Feature
A recent spate of tech releases points to a changing dynamic in the world of AI: After two years of promising, developing and hyping, solutions providers have come under pressure from users and businesses seeking a return on their investments.
OpenAI is perhaps the most publicized example. Since October, the company has released a series of new features and product packages that increase a user’s options as much as they advance technology.
The first of these was the October release of ChatGPT Search, a search engine that adds real-time information to OpenAI’s mix. That was followed in December by ChatGPT Pro, a paid tool that, for $200 per user per month, provides access to several large language models, new voice capabilities and the ability to attack complex tasks through reasoning.
Just a few days later came the release of the video generator Sora, which OpenAI positioned as a first step spreading the use of video tools and offering users the chance to experiment with AI-created video snippets by entering prompts as text, uploading their own assets and creating videos frame-by-frame through a storyboard tool.
(Sidenote: Sora is available to OpenAI’s paying customers, except in Europe and the UK, where media reports attribute the omission to OpenAI concerns about European privacy laws.)
These developments add value to OpenAI’s products for users, but analysts say they also illustrate a new focus of the AI business: profitability.
The AI Profitability Race Is On
Kashyap Kompella, CEO of analyst firm RPA2AI Research, said OpenAI’s release of new features — and the Pro plan in particular — “is a signal that the AI providers are now being forced to turn attention to profitability, not just technology,” he told TechTarget.
Kompella’s opinion is backed up by a variety of recent releases by AI vendors, outside of OpenAI.
For instance, Google released Gemini 2.0, which improves the platform’s large language model and introduces new image and audio capabilities. According to the company, the release “will enable us to build new AI agents that bring us closer to our vision of a universal assistant.”
And just this week, Google announced the release of the second generation of its video generator Veo 2, which critics say is a response to OpenAI’s Sora. The company also released Imagen 3, which creates images from text prompts.
Meanwhile, Anthropic launched Claude 3.5 Haiku, which dramatically improves performance and adds a subscription option, Midjourney launched Patchwork, another content creation tool, and Mistral revealed plans to expand into Silicon Valley, where it can take advantage of a larger pool of development talent including engineers, AI scientists and sales staff.
Related Article: OpenAI's $200 Gamble: Who Is the Audience for ChatGPT Pro?
The Compelling Business Model Will Edge Out the Competition
All of this activity spotlights another challenge — besides turning a profit — facing AI developers: How will they stand out from the crowd and convert customers?
“Will OpenAI’s first-to-market-advantage propel it in the long run, or will Google’s integrations with so much of the software people already use prove decisive?” wrote Sage Lazzaro in an editorial for Fortune. “Ads for AI products are already everywhere — will this marketing sway users?”
It's a good question, but the answer may be limited by the constraints of AI itself.
AI is still a nascent technology that has yet to offer the types of capabilities computer scientists dream of. “AI is still in an ‘adolescent phase’ in some areas, such as intelligent automation and recommendation systems,” said Nicholle Lindner, managing executive partner of global technology services at Gartner Asia-Pacific and Japan.
For now, Lindner sees AI’s strengths in creating content, offering conversational interfaces and simplifying research. Its sexiest applications, she said, remain on the horizon: predictions, forecasting and running autonomous systems, like agents. Human beings continue to outperform AI in those areas.
The problem, it seems, is that developers are paying more attention to delivering value today rather than emphasizing what might one day be possible. As Matt McIlwain wrote in a post, they’ve come to realize that “the most important model required to remain an enduring winner in AI is a compelling business model.” After all, critics have said that the initial model, which centered on access to large language models, is untenable due to the costs involved in building and training large language models far outstripping their revenue potential.
OpenAI Pro may cost $200 per month, but large companies that need its capabilities won’t find $2,400 a year to be much of a barrier. In contrast, LexisNexis’s law-focused Advance product costs up to $199 per month while the more in-depth Practice Advisor costs $999 per month. Thomson Reuters’ competing Westlaw unit charges up to $382 per month.
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Aidan Gomez, CEO of AI platform provider Cohere, said this is why there’s a lot of excitement at the application layer. Products like OpenAI’s paid subscriptions can generate revenue until the business’s true dynamics shake out. Meanwhile, products like ChatGPT Pro or Google’s Deep Research feature allow users to do things like conduct more thorough and detailed research. By offering such tangible benefits, AI providers can charge real money and build real businesses.
