What I learned from looking at 400 AI-based Startups backed by YCombinator
I analysed 400 YC backed startups, here's what I found
Be careful what you let become critical to you. Try to get yourself into situations where the most urgent problems are ones you want to think about. - Paul Graham
In search of good use cases for AI…
If you struggle with “What should I build with AI that has higher chances of success?”
This post will help you get to an answer.
YCombinator's (YC) track record in identifying and nurturing successful startups is unparalleled in the tech industry. Their selection process has consistently surfaced companies that go on to reshape entire sectors, making their portfolio a valuable indicator of emerging trends and technologies.
Given the transformative potential of artificial intelligence, YC’s track record and my curiosity to understanding what type of AI companies are attracting invesments, I decided to analyse YC backed AI-focused startups.
I was looking for answers to questions like which sectors are seeing the most AI innovation? What types of AI applications are attracting investment? What backgrounds do successful AI founders have?
To answer these questions, I conducted an extensive analysis of 417 AI companies from YC's 2023 and 2024 cohorts.
This study aims to provide insights into:
The hottest industries and sectors for AI startups
Areas ripe for AI disruption
AI in emerging technologies like blockchain and quantum computing
Companies working in AI Safety, Accessibility, Explainability
Common traits among YC-backed AI founders
How to find what you should build with AI using the above insights
For those who don’t know, YCombinator is a leading startup accelerator that provides seed funding, mentorship, and resources to help early-stage startups succeed.
How it works at YC:
YCombinator invests $500,000 in each startup accepted into their 3-month program, in exchange for a small equity stake.
The program aims to help startups dramatically improve their product and user growth, as well as increase their options for raising additional funding.
Data
I collected the data from YC’s Startup directory, filtered it for 2023 and 2024 Summer and Winter batches.
Source: YCombinator directory
Cleaned the data, extracted the tags they have and rechecked it from the description of the company to capture their main category.
Overview of the analysis of 417 AI-focused startups
I got 417 companies in my data with their description, urls, tags / categories, active founders, and founders’ bio.
If you need this data for yourself, get your copy from here.
While looking at a subset of these companies, I have found many exceptional use cases of AI. In fact, part of the data collection process was done using Gumloop (backed by YC), I love using Gumloop over Zapier, I find myself using it more often than I had imagined.
Coming to the analysis…
⚠️ Disclaimer: Some parts of this report is generated with the help of LLMs and the numbers are approximate.
Where are the current hotspots for AI-driven innovation? 🔝
Most common industry intersections with AI:
Healthcare/Biotech: 45 companies (10.8%)
Example: Elythea (preventing maternal mortality using ML)
Fintech: 38 companies (9.1%)
Example: Arcimus (AI-powered insurance premium audits)
Developer Tools: 37 companies (8.9%)
Example: Sudocode (AI for developer tools)
Sales/Marketing: 34 companies (8.2%)
Example: MicaAI (streamlining sales processes)
Education: 18 companies (4.3%)
Example: Studdy (AI tutor for students)
B2B vs B2C
B2B: Approximately 338 companies (81.1%):
Sample companies:GigaML: Helps enterprises build and deploy large language models (LLMs) on-premise.
Constructable: AI copilot for construction teams.
AiSDR: Leverages AI to streamline sales processes for B2B companies.
Corgea: Uses AI to fix vulnerable code, enhancing enterprise data security.
B2C Companies (18.9% of the portfolio):
Sample Companies:Rex: AI-powered workout and nutrition coach.
PocketPod: Offers AI-generated podcasts tailored to user interests.
Shortbread: Provides a 'Netflix for comics' service.
Roame: A travel platform leveraging AI for trip planning and booking.
Key Takeaways:
B2B Dominance: 81.1% of YC-backed AI startups focus on enterprise solutions, indicating stronger investor confidence in business-oriented AI applications.
Untapped B2C Potential: With only 18.9% of startups targeting consumers, there may be significant opportunities for innovative consumer-facing AI products.
Technical Expertise Drives Focus: The prevalence of founders with strong technical backgrounds (74.8%) likely influences the B2B emphasis and the types of AI problems being addressed.
AI infrastructure vs AI applications:
AI Infrastructure Companies - 62 (14.9%):
Epsilla: Provides a 10x faster open-source vector database.
