Google has become so integral to online navigation that its name became a verb, meaning “to find things on the Internet.” Soon, Google might just tell you what’s on the Internet instead of showing you. The company has announced an expansion of its AI search features, powered by Gemini 2.0. Everyone will soon see more AI Overviews at the top of the results page, but Google is also testing a more substantial change in the form of AI Mode. This version of Google won’t show you the 10 blue links at all—Gemini completely takes over the results in AI Mode. — Read More

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According to Business Insider, Amazon is developing an AI model that incorporates advanced “reasoning” capabilities, similar to models like OpenAI’s o3-mini and Chinese AI lab DeepSeek’s R1. The model may launch as soon as June under Amazon’s Nova brand, which the company introduced at its re:Invent developer conference last year. — Read More

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Over the past few years, Google has embarked on a quest to jam generative AI into every product and initiative possible. Google has robots summarizing search results, interacting with your apps, and analyzing the data on your phone. And sometimes, the output of generative AI systems can be surprisingly good despite lacking any real knowledge. But can they do science?

Google Research is now angling to turn AI into a scientist—well, a “co-scientist.” The company has a new multi-agent AI system based on Gemini 2.0 aimed at biomedical researchers that can supposedly point the way toward new hypotheses and areas of biomedical research. However, Google’s AI co-scientist boils down to a fancy chatbot.

… The AI co-scientist contains multiple interconnected models that churn through the input data and access Internet resources to refine the output. Inside the tool, the different agents challenge each other to create a “self-improving loop,” which is similar to the new raft of reasoning AI models like Gemini Flash Thinking and OpenAI o3.  — Read More

A new Google white paper, titled “Agents“, imagines a future where AI takes on a more active and independent role in business. Published without much fanfare in September, the 42-page document is now gaining attention on X.com (formerly Twitter) and LinkedIn.

It introduces the concept of AI agents — software systems designed to go beyond today’s AI models by reasoning, planning and taking actions to achieve specific goals. Unlike traditional AI systems, which generate responses based solely on pre-existing training data, AI agents can interact with external systems, make decisions and complete complex tasks on their own. — Read More

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For almost two years now, the world’s biggest tech companies have been at war over generative AI. Meta may be known for social media, Google for search, and Amazon for online shopping, but since the release of ChatGPT, each has made tremendous investments in an attempt to dominate in this new era. Along with start-ups such as OpenAI, Anthropic, and Perplexity, their spending on data centers and chatbots is on track to eclipse the costs of sending the first astronauts to the moon.

To be successful, these companies will have to do more than build the most “intelligent” software: They will need people to use, and return to, their products. Everyone wants to be Facebook, and nobody wants to be Friendster. To that end, the best strategy in tech hasn’t changed: build an ecosystem that users can’t help but live in. Billions of people use Google Search every day, so Google built a generative-AI product known as “AI Overviews” right into the results page, granting it an immediate advantage over competitors. — Read More

AI has led to breakthroughs in drug discovery and robotics and is in the process of entirely revolutionizing how we interact with machines and the web. The only problem is we don’t know exactly how it works, or why it works so well. We have a fair idea, but the details are too complex to unpick. That’s a problem: It could lead us to deploy an AI system in a highly sensitive field like medicine without understanding that it could have critical flaws embedded in its workings.

A team at Google DeepMind that studies something called mechanistic interpretability has been working on new ways to let us peer under the hood. At the end of July, it released Gemma Scope, a tool to help researchers understand what is happening when AI is generating an output. The hope is that if we have a better understanding of what is happening inside an AI model, we’ll be able to control its outputs more effectively, leading to better AI systems in the future. — Read More

Stratechery has benefited from a Meta cheat code since its inception: wait for investors to panic, the stock to drop, and write an Article that says Meta is fine — better than fine even — and sit back and watch the take be proven correct. Notable examples include 2013’s post-IPO swoon, the 2018 Stories swoon, and most recently, the 2022 TikTok/Reels swoon (if you want a bonus, I was optimistic during the 2020 COVID swoon too).

Perhaps with that in mind I wrote a cautionary note earlier this year about Meta and Reasonable Doubt: while investors were concerned about the sustainability of Meta’s spending on AI, I was worried about increasing ad prices and the lack of new formats after Stories and then Reels; the long-term future, particularly in terms of the metaverse, was just as much of a mystery as always.

Six months on and I feel the exact opposite: it seems increasingly clear to me that Meta is in fact the most well-placed company to take advantage of generative AI.  — Read More

The AI21 Jamba 1.5 family of models is state-of-the-art, hybrid SSM-Transformer instruction following foundation models. The Jamba models are the most powerful & efficient long-context models on the market, which deliver up to 2.5X faster inference than leading models of comparable sizes.

The models demonstrate superior long context handling, speed, and quality. They mark the first time a non-Transformer model has been successfully scaled to the quality and strength of the market’s leading models. — Read More

The Paper