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Airbnb Chief Executive Officer Brian Chesky says the company intends to integrate ChatGPT’s artificial intelligence capabilities into its travel platform, but that the software is not yet ready for the level of performance Airbnb requires.

“The [software development kit] wasn’t quite robust enough for the things we want to do,” Chesky said during an interview on CNBC’s Squawk Box on Wednesday. He added that Airbnb would “probably” proceed with ChatGPT integration eventually, emphasizing that the company continues to evaluate multiple AI models as part of its broader technology roadmap.

Airbnb this week unveiled a major product update introducing new social and personalization features, including direct user messaging and a revised AI-driven assistant capable of modifying or canceling bookings in North America. The new assistant is part of the company’s wider effort to enhance automation and create a seamless travel experience powered by artificial intelligence.

However, in a separate conversation with Bloomberg, Chesky acknowledged that OpenAI’s ChatGPT is still “not quite ready” to meet Airbnb’s operational and user-experience standards. He disclosed that the company is currently relying heavily on Alibaba’s Qwen model—a large language model developed by the Chinese technology conglomerate—alongside a blend of 13 different chatbots designed to power Airbnb’s AI capabilities.

“We’re all going to have to work together,” Chesky said. “AI is going to lift up a lot of companies. If they want to vertically integrate every single thing, that’s going to be very, very difficult.”

Airbnb’s Expanding AI Strategy

The latest update marks Airbnb’s most ambitious step toward AI-driven personalization since it launched its “Airbnb Rooms” product earlier this year, aimed at restoring affordability and user trust amid rising global travel costs. Chesky said the company’s long-term vision is to use AI to transform Airbnb into a “personal travel concierge”, capable of learning user preferences, suggesting destinations, creating custom itineraries, and handling post-booking support autonomously.

The move comes as major travel and hospitality platforms race to incorporate generative AI tools to improve efficiency and engagement. Expedia, for instance, integrated ChatGPT earlier this year to help users build travel plans conversationally, while Booking.com launched its “AI Trip Planner” powered by OpenAI’s models.

For Airbnb, the integration of ChatGPT—or any comparable large language model—poses both opportunities and challenges. Unlike simple text-based assistants, Airbnb’s AI must operate within a complex, transaction-heavy environment, coordinating between guests, hosts, payments, and support systems while maintaining regulatory and safety compliance across over 220 countries and regions.

Why ChatGPT Isn’t “Quite Ready”

According to Chesky, Airbnb’s experiments with OpenAI’s toolkit revealed limitations in reliability, contextual understanding, and the ability to integrate seamlessly with Airbnb’s proprietary systems. The software development kit (SDK), he said, “wasn’t robust enough” to manage the advanced workflows Airbnb envisions—such as real-time itinerary modification, personalized upselling, and multilingual support for millions of users simultaneously.

AI experts note that while ChatGPT’s GPT-4 model excels at natural conversation, its effectiveness in high-stakes transactional systems depends on extensive fine-tuning and data integration. Airbnb’s use of Alibaba’s Qwen model—part of the Tongyi Qianwen AI suite—suggests the company is diversifying its AI infrastructure to improve resilience and avoid dependency on a single model provider.

Airbnb’s cautious approach contrasts with some of its tech peers. Expedia Group launched its ChatGPT plugin integration earlier this year, enabling users to create itineraries directly from the chatbot interface. Trip.com Group, China’s largest travel platform, is developing its own AI model in partnership with Baidu. Google, meanwhile, has integrated its Gemini-powered assistant into its travel products, offering itinerary recommendations and hotel search tools.

Chesky’s comments denote an industry-wide recognition that while AI has transformative potential, real-world deployment requires rigorous testing, data governance, and privacy safeguards.

The Airbnb CEO, who has long been close to OpenAI CEO Sam Altman, said he believes generative AI will usher in a “consumer app boom” comparable to the smartphone revolution. However, he cautioned that most companies will need to collaborate rather than compete vertically in building end-to-end AI ecosystems.

Airbnb’s experiments with ChatGPT and Qwen represent the early stages of what could become a major transformation in online travel services. By merging conversational AI with its global accommodation network, Airbnb aims to eliminate traditional friction points—such as customer service backlogs, host-guest miscommunication, and manual trip planning—and replace them with automated, intelligent interactions.

Chesky has often framed Airbnb’s evolution as moving “from a listings company to a travel platform that knows you.” With AI at the core of that vision, the company’s eventual integration of ChatGPT—when the technology matures—could redefine how millions of travelers plan, book, and experience their trips worldwide.

Meta is cutting around 600 jobs from its newly formed superintelligence division as the company reorganizes its sprawling artificial intelligence operations, signaling a shift toward smaller, faster teams in what CEO Mark Zuckerberg has called Meta’s “year of efficiency.”

