>>> AMAZON ACQUIRES RIVR (EX-SWISS MILE) — READ-ACROSS FOR AUTOSTORE

AMAZON ACQUIRES RIVR (EX-SWISS MILE) — READ-ACROSS FOR AUTOSTORE
    AMZN confirmed acquisition of Zurich-based delivery robotics startup Rivr (valued $110M Aug 2024). Bezos Expeditions + Industrial Innovation Fund were existing investors.
      KEY POINTS: — Confirms AMZN in active robotics M&A mode. Same playback as Kiva Systems 2012 ($775M): invest → pilot → acquire — $200B 2026 capex budget explicitly includes robotics alongside AI infra — Rivr = last-mile delivery. AutoStore = warehouse/MFC. Complementary, not competing — AMZN building full-stack automated logistics: storage (AutoStore) → picking → sorting → delivery (Rivr)
        AUTOSTORE READ-ACROSS (OSE: AUTO, NOK 10.19): — Stock down ~12% from Feb highs (NOK 13.20) — better entry point — Validates acquisition appetite for robotics assets — "Amazon Integrations" self-install program (2027) deepens partnership before potential M&A — THL + SoftBank combined ~60%+ stake = ready-made exit for strategic buyer — Consensus TP NOK 13.06 implies +28% upside at current levels — Q1 2026 earnings April 23 = next catalyst
          CAVEAT: Also shows AMZN willingness to build in-house (Orbital, humanoid robots, Zoox). Partnership ≠ acquisition.
            AUTO trades ~14x EV/EBITDA 2026E at current price. 9 Buy / 6 Hold / 3 Sell. Amazon optionality not in any broker model.
              Laurent Chekroun

              >>> US After Hours Summary: FDX +8.9%, PL +16.4%, SCHL +9.4% higher on earnings;

              After Hours Summary: FDX +8.9%, PL +16.4%, SCHL +9.4% higher on earnings; CORT +3% on insider buy

              After Hours Gainers:

              Companies trading higher in after hours in reaction to earnings/guidance: CURV +20.8%, PL +16.4%, SCHL +9.4% (also to repurchase up to $200 mln of its stock through modified dutch auction tender offer), FDX +8.9%, YSS +6.8%, FLY +6.3%, GEMI +6.2%, ALMS +0.5% (also files mixed securities shelf offering)

              Companies trading higher in after hours in reaction to news: CORT +3% (Director bought 100000 shares worth ~$3.31 mln), CHAC +2.7% (shareholders approve combination with Xanadu Quantum), BOX +2.3% (authorizes $500 mln increase to its share repurchase program), TRIP +1.7% (Chair of the Board Greg Maffei will not stand for re-election), ORN +1.6% ($125 mln in contract wins), WFG +0.8% (renewal of NCIB to repurchase shares), DHT +0.5% (files mixed securities shelf offering), QCOM +0.3% (shareholder approval on China risk was not approved), APO +0.2% (O and APO to establish partnership; APO to provide a $1 bln investment to O to acquire a 49% interest in a new joint venture), VNDA +0.2% (FDA grants landmark hearing for HETLIOZ in jet lag disorder), CRM +0.2% (Director bought 2,571 shares worth ~$500K), O +0.1% (O and APO to establish partnership; APO to provide a $1 bln investment to O to acquire a 49% interest in a new joint venture)

              After Hours Losers:

              Companies trading lower in after hours in reaction to earnings/guidance: RLMD -2.4%, ASMB -1.1%

              Companies trading lower in after hours in reaction to news: KNOP -8.7% (terminates discussion on proposed buyout offer from Knutsen NYK offshore tankers AS), FLOC -8% (announces launch of 7.8 mln share offering by selling stockholders), NVGS -6.2% (7 mln share offering by selling shareholder), UMAC -4.8% (stock offering), SATL -2.4% (files for $200 mln stock offering), AHRT -1.7% (files for $300 mln mixed securities shelf offering), SLDB -1.7% (two stock offerings by selling shareholders), CODA -1.3% (files for $100 mln mixed securities shelf offering; also files for 500,000 share offering by selling shareholders), ASMB -1.1% (files for $400 mln mixed securities shelf offering), LPLA -0.2% (reports Feb activity metrics)

              WSJ : Delayed Nielsen Data Will Show Traditional TV Back on Top (for Now)

              Delayed Nielsen Data Will Show Traditional TV Back on Top (for Now)
              For a moment, it seemed like streaming had finally overtaken television. But February’s numbers on viewing time—using a new demographic data source—say not so fast

              A forthcoming Nielsen report suggests U.S. viewers spent more time watching traditional TV than streaming in February, reversing well-established trends.
              YouTube, Netflix and most other major streaming platforms saw their estimated viewership shares decline.
              Nielsen recently incorporated new third-party data in an attempt to address perceived inaccuracies.

