In the past three years, I have been feeling like Baba Vanga. I said right after the release of ChatGPT that hallucinations wouldn’t be fixed and look where we are. The new 'reasoning' models just hallucinate more convincingly, and the 'human-in-the-loop' safety net is failing because nobody actually reads the output anymore. I predicted that GPT-5 would flop. I said AI agents will snowball hallucinations and will not have the autonomy when the hype just started. I warned that LLMs are not a business as they are an auxiliary product, a commodity like a chip without an architecture, destined to be crushed by the interface owners like Google or Amazon. I said that all models would eventually end up like pickles in a jar: pick any, they all taste the same. The differentiation is dead. And here we are. So now, I am going to do some predictions for 2026.
1. Year of Disappointment In AI
AI agents will not deliver for business. They will not greatly automate business processes; the adoption of Microsoft Copilot Studio, Snowflake Cortex, and Salesforce Agentforce will remain low. For example, Salesforce has reportedly closed only ~8,000 Agentforce deals out of 150,000+ customers and after laying off 4,000 people admitted that there are trust issue and they need to scale back AI usage.
The graveyard that is currently filled with RAG bots will have a neighboring graveyard even bigger: full of AI Agent PoCs. We are already seeing failure rates of over 95% for these pilots. This time, management will realize that they were sold science fiction and that firing half of customer support because they put an “agent” into production was a big oopsie. Klarna already proved this in 2025, rehiring human agents after realizing their AI support tanked customer satisfaction. To hire people back, mitigate the losses, and simply save the budget, the savings will come from the innovation budget that was originally allocated for AI. Forrester now predicts enterprises will delay 25% of AI spend. The hype and overpromising have undermined trust: getting money for even a meaningful AI use case will be tough. Expect statements flying around from various companies similar to the infamous 1966 ALPAC report, which concluded there was ‘no usable machine translation’ and killed funding for a decade.
we do not have useful machine translation. Further, there is no immediate or predictable prospect of useful machine translation
1966 ALPAC Report
We will see very similar statements declaring LLMs are not useful for real business goals.
Before you continue, here is a little musical prediction from the senior architect Meow:
(generated with Nano Banana Pro, Veo-3.1, MiniMax Sound, Hailuo-2.0 on HailuoAI)
2. Downfall of Nvidia’s Dominance
Nvidia will lose its dominance in data centers. We already see strong indications. Google and Meta collaborate on “TorchTPU”, a project effectively breaking the CUDA lock-in by making PyTorch run natively on Google's hardware. Google is now clearly moving to sell TPUs directly to external data centers, not just via cloud. Meanwhile, AWS and Anthropic have activated Project Rainier, a massive cluster of Trainium 2 chips that Anthropic is using for its primary training, cutting Nvidia out of one of the world's largest AI workloads. China will entirely switch to Huawei and possibly even other domestic chips. Considering ASICs are both cheaper and more efficient in terms of energy consumption - Nvidia will become a luxury brand for the lazy. And after the release of TorchTPU, Nvidia’s biggest moat - CUDA - will be gone.
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