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Amazon CEO Andy Jassy announces departure of AI executive Rohit Prasad in leadership shakeup

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Amazon CEO Andy Jassy dropped an AI bombshell on employees today, announcing that Rohit Prasad—who has led Amazon’s so-called AGI (artificial general intelligence) team since 2023, overseeing the development of the company’s Nova models—will depart at the end of the year.

Prasad previously served as the head scientist behind Amazon’s Alexa voice assistant, a role he held from the product’s earliest days. When he was appointed to lead the new ambitious AGI effort after ChatGPT launched in November 2022, as part of a scramble to develop a competitive LLM that could help reinvigorate the Alexa voice assistant. it was led almost entirely by ex-Alexa executives. 

In a blog post, Jassy announced that longtime Amazon Web Services (AWS) executive Peter DeSantis will lead a new organization that drives the development of its AI models, custom computer chips (which include its Graviton, Trainium and Nitro chips), and quantum computing efforts. DeSantis had overseen the many teams designing AWS’ global infrastructure. 

“With our Nova 2 models just launched at re:Invent, our custom silicon growing rapidly, and the advantages of optimizing across models, chips, and cloud software and infrastructure, we wanted to free Peter up to focus his energy, invention cycles, and leadership on these new areas,” Jassy wrote, adding that DeSantis would report directly to him.

Jassy also said that as part of the organizational change, Pieter Abbeel, an Amazon Distinguished Scientist in robotics who is also an AI and robotics professor at UC Berkeley, will lead the company’s frontier model research team. Abbeel came to Amazon in 2024 along with other cofounders of his robotics startup Covariant, in a deal that also saw Amazon licensing Covariants software, which included AI models that gave robots the ability to quickly adapt to new environments and tasks.

“Pieter is one of the world’s leading AI researchers, and co-founder of Covariant, which pioneered the first commercial foundation model for robotics,” Jassy wrote. “His deep expertise in generative AI and reinforcement learning makes him well-suited to advance Amazon’s AI research as we push the boundaries of what’s possible for customers.” 

The news of Prasad’s departure comes as somewhat of a surprise, given that he was recently at Amazon’s Re:Invent conference discussing the latest Nova models. However, over the past two years there has been significant media coverage suggesting that Amazon’s Alexa AI and AGI-related efforts have struggled and fallen behind competitors. 

A year ago, for example, Fortune’s Jason Del Rey reported exclusively that leaked Amazon documents identified critical flaws in the delayed AI reboot of Alexa. And in June 2024, Fortune reported that Amazon’s had blown Alexa’s shot to dominate AI, according to more than a dozen employees who worked on it—partly due to a lack of adequate data, even though Prasad, pushed the AGI team to work harder and harder, with a message to “get some magic” out of the LLM. 

In addition, last week’s Amazon layoffs fueled concerns about whether Amazon’s was still lagging behind in AI, and whether the cuts reflected slowing growth. That came on the heels of comments in October by analyst Mark Shmulik of Bernstein, who said Amazon’s AWS was in “last place” in the AI cloud race. 

However, The Information as well as Bloomberg reported this week that Amazon was in talks to invest $10 billion in OpenAI. OpenAI, in turn, had agreed to use Amazon’s Tranium AI chips, perhaps helping to counter the narrative that the company is behind in AI. OpenAI had previously agreed to spend $38 billion using AWS for computing.

Amazon also has a deal with AI company Anthropic, in which Amazon has invested $8 billion. Anthropic has agreed to use AWS’s Trainium chips for training and Anthropic’s Claude model is being used to answer some queries in the new Alexa Plus.



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From search to discovery: how AI Is redrawing the competitive map for every brand

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In the past, search used to look something like this in Google: “black running shoes, women’s size 8, under $100” – and ten blue links and a few shopping ads likely appeared. A helpful first step, but requiring further research and analysis.

Now, you can ask an even more pointed question – perhaps adding in a preference for arch support, a shopping mile radius – to a large language model (LLM) and get a clear, context-rich answer: “Here are three nearby options that fit your criteria. The top-rated one is available for pickup in 40 minutes.”

It’s an improved interaction, but not at the cost of a more complex user experience. This new way of search is redefining consumer behavior and expectations, and how marketers must approach brand visibility. In fact, it represents a reconfiguration of digital marketing and a new economy of visibility.

As these interactions become more complex and context-rich, the way we measure success must evolve too.

Visibility Is the New KPI

In traditional SEO, success means ranking on page one of Google. In the AI era, success means being part of the answer — cited, mentioned, or described accurately when an AI system responds.

This is not a mere marketing nuance: it’s a structural shift in how digital presence is valued. Companies that understand this will treat AI visibility as a new form of brand capital, something to monitor and manage as carefully as reputation or market share.

