speakers
• PG (Paul Graham) - Referenced for his advice about "living at the edge of the future"
• Bob McGrew - Former Chief Research Officer at OpenAI who was surprised by the slow adoption of AI technology
• Ilya Sutskever - Co-founder of OpenAI mentioned as having followed his curiosity for a long time, leading to breakthroughs

Organizations:

• YC (Y Combinator) - Startup accelerator where the speakers work and observe AI startup trends
• Hugging Face - Repository for open source models mentioned as hosting earlier, less capable models like BERT

Technology Concepts:

• ML ops (Machine Learning Operations) - Tools and infrastructure for deploying and managing machine learning models
• Image diffusion models - AI models for generating images that created a breakthrough moment for Replicate
• BERT models - Earlier generation of language models that weren't widely adopted due to limited capabilities
• Llama - Open source large language model whose release created a market for Ollama
• Speech-to-text/Text-to-speech - Technology developed by Deepgram that became essential for voice agents
• Evals - Evaluation methodologies for assessing AI model performance

Business Concepts:

• The Lean Startup - Business methodology focused on testing ideas before building products, described as potentially outdated for the AI era
• Fail fast - Startup philosophy of quickly abandoning underperforming ideas, contrasted with the persistence needed in AI infrastructure
• Customer discovery - Process of validating market needs before building products, described as less relevant in the current AI landscape
• Skunkworks project - Internal innovation initiative, noted as absent in many established tech companies

Timestamp: [32:31-40:34]