Meta Delays Launch of New ‘Avocado’ AI Model
Meta is delaying Avocado, its next flagship AI model, after internal benchmarks came back uncomfortable. The model, originally expected earlier this year, is now pushed to at least May.
It did not outperform Google’s Gemini 3. It trails leading models from OpenAI and Anthropic. It did beat Gemini 2.5 and improved on Llama 4, which is something, though not the kind of something you lead a press release with.
In response, Meta’s senior leadership is reportedly exploring licensing Gemini models from Google to keep Meta AI competitive across Facebook, Instagram, and WhatsApp while internal development catches up. Apple already did something similar, paying roughly a billion dollars to integrate Gemini into Siri. So there is precedent. Still, the image of two of the world’s largest technology companies licensing their AI brains from a third is worth pausing on.
The model itself, Avocado, comes out of Meta’s newly formed Superintelligence Labs, led by Alexandr Wang, whose company Scale AI Meta acquired last year for $14.5 billion. It is designed for logical reasoning, software development, and agentic behavior, meaning it is meant to plan and execute tasks across multiple steps autonomously. Meta is spending between $115 billion and $135 billion on AI infrastructure this year. That number is not a typo.
So we have a company spending at a scale almost impossible to conceptualize, building toward a model it had to delay, potentially filling the gap by licensing from a competitor. The honest question this raises is not about Avocado specifically.
It is about what all of this is starting to look like.
SaaS, at its peak, worked on a simple premise. Big companies built software, smaller companies and enterprises paid monthly to use it, and the value was in the product being better than whatever you could build yourself. The switching costs were real, the integrations ran deep, and the recurring revenue was extraordinarily predictable. Salesforce, Workday, ServiceNow. The model printed money for two decades.
AI is replicating that architecture almost beat for beat, except the product is not software anymore. It is intelligence. OpenAI has a subscription. Anthropic has a subscription. Google has a subscription. Meta wants one too. The enterprise deals, the partner networks, the platform integrations, the certifications for implementation consultants. If you squint, it is SaaS with a different name on the door and a much larger infrastructure bill.
The difference, and it matters, is that in SaaS the product mostly stayed where you put it. An AI model that is behind the competition is a much more immediately felt problem because the user knows. They have used something better. They will go find it again. The switching cost that protected SaaS incumbents for years is much thinner here because the interface is often just a text box and the alternative is one tab away.
This is what Meta’s delay actually tells us. In a world where the product is intelligence, being second is a real problem in a way it was not when the product was a feature set that took months to migrate away from. The benchmarks that came back short on Avocado are not just an engineering setback. They are a user retention problem, a distribution problem, and a positioning problem, all arriving at the same time.
Meta has the infrastructure spend to fix the engineering part. The rest of it is harder to budget for.
Whether these companies have thought carefully enough about what it means to be in a subscription business where the customer can feel, in real time, whether what they are paying for is good enough, is the question we keep coming back to.
SaaS companies spent years making it hard to leave. AI companies are making it very easy to compare. That is a different game entirely.