Advertising economics are already following this pattern: U.S. advertisers are projected to spend over $25 billion annually on AI-powered search placements by 2029, which is nearly 14% of total search budgets.

But, understanding how visibility is measured is just the first step. To capture it effectively, brands must recognize that product discovery itself is being reconstructed, with two distinct search experiences shaping how users find and interact with information.

Two User Experiences, Two Optimization Models

We now have two search experiences — traditional search and AI-driven search — each serving different user needs.

Frankly, this is the simplest framework to offer, when in fact, it is even more complex and nuanced once you take into account AI agents that act autonomously on behalf of the customer.

Traditional search is navigational, guiding users through lists of pages. Effectively, it points them in the right direction.

Meanwhile, AI-driven search is conversational, contextual, and consultative. It’s able to perform multi-step research, interpret context, and merge data from multiple sources into one synthesized response. For marketers, that means building for two visibility models: in SEO, we optimize for keywords; in AI discovery, we optimize for prompts.

The shift in user behavior is measurable and gaining ground. According to Semrush AI Visibility Index, between August and October 2025:

To stay visible, brands must start by identifying which questions matter most to their business – prioritizing prompts that are both high-volume and high-impact. Irrelevant traffic is wasted effort; rare relevance won’t scale. The sweet spot has always been where volume meets relevance, and AI discovery only raises the stakes—rewarding context, authority, and precision the same way great SEO always has.

As AI-driven and traditional search continue to evolve, the line between them is beginning to blur. Brands that optimize for both experiences today will be best positioned to thrive as these models converge into a single, unified discovery interface.

Preparing for the AI + Traditional Search Convergence

Eventually, you’ll see conversational answers alongside maps, reviews, and transactional links — a mix of synthesis and structure. When that happens, businesses will track two main metrics:

  • Traffic, the traditional measure of visits
  • AI Visibility, a new measure of how often and how accurately a brand appears in AI-generated responses

But visibility alone won’t be enough. The next wave of competition will happen at the content layer.

Brands will need to build for both bots and humans — crafting content that reads naturally, ranks intelligently, and feeds the context these models rely on. It’s a new kind of content development, where clarity for users and machine readability carry equal weight.

When that becomes common, websites will need to work as seamlessly for bots as they do for people. Features like SMS-based authentication or manual verification could block machine-driven transactions entirely. Businesses will need to rethink checkout and navigation to accommodate non-human operators.

While optimizing for visibility and content readiness is essential, the larger shift is economic: the convergence of AI and search is redefining how value is created, measured, and captured across the digital landscape.

AI Discovery and the New Economics of Search

The economics of search are changing.

This convergence of SEO and AI visibility is not a short-term marketing trend. It’s a deeper transformation — the creation of a discovery layer that connects information accuracy, credibility, and commercial outcomes in a continuous loop.

Within five years, we’ll unlikely distinguish between “search engines” and “AI assistants.” Instead, we’ll talk about several intelligent systems from companies such as Google and OpenAI that decide what people see, trust, and buy.

While the system itself is changing, the opportunity remains open. AI Search doesn’t belong only to the biggest players — it’s a reset. Smaller brands can rise faster by being precise, credible, and contextually relevant, while larger enterprises must relearn agility and authority at scale.

In traditional SEO, the strongest often dominated; in AI discovery, the most relevant wins.

Businesses that measure and manage their visibility within this new system will define the next era of digital competition.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.



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TikTok agrees U.S. joint venture deal with Oracle, Silver Lake and MGX

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TikTok has signed agreements with three major investors — Oracle, Silver Lake and MGX — to form a new TikTok U.S. joint venture, ensuring the popular social video platform can continue operating in the United States.

The deal is expected to close on Jan. 22, according to an internal memo seen by The Associated Press. In the communication, CEO Shou Zi Chew confirmed to employees that ByteDance and TikTok signed the binding agreements with the consortium.

“I want to take this opportunity to thank you for your continued dedication and tireless work. Your efforts keep us operating at the highest level and will ensure that TikTok continues to grow and thrive in the U.S. and around the world,” Chew wrote in the memo to employees. “With these agreements in place, our focus must stay where it’s always been—firmly on delivering for our users, creators, businesses and the global TikTok community.”

Half of the new TikTok U.S. joint venture will be owned by a group of investors — among them Oracle, Silver Lake and the Emirati investment firm MGX, who will each hold a 15% share. 19.9% of the new app will be held by ByteDance itself, and another 30.1% will be held by affiliates of existing ByteDance investors, according to the memo. The memo did not say who the other investors are and both TikTok and the White House declined to comment.

The U.S. venture will have a new, seven-member majority-American board of directors, the memo said. It will also be subject to terms that “protect Americans’ data and U.S. national security.”

