Introduction
I've witnessed firsthand the incredible potential of artificial intelligence to transform industries and create massive value. But I've also seen the unique challenges and complexities that founders face when building a company around this still-nascent technology.
In this post, I want to share some key insights and lessons learned on what it takes to successfully build an AI startup from the ground up. We'll explore how AI startups differ from traditional tech companies, the critical ingredients for success, and the landmines to avoid. Whether you're an aspiring entrepreneur, a business leader, or an investor intrigued by AI, I hope this serves as a practical guide. Let's dive in.
Defining an AI Startup
First, let's clarify what we mean by an "AI startup." At the most basic level, it's a company whose core product or service is fundamentally enabled by artificial intelligence technologies like machine learning, natural language processing, computer vision etc. The startup's value proposition and competitive advantage are inextricably tied to the performance of its AI models and systems.
This is distinct from a traditional software company that may sprinkle in some AI features or use machine learning in the backend to optimize certain functions. In an AI startup, the AI is the make-or-break factor that drives the product experience and business model. Remove the AI and you no longer have a viable offering.
Some prominent categories of AI startups include:
Horizontal AI platforms: Companies building foundational AI tools and infrastructure for developers and enterprises (e.g. Databricks, Dataiku, H2O.ai)
Industry vertical AI: Startups leveraging AI to disrupt specific industry domains like healthcare, finance, retail, manufacturing (e.g. Flatiron Health, Darktrace, Noodle.ai)
AI-first products: Consumer and business applications where AI drives the core user experience and value proposition (e.g. Grammarly, Glean, Replika)
Autonomous systems: Companies developing AI-powered robots, drones, and vehicles for industrial and commercial use (e.g. Nuro, Saildrone, Skydio)
AI processing hardware: Startups building specialized chips and hardware optimized for machine learning workloads (e.g. Cerebras, Graphcore, SambaNova)
Of course, many startups span multiple categories or defy clean labels - the boundaries blur as AI permeates every domain. But the unifying thread is that AI is at the beating heart of the business, not just a nice-to-have.
The Technical AI Challenge
The defining characteristic and core challenge of building an AI startup is that you are grappling with cutting-edge, rapidly evolving technologies that have not yet reached the plug-and-play simplicity of conventional software. Simply put, AI is really hard.
Developing robust, production-grade AI systems requires a level of technical sophistication and specialized talent that most traditional software shops lack. From data ingestion pipelines to model training infrastructure to inference optimization, the toolchain is complex and bespoke. An AI startup's success hinges on pushing the boundaries of algorithms, systems design, and computing hardware.
This has a few key implications
Outsized Dependence on Elite AI Talent
AI startups live and die by their ability to attract and retain true experts in machine learning, data engineering, and their specific domain. When a handful of researchers and engineers are your main assets and speed to innovation is paramount, you need serious AI firepower, not just a few good generalist coders.
This scarcity of elite AI talent has made hiring extremely competitive and expensive, with PhD-level machine learning engineers commanding $400k+ in Silicon Valley. Big Tech vacuums up much of this talent pool with lavish compensation packages. AI startups that can't afford those salaries need to get creative, selling candidates on their mission, culture, and upside potential.
But technical recruiting alone isn't enough, AI expertise needs to permeate the entire organization. PMs must grasp the capabilities and limitations of the models to inform the product roadmap. Designers need to understand concepts like few-shot learning and prompt engineering to craft intuitive AI interfaces. Sales and customer success teams should be fluent in AI concepts to educate buyers and troubleshoot issues. Everyone is an "AI person" to some degree.
Data as a Defining Asset
We've all heard the cliche that "data is the new oil." For AI startups, it's the literal truth. Models are only as good as the data they're trained on. Access to unique, proprietary datasets is often the key competitive moat in a world where powerful algorithms and model architectures are increasingly open-sourced.
AI startups must have a coherent data acquisition strategy from the jump. This could involve bootstrapping initial models with public datasets, making inroads in a specific industry to access domain-specific data, or finding clever ways to generate synthetic data. As they scale, AI startups must treat their ever-expanding corpus of customer data as the crown jewel asset it is.
Some AI companies make their data the whole ballgame. Scale.ai created a "data as a service" model where they provide human-annotated datasets to other companies training computer vision models. Gong and Cresta use the conversation data ingested by their tools to continuously retrain models that benefit all their customers.
Ultimately, AI startups must cultivate a "data mindset" on par with their algorithmic one. They should always be asking: what unique data assets can we assemble? How can our product become a data flytrap where usage enriches the models for all? What emerging tools and markets can grow our data corpus?
The need to protect this data is also paramount, not just for privacy and security but to prevent competitors from freeriding. Expect data licensing and rights management to become a key part of the AI startup playbook.