In a memo to staff on Wednesday, Meta’s chief AI officer, Alexandr Wang, said the restructuring was meant to make the team “more agile and talent-dense” as the company pursues its ambitious goal of building “personal superintelligence” — a term Zuckerberg uses to describe AI systems that could eventually surpass human capabilities.

“By reducing the size of our team, fewer conversations will be required to make a decision, and each person will be more load-bearing and have more scope and impact,” Wang wrote.

The layoffs mark one of the most significant reorganizations within Meta’s AI division since the company established the Meta Superintelligence Lab (MSL) in June. The division was designed to spearhead research into next-generation AI models, consolidating engineers and scientists from multiple Meta units — including Facebook AI Research (FAIR), infrastructure, and product teams — under one structure.

Meta has described MSL as a cornerstone of its long-term AI strategy, with Zuckerberg pledging billions of dollars in compute and talent to compete with OpenAI, Anthropic, and Google DeepMind. The company has been aggressively recruiting since mid-2024, offering multimillion-dollar pay packages to lure researchers from rival firms.

Over the summer, Meta poached more than 50 scientists and engineers from competitors, including OpenAI and Google, though OpenAI CEO Sam Altman downplayed the departures. “None of our best people took the offers,” Altman said at the time.

Despite the hiring spree, the rapid growth of MSL has created internal strain. Current and former employees have described overlapping mandates and shifting priorities that led to confusion and tension across Meta’s broader AI organization. Several senior researchers reportedly left within months of joining.

The latest cuts appear aimed at resolving that internal friction by consolidating roles and tightening coordination. Wang’s memo said the reorganization would allow the division to “move faster” and empower each remaining team member to have greater influence.

“These are talented people who have worked extremely hard and contributed to our AI effort,” Wang wrote. “We are supporting the majority of those impacted in finding new roles at the company.”

He added that Meta had formed a “tiger team of recruiters” to help displaced employees quickly transition into new positions within the company.

Meta said the restructuring does not meaningfully reduce its overall headcount, as many of those affected are expected to be rehired in other AI or engineering roles.

The company’s move fits into a broader philosophy articulated by Zuckerberg since early 2023, when he began describing Meta’s “year of efficiency” as a way to make the company “leaner is better.” That initiative led to tens of thousands of job cuts across divisions, aimed at flattening management layers and improving decision speed.

In the case of the superintelligence lab, the goal appears to be organizational streamlining rather than cost-cutting.

“Our goal is to enable MSL to move faster,” Wang wrote, emphasizing that Meta’s investment in compute and AI infrastructure would continue. “We remain excited about the models we are training, our ambitious compute plans, and the products we are building, and I’m confident in our path to superintelligence.”

The superintelligence project has become one of Meta’s most expensive bets, with the company investing hundreds of millions of dollars to scale its compute clusters and train large AI models. Meta’s AI ambitions span multiple fronts — from the consumer-facing “Meta AI” assistant integrated into its apps to foundational models powering future augmented and virtual reality experiences.

Zuckerberg has framed AI as central to Meta’s long-term future, positioning the company as a challenger to OpenAI and Google in both research and product deployment.

“We want to build general intelligence openly and responsibly,” he said earlier this year. “Our goal is to make it available to everyone as widely as possible.”

Wang’s memo reflects that same ethos of aggressive efficiency paired with scale. His remarks echo a recurring theme across Meta’s AI units — that smaller, specialized teams are better suited to innovate in a field moving at unprecedented speed.

The restructuring also comes at a time when competition among leading AI companies has intensified. OpenAI, Anthropic, Google, and Meta are each training new frontier models that require massive amounts of compute power, driving a surge in demand for Nvidia chips and data center capacity.

Within this race, Meta has sought to differentiate itself by emphasizing open research and democratized access to its AI tools, including the Llama family of open-source models. The company recently launched Llama 3, which has been widely adopted by developers and integrated into several enterprise systems.

Still, internal tensions have emerged as Meta expands its ambitions beyond open-source models to the more exclusive superintelligence project, where research secrecy and proprietary models are expected to play a larger role. Some researchers reportedly expressed unease over the shift toward confidentiality and the heavy emphasis on rapid scaling.

Wang’s decision to trim the MSL workforce underscores the company’s effort to stabilize the division’s structure and clarify its direction after a summer of intense hiring and shifting goals. Analysts see the move as a sign that Meta is entering a more disciplined phase in its AI race — focusing on execution rather than expansion.

While the exact financial impact of the restructuring remains unclear, Meta insists that the investment pipeline for its AI research remains intact.

“This by no means signals any decrease in investment,” Wang reiterated. “We will continue to hire industry-leading AI-native talent.”