Home Money The unattractive future of generative AI is enterprise applications

The unattractive future of generative AI is enterprise applications

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The unattractive future of generative AI is enterprise applications

However, that amount includes massive funding from corporate sponsors, such as Microsoft’s capital infusion into OpenAI and Amazon’s funding of Anthropic. If you boil it down to conventional venture capital investments, funding in 2023 for AI startups was much lower and was only on track to match the total amount raised in 2021.

Pitchbook senior analyst Brendan Burke noted in a report that venture capital funding was increasingly being channeled toward “underlying AI technologies and their end vertical applications, rather than general-purpose middleware in audio, language, images and video”.

In other words: a GenAI app that helps a business generate e-commerce sales, analyze legal documents, or maintain SOC2 compliance is probably a safer bet than one that pops up a smart video or photo every once in a while.

Sierra co-founder Clay Bavor says he believes it’s not necessarily cloud computing or API costs that are driving AI startups toward B2B models, but rather the benefits of targeting a specific customer and iterating. about a product based on your comments. “I think everyone, myself included, is pretty optimistic that the capabilities of these AI models will increase as costs go down,” Bavor says.

“There’s something really powerful about having a clear problem to solve for a particular customer,” he says. “And then you can get feedback about, ‘Does this work? Is this solving a problem?’ And if you build a business with that, it’s very powerful.”

Although ChatGPT sparked an AI boom in part because it can nimbly generate code one second and sonnets the next, Arvind Jain, CEO of AI startup Glean, says the nature of the technology still favors narrow tools. On average, a large company uses more than a thousand different technical systems to store company data and information, he says, creating an opportunity for many smaller companies to sell their technology to these corporations.

“We’re in this world where there are basically a ton of functional tools, each of which solves a very specific need. That’s the way of the future,” says Jain, who spent more than a decade working on search at Google. Glean powers a workplace search engine by connecting to various corporate applications. It was founded in 2019 and has raised more than $200 million in venture capital funding from Kleiner Perkins, Sequoia Capital, Coatue and others.

Error checking

Adjusting a generative AI product to serve enterprise customers has its challenges. Errors and “hallucinations” from systems like ChatGPT can have greater consequences in a corporate, legal or medical environment. Selling generation AI tools to other companies also means meeting their privacy and security standards and, potentially, the legal and regulatory requirements of your industry.

“It’s one thing for ChatGPT or Midjourney to be creative for an end user,” Bavor says. “It’s quite another thing for AI to get creative in the context of enterprise applications.”

Bavor says Sierra has dedicated “a tremendous amount of effort” to establishing safeguards and parameters in order to meet security and compliance standards. This includes using… more AI to fine-tune Sierra’s AI. By using an AI model that generates correct answers 90 percent of the time, but then adding additional technology that can detect and correct some of the errors, you can achieve a much higher level of accuracy, he explains.

“You really have to connect AI systems to business use cases,” says Jain, CEO of Glean. “Imagine a nurse in a hospital system using AI to make some decision about patient care; you simply can’t go wrong.”

A less predictable threat to smaller AI companies that sell their products to enterprise customers: What if a giant generation AI unicorn like OpenAI, with its burgeoning sales team, decides to implement exactly the tool that a singular startup has been building?

Many of the AI ​​startups WIRED spoke to are trying to move away from relying entirely on OpenAI technology by using alternatives like Anthropic’s Claude or large open source language models like Meta’s Llama 3. Some startups even intend to eventually build their own AI technology. But many AI entrepreneurs are stuck paying for access to OpenAI’s technology while potentially competing with it in the future.

Tome’s Peiris considered the question and then said he is now especially focused on sales and marketing use cases and “being amazing at generating high quality for these people.”

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