Amazon doesn't have enough data and funds to train genAI Alexa

According to sources familiar with the project, Amazon’s dream of a super-intelligent Alexa powered by generative AI seems to be hitting roadblocks. While competitors like OpenAI, Google, and now even Siri are seeing progress in conversational AI, especially GPT4o, Alexa remains stuck behind.

Multiple sources point to challenges stalling the development of Alexa.

• One major hurdle is data. Training large language models LLMs requires massive datasets. Here, Alexa falls short compared to rivals.
• Another challenge is Amazon’s lack of new computing resources. Training LLMs require powerful GPUs, and Amazon appears to be lagging behind in acquiring them.

Additionally, integrating LLMs with smart home devices and services Alexa connects with is proving more difficult than anticipated.

Internally, Amazon seems to be fighting with its own organizational issues. Slow decision-making is hindering Alexa’s ability to adapt quickly to the fast-paced world of AI.

Different Alexa teams, like Music or Home functionalities, are reportedly clashing over resources and how to fine-tune the LLM for their specific needs.

On the other hand, Amazon has access to Anthropic’s Claude. However, privacy concerns are reportedly preventing Alexa’s teams from leveraging Claude’s capabilities. Not long ago, it was seen that Amazon’s AI chatbot Amazon Q leaked confidential data, internal discount programs, and more.

An internal document about Q’s hallucinations and wrong answers notes that.

Amazon Q can hallucinate and return harmful or inappropriate responses. For example, Amazon Q might return out-of-date security information that could put customer accounts at risk.

Whether Amazon can overcome these hurdles and deliver a truly “super agent” Alexa as envisioned, or if the current version becomes a cautionary tale of missed opportunities, remains to be seen.

More here.