Tooling and Infrastructure Choices
Because AI/ML tooling is still in its adolescence, AI startups must think carefully about their technical stack and whether to build or buy critical components. Should you go all-in on cloud AI platforms from the big 3 (AWS, Azure, GCP)? Use managed services for parts of the pipeline but build your own model training and serving layers? Eschew the cloud entirely and roll your own infra?
Arguments can be made for various approaches depending on the startup's talent mix, performance needs, and appetite for vendor lock-in. But the days of doing everything from scratch are fading. As the ecosystem of AI/ML platforms, frameworks, and dev tools matures, startups are increasingly "standing on the shoulders of giants" vs. reinventing the wheel.
Some concrete examples:
Using a feature store like Tecton or Feast to manage feature pipelines vs. building in-house
Leveraging Weights & Biases for ML experiment tracking rather than a homegrown system
Deploying models on serverless inference platforms like AWS Sagemaker or Algorithmia instead of bespoke infra
Instrumenting ML monitoring tools like Fiddler or Arthur to detect model drift and data quality issues
Adopting open source frameworks like Hugging Face's transformers or Ludwig for flexible model building
The key is to be judicious about where you invest in-house development based on your secret sauce and strategic roadmap. But even then, don't be afraid to swap out components as better tooling emerges, technical debt accrues fast in AI startups.
Finding Product-Market Fit
Amidst the technical heavy-lifting, it can be easy for AI startups to lose sight of the fundamentals of product and business strategy. I've seen many teams so enamored with their models and algorithms that they neglect the hard work of finding product-market fit.
In the early stages, it's critical to resist the urge to chase the latest shiny AI achievement ("we used GPT-3 to power a workout app!") and instead relentlessly focus on what actual customer problems you can uniquely solve. Great AI is useless if there's no real market demand for it.
Some key questions to pressure-test product-market fit:
What specific user pain points or inefficiencies does our AI uniquely address?
How significant are the time/cost/quality gains from our AI vs. the status quo approach?
What evidence do we have that customers value these gains enough to change their behavior and pay for them?
How large is the addressable market for this use case, and how much of it can we realistically capture?
What are the user trust and explainability requirements and how well does our AI meet them?
How defensible is our product from the onslaught of other AI solutions targeting this space?
The answers to these questions should be crystal clear before investing heavily in scaling the AI product. Many AI startups waste time chasing marginal improvements in technical performance metrics that customers don't actually care about.
Some proven strategies for finding product-market fit:
Build a deep understanding of a specific industry and the daily pains of a particular type of user that AI can alleviate
Leverage domain expertise and customer relationships of a founding team member to identify unsolved problems
Launch a hyper-focused, opinionated initial product that may not have much AI at all, in order to validate demand and collect data
Pursue paid pilots with progressive early adopters to co-develop AI solutions that can then be packaged for other customers
The connective tissue is putting in the customer development legwork early, getting real skin in the game from partners, and only then layering on the AI secret sauce. Otherwise you risk being a hammer in search of a nail.
Go-to-Market and Commercialization
With a killer AI product taking shape, it's time to shift gears to commercialization and growth. Go-to-market for AI startups is widely varied and depends heavily on the specific vertical and business model. But some common patterns and learnings have emerged:
AI platforms and infrastructure startups tend to rely on a technical, bottoms-up adoption model. They'll often pursue an open source-led strategy (ex. Hugging Face, Weights & Biases) to build awareness and usage among data scientists and ML engineers, then layer on commercial offerings. Field sales only kicks in for enterprise customer expansion.
Industry vertical AI solutions usually require a more targeted, top-down sales motion. Category education and change management is key since you're often introducing AI to stodgy, slow-moving buyers. Co-selling and product integration with existing software vendors in the vertical can grease the wheels.
AI-first end user products can pursue traditional consumer and SMB marketing playbooks: viral invite flows, self-service onboarding, PLG funnels, etc. But even here, AI can necessitate a human touch. Copilot-style "AI assistant" products work well as a wedge but often need to ladder up to team/org-wide deals. I've seen startups deploy "AI trainers" to work closely with users on prompt engineering and creative best practices.
The common thread is that AI go-to-market often needs to be more hands-on than traditional SaaS. Customers need extra handholding to build trust in the technology, integrate models into their workflows, and drive org-wide behavior change. Evangelizing the AI capabilities and guiding users up the learning curve is key.