              Streaming TV’s ascendance over broadcast and cable isn’t as complete as it seemed, a report from the measurement firm Nielsen is set to suggest.

              Streaming accounted for 41.9% of U.S. TV viewing time in February, compared with 47.4% for so-called linear TV, according to unreleased Nielsen data described by people with direct knowledge of the numbers.

              That’s a reversal from Nielsen’s most recent monthly Gauge report, which said TV viewing in January was 47% streaming and 42.7% linear, as well as Nielsen’s announcement last year that streaming had surpassed broadcast and cable viewing in May for the first time.

              The shift wasn’t entirely unexpected. Nielsen had warned clients that its broadcast and cable TV figures could see a boost in February after it began using a study from the industry’s Advertising Research Foundation to inform its estimates of the demographic groups in U.S. households and the technologies they use to watch TV. The company previously drew those estimates entirely from its own volunteer panels.

              Nielsen also communicated the impending change to clients multiple times in recent months, according to a spokeswoman.

              The actual numbers, however, weren’t available to clients until earlier this month.

              YouTube’s estimated share of viewing on TV sets was 11% in the report for the month of February, compared with 12.5% in January. Estimates similarly declined to 7.5% from 8.8% for Netflix, to 3.3% from 4.1% for Amazon Prime Video and to 2.4% from 3% for Roku.

              Other streamers including Disney+, Paramount+ and Tubi also saw their numbers drop.

              Nielsen had planned to publicly release the February report on March 17, but delayed it for a week after some streaming platforms asked for more information. The delay was first reported by Variety.

              Streamers are seeking more data to determine how much of the drop in share of viewership can be attributed to the adoption of ARF data as opposed to actual viewing shifts since January.

              “The question should be, if you had applied this methodology a year ago, then what would the numbers have been a year ago?” said Hernan Lopez, founder of Owl & Co., an advisory firm that focuses on media consumption trends.

              The results suggest Nielsen has historically undercounted the prevalence of broadcast and cable relative to streaming, said Lopez.

              But February also included NBCUniversal’s coverage of the Super Bowl and the Winter Olympics, benefiting Peacock, NBC and the company’s cable networks. Peacock was the only streaming platform to see a lift in the February data, to 2.7% of total viewership from 1.9% in January.

              Nielsen incorporated the ARF study after the Media Rating Council, an industry self-regulatory group, asked the company to use an independent source to address perceived inaccuracies in its demographic data, said George Ivie, executive director at the MRC.

              In a statement, Nielsen said, “Different methodologies produce different results,” adding that the new methodology would “create a one-time shift in viewing data, reflected in the February 2026 Gauge.”

              Nonetheless, it saw long-term trends hewing toward streaming. “Streaming viewership is expected to continue to grow,” the company said.

              Outcomes have varied month to month throughout the five-year history of the Gauge, but streaming hasn’t previously lost as much ground to traditional TV as it did, at least on paper, in the February report.

              The news arrives just ahead of the annual upfronts and newfronts events, when broadcasters and streaming platforms pitch upcoming inventory to marketers and ad buyers.

              Although the share numbers in the Gauge are distinct from the Nielsen audience estimates that underpin most TV ad deals, they could help buyers demand more data from streamers to prove that viewership among key demographics is growing at a healthy clip, according to Ross Benes, senior analyst at eMarketer.

              But it won’t change the fact that eyeballs are moving toward streaming, said Benes.

              “This is something, I think, that matters a lot to marketers right now,” he said. “It probably doesn’t matter much to the consumers—and might not even matter to marketers long-term, because the trends will be what the trends are.”

              WSJ : Jeff Bezos in Talks to Raise $100 Billion for AI Manufacturing Fund

              Jeff Bezos in Talks to Raise $100 Billion for AI Manufacturing Fund
              Amazon.com founder has traveled to Middle East, Singapore in fundraising effort linked to Project Prometheus AI startup

              • Jeff Bezos is in early talks to raise $100 billion for a fund to acquire manufacturing companies and apply AI for automation.
              • The fund, described as a “manufacturing transformation vehicle,” aims to buy companies in major industrial sectors like chipmaking, defense and aerospace.
              • Jeff Bezos, co-CEO of Project Prometheus, plans to use its AI technology to boost the efficiency of businesses owned by the fund.

              Jeff Bezos is in early talks to raise $100 billion for a new fund that would buy up manufacturing companies and seek to use AI technology to accelerate their path to automation.

              The Amazon.com founder is meeting with some of the world’s largest asset managers in order to raise funding for the project. A few months ago, he traveled to the Middle East to discuss the new fund with sovereign wealth representatives in the region. More recently, he went to Singapore to raise funding for the effort as well, according to people familiar with the efforts.