GigaML: Helps enterprises build and deploy large language models (LLMs) on-premise.
AI Application Companies - 355 (85.1%):
Corgea: Uses AI to quickly fix vulnerable code, enhancing enterprise data security.
Elythea: Applies machine learning to prevent maternal mortality.
Key takeaways:
Application-Heavy Focus: With 85.1% of companies developing AI applications versus 14.9% working on infrastructure, there's a clear emphasis on practical, industry-specific AI solutions.
Potential Infrastructure Gap: The relatively low number of infrastructure-focused startups might indicate an opportunity for more foundational AI tools and platforms.
Specialization Trend: AI applications tend to target specific industry problems, while infrastructure companies aim to provide more generalized tools for AI development and deployment.
AI-driven automation vs. AI-assisted human work
Automation is the biggest use case of AI across industries in the application layer. While some automations are completely AI driven, others are just assisted by AI but are largely driven by Humans.
AI-driven automation - 129 companies (30.9%):
Ofone: Automates order-taking at fast food drive-thrus, streamlining the ordering process and reducing wait times.
Respaid: A modern B2B collection platform that automates the process of managing and tracking unpaid invoices.
RetailReady: Automates supply chain compliance, specializing in warehouse shipping solutions to improve logistics operations.
AI-assisted human work - 288 companies (69.1%):
Constructable: An AI copilot for construction teams, helping streamline projects and reduce losses due to bad data.
RadMateAI: Provides an AI copilot for radiologists to improve diagnostic accuracy and efficiency.
Agentive: Offers an AI-powered copilot for auditors, enhancing their efficiency and effectiveness through advanced technologies.
While these industries are thriving, others are lagging behind...
The Untapped Frontiers - Industries Ripe for AI Disruption
Rapid adoption:
Healthcare
Finance
Software Development
Sales/Marketing
Lagging:
Manufacturing (4 companies, 1%)
Agriculture (3 companies, 0.7%)
Energy (4 companies, 1%)
Retail (5 companies, 1.2%)
Sectors like manufacturing, agriculture, energy, and retail still present opportunities for first-movers in AI adoption.
Note that this is only representative of YC startups that follow a specific funding model, focus, domain expertise from YC staff and mentors which might not align with these industries.
As AI permeates various industries, certain technologies are leading the charge...
The Tech Trends Shaping AI's Future
Most prevalent AI technologies:
1. Generative AI: 78 companies (18.7%)
2. Machine Learning: 56 companies (13.4%)
3. Natural Language Processing (NLP): 47 companies (11.3%)
4. Computer Vision: 18 companies (4.3%)
Please note that there can be a lot of overlap here as a company mentioning AI may be working on all 3 technologies Gen AI, Machine Learning, NLP.
Open-source vs. proprietary:
Open-source: 18 companies (4.3%)
Proprietary: 399 companies (95.7%)
Example of open-source: FlowiseAI (open-source AI solutions)
Please note that this is only representative of YC’s portfolio. There are many companies coming out of open-source projects.
Edge AI vs. cloud-based AI:
Only 2 companies (0.5%) 🔻 explicitly mention edge AI, while the vast majority appear to be cloud-based solutions.
AI model efficiency and reducing computational resources:
Only 5 companies (1.2%) 🔻 explicitly mention focusing on AI model efficiency or reducing computational resources.
Real-time AI applications:
About 46 companies (11%) ✅ mention or imply working on real-time AI applications.
Example: Retell AI (real-time AI-powered voice agents)
Multi-modal AI:
Approximately 22 companies (5.3%) appear to be working on multi-modal AI solutions.
Key Takeaways 💡:
The Generative AI Revolution: With 18.7% of companies focusing on generative AI, we're witnessing a paradigm shift in AI capabilities. This trend suggests a future where AI not only analyzes but creates, potentially transforming industries from content creation to drug discovery.
The Cloud-Edge Disconnect: With only 0.5% of companies focusing on edge AI, there's a glaring gap between current AI development and the growing need for real-time, on-device AI processing. This disparity could be a blind spot in the industry, overlooking crucial applications in IoT, autonomous systems, and privacy-preserving AI.
As AI becomes more powerful, new challenges and opportunities emerge...