Some other AI-specific GTM lessons:
Invest in case studies and reference customers to demonstrate proven ROI and secure marquee logos
Develop service and training offerings to boost consumption and derive insights for product improvements
Explore risk-sharing deal structures like outcome-based pricing to give customers peace of mind
Build out explainable AI features and human oversight tools to address adoption blockers
Leverage partners and marketplaces (ex. AWS Marketplace, Snowflake Partner Connect) to extend reach
As much as possible, create a pricing model that scales with usage and value so your revenue potential grows automatically as customers reap more gains from your AI.
The Responsible AI Imperative
With the power of today's AI systems comes a heavy responsibility to deploy them safely and ethically. The reputational risks of getting this wrong are immense, and you don't have to look far for cautionary tales - facial recognition bias, discriminatory lending algorithms, social media filter bubbles eroding the fabric of democracy, etc.
So how do AI startups bake in the tenets of responsible AI development from day one? It's a complex topic that deserves its own dedicated post, but some key principles:
Establish a clear code of AI ethics early and make it a core part of the company culture
Appoint an internal review board and hire a dedicated responsible AI lead
Evaluate training datasets for bias and work to assemble diverse data corpuses
Instrument model monitoring, debugging, and auditing processes to catch fairness and safety issues
Develop model cards and transparent documentation to foster accountability
Set up escalation paths and human oversight for high-stakes decisions made by AI systems
Engage proactively with policymakers and academics to shape responsible AI guidelines for your domain
There's no simple playbook for responsible AI, and the considerations vary across use cases. But directionally, AI startups need dedicated roles and processes to assess these issues as part of the product development lifecycle.
It’s not just about risk mitigation, proactive responsible AI investment is increasingly a competitive advantage. Forrester predicts that customers will actively seek out vendors with robust AI governance and a reputation for ethical ML practices. Responsible AI is becoming table stakes.
Screening AI Startups
When it comes to technical aspects and intellectual property (IP), investors in AI startups will typically screen for the following key factors:
Core AI Technology and Differentiation
Investors want to see that the startup has developed a truly novel and differentiated AI technology that gives them a sustainable competitive advantage. This could be a proprietary model architecture, training technique, or data asset that enables superior performance on a specific task or domain.
Some key questions investors will probe:
What is the core AI innovation powering the startup's product?
How does it compare to the state of the art in academia and industry?
Is the performance lift significant enough to be a game-changer for customers?
How defensible is the technology from replication by larger players?
Expect investors to dive deep into the technical details and push on benchmarking results. Founders should be prepared to clearly articulate their secret sauce without relying on buzzwords.
Data Moats and Network Effects
In a world where many cutting-edge AI models are open-sourced, proprietary data is often the key differentiator. Investors will want to understand the startup's data acquisition strategy and how it creates a sustainable competitive advantage.
Some key questions investors will ask:
What unique datasets does the startup have access to?
How large and diverse are the datasets compared to public benchmarks?
Does usage of the product generate new data that improves model performance over time?
Are there strong network effects and switching costs that make the data asset more valuable with scale?
Startups that can demonstrate a compelling data flywheel - where each additional customer adds data that makes the product more accurate and attractive for the next customer - will have an advantage with investors.
Model Performance and Robustness
Of course, investors will want to see strong empirical evidence of the AI system's performance on real-world tasks. Beyond just accuracy metrics, they will also probe for model robustness, fairness, and safety.
Some key questions investors will investigate:
How does model performance compare to human baselines and industry benchmarks?
Has the system been rigorously tested on diverse and representative data slices?
What is the model's performance on edge cases and rare events?
How robust is the system to distributional shift and adversarial attacks?
What safeguards are in place to mitigate bias and ensure fairness?
Be prepared to show extensive testing results and engage in technical discussions around model evaluation. Investors with strong AI teams will conduct thorough technical diligence.
IP Strategy and Protection
Intellectual property will be a key area of focus for investors, especially at later stages. They will want to understand the startup's IP strategy and level of protection around their core technology assets.
Some key questions investors will diligence:
What types of IP has the startup filed (patents, copyrights, trade secrets)?
How strong and defensible are the IP claims?
Is the startup's IP properly assigned and unencumbered by previous employers/universities?
What is the freedom to operate vs. existing patents in the space?
Does the startup have proper invention assignment and non-disclosure agreements with employees and contractors?
While software patents can be tricky, filing key method patents around novel techniques is still advised for AI startups. Investors will often bring in outside patent counsel to assess the strength of a startup's IP position.
Technical Team and Research Credentials
Ultimately, an AI startup's core assets are its people. Investors will closely evaluate the caliber and credibility of the startup's technical team and advisory board.
Some key factors investors will assess:
Do the founders and technical leadership have deep AI expertise and industry experience?
How strong are the research credentials and publication records of the team?
Is there a diverse mix of machine learning, engineering, product, and domain experts?