U.S. user data will be stored locally in a system run by Oracle. The memo said U.S. users will continue “enjoying the same experience as today” and advertisers will continue to serve global audiences with no impact from the deal.

TikTok’s algorithm — the secret sauce that powers its addictive video feed — will be retrained on U.S. user data to “ensure the content feed is free from outside manipulation,” the memo said. The U.S. venture will also oversee content moderation and policies within the country.

American officials have previously warned that ByteDance’s algorithm is vulnerable to manipulation by Chinese authorities, who can use it to shape content on the platform in a way that’s difficult to detect.

The algorithm has been a central issue in the security debate over TikTok. China previously maintained the algorithm must remain under Chinese control by law. But the U.S. regulation passed with bipartisan support said any divestment of TikTok must mean the platform cuts ties — specifically the algorithm — with ByteDance.

The deal marks the end of years of uncertainty about the fate of the popular video-sharing platform in the United States. After wide bipartisan majorities in Congress passed — and President Joe Biden signed — a law that would ban TikTok in the U.S. if it did not find a new owner in the place of China’s ByteDance, the platform was set to go dark on the law’s January 2025 deadline. For a several hours, it did. But on his first day in office, President Donald Trump signed an executive order to keep it running while his administration tries to reach an agreement for the sale of the company.

Three more executive orders followed, as Trump, without a clear legal basis, continued to extend the deadline for a TikTok deal. The second was in April, when White House officials believed they were nearing a deal to spin off TikTok into a new company with U.S. ownership that fell apart after China backed out following Trump’s tariff announcement. The third came in June, then another in September, which Trump said would allow TikTok to continue operating in the United States in a way that meets national security concerns.

TikTok has more than 170 million users in the U.S. About 43% of U.S. adults under the age of 30 say they regularly get news from TikTok, higher than any other social media app including YouTube, Facebook and Instagram, according to a Pew Research Center report published this fall.

Shares of Oracle jumped $9.07, or 5%, to $189.10 in after-hours trading.

This story was originally featured on Fortune.com



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Stocks: Bank of America warns fund managers just triggered a contrarian ‘sell’ signal

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Bank of America’s “Bull & Bear Indicator” rose from 7.9 to 8.5 in the last few days, triggering its contrarian “sell” signal for risk assets, according to a note from analyst Michael Hartnett and his colleagues seen by Fortune this morning. The indicator is derived from BofA’s regular fund manager survey, which asks 200-plus investment managers about their appetite for risk. The logic of the Bull & Bear Indicator is that when everyone in the market is bullish, it’s time to leave.

S&P 500 futures were up 0.25% this morning. The last session closed up 0.79%. The index remains a little less than 2% beneath its all-time high. Markets in Asia largely closed up this morning. Europe and the UK were flat in early trading. Whether stocks are overvalued—especially tech stocks—has been a running theme in the equity markets all year long. 

BofA’s sell signal has been activated 16 times since 2002, Hartnett says. On average, the MSCI All Country World Index (an index that represents stocks globally) declined by 2.4% afterwards, the bank says, with a maximum average drawdown of 8.5% by three months later.

The indicator has a record of being right 63% of the time—so it isn’t flawless. But BofA also notes that investors are in an unusually “risk-on” mood in equities right now: Last week saw a record inflow of $145 billion into equity exchange-traded funds, and the second-highest ever weekly inflow of money into U.S. stocks ($77.9 billion), Hartnett wrote. The indicator thus implies that a smart investor might want to become fearful given that others are too greedy.

Investor sentiment roughly correlates with sentiment in the Purchasing Managers Index, a survey of supply chain managers responsible for corporate buying. Right now, investors have broken ranks with the PMI, with the former being much more positive about future than the latter. They appear to be expecting the PMI to follow their lead, Hartnett argues.

“Investors [appear to be] bull positioned for ‘run-it-hot’ PMI & [earnings per share] acceleration on rate cuts, tariff cuts, tax cuts,” he told clients.

Conversely, assuming the market does not pull back—or a revesal is temporary—he predicts EPS growth of 9% for stocks in 2026 despite increased U.S. unemployment, and the threat of “bond vigilantes slowing [the] AI capex boom.”

Here’s a snapshot of the markets ahead of the opening bell in New York this morning:

  • S&P 500 futures are up 0.33% this morning. The last session closed up 0.79%. 
  • STOXX Europe 600 was flat in early trading. The U.K.’s FTSE 100 was flat in early trading. 
  • Japan’s Nikkei 225 was up 1.03%. 
  • China’s CSI 300 was up 0.34%. 
  • The South Korea KOSPI was up 0.65%. 
  • India’s NIFTY 50 was up 0.59%. 
  • Bitcoin was at $88K.
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