Potential in Ethical, Efficient, and Accessible AI
Out of 417 YC-backed AI startups, surprisingly few are tackling critical issues like data privacy, AI ethics, accessibility, and fairness. This section explores the small but crucial subset of companies working on these foundational problems, highlighting both the progress made and the vast opportunities that remain in creating more responsible, transparent, and inclusive AI systems.
Startups addressing data privacy and security concerns:
Approximately 18 companies (4.3%) 🔻 explicitly focus on data privacy and security.
Example: Corgea - uses AI to easily and quickly fix vulnerable code, enhancing enterprise data security and privacy.
Given increasing regulations, there's an opportunity for more AI startups to focus on data privacy and security.
Startups addressing Ethical AI and AI Safety
Only 5 companies (1.2%) 🔻 explicitly mention focusing on AI ethics or safety.
Example: Atla (building AI models with guardrails)
Startups making AI accessible to non-technical users
Approximately 28 companies (6.7%) 🔻 focus on making AI more accessible to non-technical users.
Example: Creo (building internal tools with AI without coding)
Startups working on explainable AI or AI transparency
Only 3 companies (0.7%) 🔻 explicitly mention working on explainable AI or AI transparency.
Examples:
Atla: Atla is focused on building text-generating AI models with guardrails. Their mission is to create AI assistants that are trustworthy and useful for various applications, particularly in legal contexts.
GuideLabs: Guide Labs develops interpretable foundation models, focusing on AI and machine learning.
Sizeless: Sizeless is a company focused on making machine learning reproducible and safe.
AI for sustainability or climate tech:
11 companies (2.6%) 🔻 focus on sustainability or climate tech.
Companies like AetherEnergy (AI platform for optimizing rooftop solar installations)
Startups addressing AI bias and fairness ⏬:
Only 3 companies (0.7%) 🔻 explicitly mention addressing AI bias and fairness.
AI for small businesses vs. enterprise solutions:
Small businesses: Approximately 37 companies (8.9%) 🔻
Enterprise solutions: Approximately 295 companies (70.7%)
Example of small business focus: HostAI (AI-powered operating system for vacation rentals)
Key Takeaways 💡:
The Ethics Gap: With only 1.2% of startups focusing on AI ethics and safety, we're facing a critical imbalance between AI's rapid advancement and its responsible development. This stark underrepresentation could lead to significant societal and regulatory challenges as AI becomes more pervasive in decision-making processes.
The Transparency Paradox: Despite the growing demand for AI accountability, a mere 0.7% of startups are tackling explainable AI. This gap threatens to create a "black box" problem at scale, potentially eroding trust in AI systems and hindering their adoption in critical sectors like healthcare and finance.
The Democratization Dilemma: While 6.7% of startups are working to make AI accessible to non-technical users, this figure suggests a missed opportunity to truly democratize AI. The concentration of AI power in the hands of a technical elite could exacerbate existing digital divides and limit AI's potential to drive inclusive innovation across various sectors.
AI in emerging technologies 💎:
At the bleeding edge of innovation, a select few startups are pioneering the integration of AI with revolutionary technologies:
Quantum Computing: 2 companies (0.5%)
Blockchain: 3 companies (0.7%)
Trailblazers in this space include:
ConductorQuantum: Harnessing quantum computing to potentially solve complex problems beyond the reach of classical AI.
Cedalio: Merging blockchain with AI for enhanced data integrity and decentralized intelligence.
Key Takeaways:
Untapped Potential: The scarcity of startups in these fields (1.2% combined) signals vast unexplored territories for AI applications.
Exponential Impact: Successfully combining AI with quantum computing or blockchain could lead to breakthroughs in cryptography, drug discovery, and financial systems.
High-Risk, High-Reward: While these ventures face significant technical challenges, they represent the vanguard of computational advancement, potentially reshaping the entire AI landscape.
Typical background and skills of a YC-backed Founder
This analysis will help paint a picture of the typical YC-backed AI startup founder.
Technical Expertise:
A significant (> 75%) majority of founders have strong technical backgrounds like Computer Science, Software Engineering, Artificial Intelligence/Machine Learning and Data Science.
Technical expertise, especially in AI and related fields, appears to be highly valued by YC.
