AI Startups Shift Focus to Enterprise Applications
While the total venture capital funding for AI startups in 2023 appears substantial, a significant portion comes from corporate giants like Microsoft and Amazon. Conventional VC investments in AI are on track to match 2021 levels, with a growing emphasis on core AI technologies and their vertical applications rather than general-purpose middleware.
The Power of Solving Specific Problems
Clay Bavor, cofounder of Sierra, believes that targeting a specific customer and iterating based on their feedback is more influential in driving AI startups towards B2B models than computing or cloud API costs. He states, ”There’s just something really powerful about having a clear problem to solve for a particular customer. And then you can get feedback on, ‘Is this working? Is this solving a problem?’ And if you build a business with that, it’s very powerful.”
The Future of AI: Specialized Tools
Despite ChatGPT’s versatility, Arvind Jain, CEO of AI startup Glean, argues that the nature of technology favors narrow tools. With large companies using hundreds of different systems to store data, there is an opportunity for smaller companies to provide specialized solutions. Jain, a former Google search expert, says, “We are in this world where there are basically a bunch of functional tools, each solving a very specific need. That’s the way of the future.”
Challenges in Serving Business Customers
Adapting generative AI products for enterprise clients comes with its own set of challenges. Errors and “hallucinations” can have more severe consequences in corporate, legal, or medical settings. Additionally, AI tools must meet the privacy, security, and regulatory requirements of their target sectors.
Ensuring Accuracy and Compliance
Sierra has invested heavily in establishing safeguards and parameters to meet security and compliance standards, using additional AI to fine-tune its primary AI models. Bavor explains, “If you’re using an AI model that generates correct responses 90 percent of the time, but then layer in additional technology that can catch and correct some of the errors, you can achieve a much higher level of accuracy.”
Jain emphasizes the importance of grounding AI systems for enterprise use cases, stating, “Imagine a nurse in a hospital system using AI to make some decision about patient care—you simply can’t be wrong.”
The Looming Threat of Big Tech
Smaller AI companies face the risk of giant gen AI unicorns like OpenAI rolling out tools that directly compete with their offerings. Many startups are attempting to reduce their dependence on OpenAI’s technology by exploring alternatives like Anthropic’s Claude or open-source large language models. Some even aim to develop their own AI technology in the future. However, many AI entrepreneurs find themselves paying for access to OpenAI’s tech while potentially competing with it down the line.
Peiris, of Tome, remains focused on excelling in sales and marketing use cases, stating, “being amazing at high-quality generation for these folks.”
1 Comment
Transforming enterprise apps? Seems like generative AI is the revolutionary tool we were waiting for!