Are there notable AI luminaries or industry leaders advising the company?
Can the startup attract top AI talent from industry and academia?
Investors will often backchannel with a startup's former colleagues and research partners to get an unvarnished view of the team's capabilities and culture. Strong technical brands and talent networks are a huge asset.
Model Explainability and Compliance
Increasingly, investors are also probing startups' readiness for AI regulation and compliance, particularly in heavily regulated industries like healthcare and finance. Model explainability and audit-ability are becoming table stakes.
Can the startup's AI system provide clear explanations for its decisions and outputs?
Does the system have robust audit trails and versioning to enable compliance review?
Has the startup engaged with relevant regulatory bodies and developed compliance roadmaps?
Are there clear governance structures and ethical review processes in place?
Has the startup conducted any third-party audits or risk assessments?
Startups that can credibly point to responsible AI development practices and proactive regulatory engagement will have an advantage in diligence.
Technical Scalability and Deployment
Finally, investors will kick the tires on the startup's technical architecture and deployment capabilities. They want to see that the system can scale efficiently and be integrated seamlessly into customer environments.
Is the startup's tech stack and infrastructure built to scale?
Have they stress-tested the system under high-volume, high-concurrency loads?
What is the inference latency and cost at scale?
Does the startup have battle-tested MLOps and deployment pipelines?
What is involved in integrating the AI system into customer environments?
How flexible and modular are the API and SDK layers?
Investors will often conduct technical scalability assessments and speak with customer references to validate the startup's deployment chops.
The bar for technical diligence on AI startups is high, and only getting higher as the industry matures. Founders need to invest heavily in not only developing cutting-edge technology, but building a world-class technical organization and IP portfolio behind it. Those that can marry technical excellence with business rigor and regulatory savvy will be best positioned to pass investor scrutiny and scale to industry leadership.
Tech moat +World Class Team+ Scalability
In summary, when screening AI startups, investors are looking for companies that can demonstrate:
Novel, defensible, and high-performing core AI technology
Proprietary datasets and strong data network effects
Robust model performance, fairness, and safety
Comprehensive IP protection and freedom to operate
World-class technical talent and research credentials
Rigorous model explainability, auditability, and regulatory compliance
Scalable, flexible, and developer-friendly deployment capabilities
The hypothetical "less attractive" responses highlight common gaps or areas of immaturity that can raise red flags for investors, such as:
Unclear differentiation or benchmarking against state-of-the-art
Lack of defensibility against larger players
Small or undifferentiated datasets without network effects
Insufficient testing for robustness, fairness, and safety
Weak or unprotected IP with limited freedom to operate
Junior, unproven technical teams without notable advisors
Lack of focus on explainability, compliance, and responsible AI
Unproven scalability and manual, ad-hoc deployment processes
In contrast, the "most attractive" responses demonstrate the key strengths that investors prize in top-tier AI startups, including:
Significant outperformance versus benchmarks and baselines
Hard-to-replicate technical moats and data assets
Rigorous testing and risk mitigation for model edge cases
Comprehensive, well-executed IP strategy with robust protection
Accomplished and diverse team with relevant domain expertise
Proactive engagement with regulators and 3rd party auditors
Scalable infra, battle-tested MLOps, and flexible integration
A successful AI startup like Anthropic would be able to credibly tout these advantages with specific, quantified evidence. Their responses showcase:
40-50% lift in performance from novel architectures and training
Massive proprietary datasets and powerful flywheel effects
Fairness and safety validation against the highest industry standards
15+ patents and multi-layered IP protection vetted by top counsel
World-renowned research talent and luminary advisors/investors
Robust explainability and compliance with proactive regulatory engagement
Stress-tested, low-latency serving infra and seamless deployment
Of course, every AI startup is unique and the relative importance of these factors can vary based on the specific vertical, business model, and stage of the company. But in general, the ability to nail these core aspects of the technology, team, and GTM is what separates the most fundable AI startups from the rest of the pack.
Investors are essentially looking for AI startups that have an "unfair" technical advantage, built by a world-class team, that's defensible over the long run as they scale. Those that can pass the sniff test on these key dimensions with flying colors will have their pick of term sheets.
Even for those earlier in their journey, understanding these investor evaluation criteria can help prioritize where to focus precious startup cycles. Founders should constantly pressure test their platform against this rubric and proactively de-risk the business by filling in gaps.
At the end of the day, building a successful AI startup is just really, really hard, it requires a level of technical sophistication, operational intensity, and regulatory savvy that few teams can pull off. But for those that do, the opportunity to shape the future and build an iconic company has never been greater. I have no doubt the next generation of AI giants will emerge from this moment. The question is who wants it more.