Educational Background:
Around 20% of the companies have founders have degrees from prestigious institutions, as mentioned in their profiles:
Stanford University
MIT (Massachusetts Institute of Technology)
Harvard University
UC Berkeley
Other top-tier universities
A lot of founders have a strong educational credentials from renowned institutions, particularly those with strong computer science and engineering programs.
Prior Work Experience:
Many (~25%) founders have experience at leading tech companies like:
Google
Facebook (Meta)
Amazon
Microsoft
Apple
LinkedIn
Experience at top tech companies seems to be a strong positive factor for YC funding.
Entrepreneurial Experience:
A notable (~15%) number of founders have prior startup experience:
Serial entrepreneurs
Previously founded or co-founded other startups
Example: Surbhi Sarna, founder of multiple companies including Olio Labs, "previously founded nVision Medical and sold it to Boston Scientific."
YC values founders with prior entrepreneurial experience, especially those who have had successful exits.
Please note that even if you did not attend top schools or worked at leading tech organisations, you still stand out by showcasing exceptional work.
For example Jaspar Carmichael jack (founder of Artisan) doesn’t have big titles in his profile but only exceptional work to showcase.
Academic Research:
Some (~8%) founders come from academic research backgrounds:
PhDs in relevant fields
Postdoctoral researchers
University professors
Example: Roman Engeler from Atla "holds a PhD in AI and has been involved in multiple machine learning projects."
Strong research backgrounds, especially in AI and ML, are valued by YC.
Diverse Skill Sets in Co-Founding Teams:
Many (45%) startups have co-founding teams with complementary skills:
Technical founder + Business/Operations founder
AI expert + Domain expert
Example: Arcimus has "Hussein Syed: Experienced in AI and software development" and "Omar Dadabhoy: Background in finance and insurance."
YC seems to favor founding teams that combine technical expertise with business acumen or domain knowledge.
Industry Disruptors:
Many (~24%) founders have backgrounds that position them to disrupt traditional industries:
Ex-employees of large corporations in the industry they're now disrupting
Individuals with unique insights into industry pain points
Example: Tom Blomfield, involved in several YC companies, is the "Former CEO of Monzo, co-founder of GoCardless."
YC values founders who can bring fresh perspectives and disruptive ideas to established industries.
How to find what you should build with AI
Paul Graham says that great work is a mix of three key elements: natural aptitude, deep interest, and scope to do great work. Let's apply this framework to finding your ideal AI startup focus:
Natural Aptitude: Assess your strengths. If you have a technical background, you're in good company with 74.8% of YC AI founders. If not, consider partnering with a technical co-founder to complement your skills. Your natural talents, whether technical or non-technical, will be the foundation of your startup's success.
Deep Interest: Identify which industry, sector, or problem appeals most to you. Your passion will fuel your persistence through challenges. Look at high-potential sectors like Healthcare/Biotech (10.8%), Fintech (9.1%), and Developer Tools (8.9%), or explore underserved areas like Manufacturing (1%) or Agriculture (0.7%). Your genuine interest in the problem you're solving will be crucial for long-term motivation.
Scope to Do Great Work: This is where market analysis comes in. Consider whether to focus on the dominant B2B market (81.1%) or the less saturated B2C space (18.9%). Explore critical gaps like data privacy (4.3%), AI ethics (1.2%), or explainable AI (0.7%). For those drawn to cutting-edge technology, quantum computing (0.5%) and blockchain (0.7%) offer high-risk, high-reward opportunities. The key is to identify areas where AI can make a significant impact and where there's room for innovative solutions.
Conclusion
So, if you’re an aspiring AI Founder or a builder in general, I’d suggest the following:
Focus on B2B: With 81.1% of YC-backed AI startups targeting businesses, consider enterprise solutions for higher chances of funding and success.
Explore Underserved Sectors: While healthcare/biotech (10.8%), fintech (9.1%), and developer tools (8.9%) dominate, look for opportunities in neglected areas like manufacturing (1%) or agriculture (0.7%).
Prioritize Technical Expertise: Ensure your founding team includes strong technical talent, as 74.8% of YC-backed AI companies have at least one founder with a robust technical background.
Capitalize on Generative AI: With 18.7% of startups in this space, generative AI is hot. However, consider how you can apply it innovatively to stand out.
Address Ethical Concerns: Only 1.2% of startups focus on ethical AI. This gap represents a significant opportunity for forward-thinking founders.
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