              The fund, described in investor documents as a “manufacturing transformation vehicle,” is aiming to buy companies in major industrial sectors such as chipmaking, defense and aerospace. It would dwarf the size of some of the world’s largest buyout funds and rival SoftBank’s $100-billion, tech-focused Vision Fund.

              Bezos was recently appointed co-CEO of Project Prometheus, a new startup that is building AI models that can understand and simulate the physical world. Bezos plans to use the company’s technology to boost the efficiency and profitability of businesses owned by the fund, a playbook that some investment firms are similarly deploying in sectors such as accounting and property management.

              Project Prometheus is separately in talks to raise up to $6 billion in funding, according to people familiar with the matter. It recently appointed David Limp, the CEO of the rocket company Blue Origin, to its board of directors. Bezos founded Blue Origin in 2000 and has contributed billions a year toward the rocket company.

              FT : Tokens may soon drive the AI economy

              Tokens may soon drive the AI economy
              Jensen Huang of Nvidia has outlined a future based around the production, consumption and monetisation of output units

              A new economic reality is starting to take hold in AI. It already underpins the industry’s giant data centres and it will one day become an iron rule for all companies that use machine-generated intelligence.

              That, at least, is according to Jensen Huang, chief executive of Nvidia, who promoted the idea heavily at his company’s main annual tech event this week. His theory helps to make a case for Nvidia’s continued dominance in chips. But it also reveals how far the industry has to go to make a wider case for the technology.

              Huang’s take on AI economics is based around the production, consumption and monetisation of tokens. These are the most basic units of output from large language models: it takes about 1,300 tokens to generate 1,000 words of text. The key metric, he argues, is the cost per token of output. And as the main input into AI-powered services, he adds, tokens translate directly into revenue.

              It is not hard to see why the Nvidia boss wants a nervous Wall Street to focus on token economics. Forget the gargantuan capital spending or the fact that so many competitors are lining up to eat into Nvidia’s fat profit margins, he seems to be saying: as long as his company’s chips keep pumping out tokens at the lowest cost and as long as demand for tokens continues to far outstrip supply, then all is well with the AI boom.

              As a theory of Nvidia’s continued pre-eminence, it sounds compelling. But if token economics is ever to rule the AI world in the way that Huang predicts, some important gaps need to be filled in.

              One is the lack of a clear link between the production of tokens and the creation of value for customers. Just because the cost of tokens is falling doesn’t mean the services created with AI suddenly become valuable or that this will automatically generate revenue across the industry, as Huang suggests.

              Complicating this picture is the fact that newer AI models consume far larger numbers of tokens. The “reasoning” models that emerged late in 2024, starting with OpenAI’s o1, perform far more work to arrive at an answer. These are now being supplemented by agents, which promise to automate white-collar work and bring an explosion in token use — and, by extension, hefty bills for companies that give workers unlimited use of AI.

              Nvidia and the rest of the AI industry have barely scratched the surface when it comes to showing how this will translate into revenue for their customers. In software engineering, which has seen the first widespread use of AI agents, there have been efforts to measure how token use is linked to output and to use this to apportion tokens to workers. Eventually, tech companies dream of AI becoming a core part of employment, with the cost of all white-collar workers coming to be seen as a salary plus a certain number of tokens per month. For now, that is still only a pipe dream.

              The second significant piece missing from Huang’s narrative about an emerging token economy is how the companies that produce tokens, the raw commodity on which all of this depends, will make profits. If these “AI factories” all use Nvidia’s latest chips, then it may be hard for any of them to gain a cost-per-token advantage or retain any pricing power.

              The big price declines that have accompanied the plunging cost of producing tokens seem to bear this out. When OpenAI launched GPT-4 two years ago, for instance, it charged $33 for 1mn tokens. Today, it charges only 9 cents for 1mn tokens produced by its cheapest model. That may be great for customers, but it has fed worries about commoditisation.

              Such worries are hardly new. It is the same argument that was heard in the early days of cloud computing, when Amazon Web Services charged for access to basic data storage and computing power. How could cloud companies ever make a decent profit if computing services were stripped back and sold as commodities like this? The answer was that these were only the first components of what became higher-value services — full-scale computing platforms on which customers could run their businesses. Whether OpenAI and Anthropic will be able to work a similar trick is unclear, but the opportunity before them is clear.

              There may be other explanations for the healthy profit margins in cloud computing. The business is ruled by a small oligopoly. Cloud companies have also faced pressure from regulators to reduce switching costs that may help to pad their profits.

              For now, there is no shortage of competition among frontier AI companies. How that shakes out in future will go a long way to shaping the industry’s profits.