AI-first start-ups must prioritize solving customer problems over the novelty of technology.
AI-first start-ups must establish defensible advantages from the beginning, such as proprietary data, deep domain knowledge, client-side workflow integration or compliance readiness.
Focused companies scale faster than scattered competitors.
세계경제포럼, 2025년 7월 25일 게시
Anil Gupta
Michael Dingman Chair in Strategy, Globalization and Entrepreneurship, University of Maryland
Wang Haiyan
Managing Partner, China India Institute
This is the age of artificial intelligence (AI). By August 2024, 39% of working-age American adults were using generative AI (GenAI) weekly. And, according to OpenAI, ChatGPT had 400 million weekly users in February 2025, on track to hit the 1 billion mark by year-end.
Unsurprisingly, this is also the age of AI-first start-ups – companies with core business models built around AI. Silicon Valley’s Y Combinator – the world’s most influential start-up accelerator – explicitly prioritizes AI agent companies, which made up 46% of its Spring 2025 cohort.
AI is a double-edged sword. AI-first start-ups can benefit from scalability, strong margins and data-driven moats but they may also face steep challenges: data access, rapid technical advances and brutal competition for specialized talent.
Here are some of the key principles for building a successful AI-first venture.
Bias for customer pain over cool technology
Most AI ventures are founded by engineers who can get swept up in the novelty of technology and overlook real customer needs.
Humane Inc. is a cautionary tale. Founded by two former Apple employees, the company launched the AI Pin in 2023: a lapel-worn assistant that could take photos, search the web and send messages. Despite raising over $200 million, the product failed.
“Without focus, you risk spreading yourself too thin and taking on too much complexity too soon.”
Some critics said that few saw the need for a redundant device in a smartphone-saturated world. In early 2025, Humane was sold for a fraction of its peak valuation.
Grammarly, by contrast, focused squarely on solving a widespread pain point. Founded in 2009, it addressed struggles with grammar and clarity. Instead of building the most sophisticated natural language processing (NLP) model, Grammarly prioritized real-time correction, contextual suggestions and clear explanations.
By May 2025, Grammarly was generating $700 million in annualized revenues and had raised another $1 billion in venture capital.
AI products also require continuous iteration. Because model outputs can be probabilistic, edge cases and hallucinations are common. Start-ups that refine their approach based on user feedback can dramatically improve performance.
Grammarly’s feedback loop – tracking edits and user corrections – is now a core asset in refining its models.
Build a defensible moat early
Even if you’re the first to solve a compelling need, brutal competition is a given with AI, driving the need to build a defensible moat from the get-go.
This can take many forms: proprietary data, deep domain expertise, better models and integration into customer workflows.
Health care technology company Abridge AI, for instance, uses GenAI to transcribe and summarize doctor-patient conversations into clinical notes formatted for electronic health records.
It has built several layers of defensibility: deep knowledge of clinical settings, proprietary NLP models tuned to medical terminology, integration into EPIC (the dominant EHR platform) and seamless embedding into clinical workflows.
Today, Abridge serves over 100 health systems. Crucially, scale provides Abridge with a flywheel to continually improve its models through real-world usage.
Start-ups in regulated domains, such as healthcare, finance or education, must also consider compliance and ethical constraints early on. Building models and workflows with the Health Insurance Portability and Accountability Act, the General Data Protection Regulation or the European Union AI Act in mind can also be a long-term moat.
Focus to scale faster
Every start-up faces resource constraints. Without focus, you risk spreading yourself too thin and taking on too much complexity too soon.
Clarifai, a pioneer in computer vision, expanded into too many verticals – retail, manufacturing, media, public sector and more – too early, without dominating any of them. Twelve years later, it remains a relatively minor player.
“Besides competitive compensation, to attract and retain top-tier talent, leaders of AI-first start-ups must also meet distinct expectations.”
In contrast, Runway AI focuses exclusively on building proprietary tools for video generation and editing by filmmakers and advertisers, tailoring its products to address their quality standards and specific workflows, without distraction from other markets.
Despite intense competition from Google and OpenAI, Runway has established strong brand recognition within its niche and recently raised funding at a valuation over $3 billion.
Selling AI solutions may also require overcoming user scepticism, as AI is often perceived as opaque or unreliable.
Successful AI start-ups prioritize transparency, education and hybrid human-in-the-loop workflows to build trust. They also tailor sales and onboarding processes to different buyers – technical leaders, operations teams and end users – who may define value differently. This is much more easily done with focus than across too many verticals.
Specialized talent as a strategic asset
Hiring and retaining talent is crucial for any tech company but AI-first ventures face unique challenges.
First, the pool of machine learning engineers is far smaller than that of software engineers at large, making the competition for talent especially fierce.
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Second, AI tools, model architectures and best practices evolve constantly; thus, engineering teams must stay current and adaptive.
Third, for enterprise AI start-ups, domain expertise is as crucial as machine learning expertise, which requires building and nurturing collaborative interdisciplinary teams.
Besides competitive compensation, to attract and retain top-tier talent, leaders of AI-first start-ups must also meet distinct expectations.
Is the work intellectually meaningful and mission-driven? Will I continue to learn and advance at the same pace as the field? Do leaders understand and value deep technical work? Is there a clear path for growth and contribution?
For AI-first start-ups, leadership is not just about setting direction but also about cultivating an environment where experts can thrive. Ultimately, your talent is your moat.
Technology will keep evolving. What will not change is the need to understand the customer in depth, executional discipline and maintaining a learning mindset. In a world flooded with AI wrappers, success will more likely come to ventures that combine innovation with clarity, focus and speed.
