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Amazon Wins Suit Against Perplexity, Blocking AI Shopping Agent
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Perp walk

Perplexity has been left behind in the AI wars

Once touted as a potential Google killer, the AI search engine’s traffic has been flat over the last year, while peers like Claude have surged ahead.

David Crowther, Claire Yubin Oh
Updated 3/12/26 9:19AM

Last summer, Perplexity was hot stuff. The CEO of the AI-powered research tool was on the front cover of Fortune magazine, the company itself was about to make a long-shot $34.5 billion offer to buy Google Chrome from Alphabet, and executives at Apple were warning that traditional internet search was starting to feel the pain as users switched to services like ChatGPT and Perplexity.

But a year later, Perplexity’s progress has stalled out.

Per data from Similarweb, US website traffic to perplexity.ai has been broadly flat, adding fewer than 4 million visitors from February 2025 to February 2026, while rival Claude more than quadrupled its web users.

How did perplexity get left behind in the AI wars
Sherwood News

Naturally, Perplexity lags even further behind the true giants of the GenAI game, ChatGPT and Gemini, which racked up 1 billion and 276 million US monthly website visits, respectively, in February.

Confused, baffled, bewildered

From the start, Perplexity was structured somewhat differently to ChatGPT and others. Its primary function was to answer questions as precisely as possible, with its powerful citation framework making it a research tool for many. It isn’t a frontier model maker, instead relying on some of the very LLMs that it competes against for users.

But as models like ChatGPT, Gemini, and Claude have progressed, what made Perplexity special has become table stakes. Each is now pretty adept at complex, research-based tasks, with Google incorporating Gemini’s real-time AI overviews and ChatGPT adding answers with citations. Anthropic’s success in the enterprise market, targeting professional use cases with Claude, has even shifted the entire industry toward agentic AI — tools that can execute on tasks.

It’s almost hard not to feel sorry for Perplexity, considering the company’s three biggest competitors are: the most well-funded startup in history, Google, and the second-most well-funded startup in history. Throw in a number of bitter lawsuits and legal tensions, including from major news publishers and forums like Reddit, and Perplexity’s offering has become increasingly difficult to execute on and increasingly available elsewhere.

That said, the company has recently launched a suite of new enterprise AI products, including two new AI agents in its bid to remain competitive.

Just yesterday the company unveiled “Personal Computer” — software that will run locally on a dedicated device, per Axios, with full access to local files and a "kill switch" in case users need immediate control. The other — Perplexity Computer, which was announced a few weeks ago — runs in the cloud and is described as able to create and execute "entire workflows, capable of running for hours or even months."

The uptake of those products will be a great litmus test for a swathe of startups that aren't spending billions on their own frontier models.

Note: This article was updated on March 12th to reflect the launch of Perplexity's "Personal Computer."

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DeepSeek releases new V4 series models highlighting efficiency and long context

Chinese AI lab DeepSeek has released a major new version of its eponymous open-source AI models that are nipping at the heels of leading frontier models in some areas.

The most significant DeepSeek-V4 Pro and DeepSeek-V4 Flash both have a 1 million-token context — the amount of information the model can actively work with in a single session — which is a crucial feature for complex, long-running coding tasks.

DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.

Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.

The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”

As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.

But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.

DeepSeek Unveils Flagship AI Model a Year After Breakthrough

DeepSeek Unveils Flagship AI Model a Year After Breakthrough

DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.

Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.

The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”

As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.

But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.

$28.5T
Rani Molla

SpaceX thinks its total addressable market (TAM) is a whopping $28.5 trillion for its businesses, according to an S-1 filing for its upcoming IPO reviewed by Reuters. And most of that market isn’t rockets. The company says roughly 90% could come from AI — largely selling artificial intelligence tools to businesses.

“We believe that our enterprise strategy, which is focused on serving the digital needs of the world’s largest industries with Al solutions, positions us competitively to pursue this rapidly ⁠growing opportunity,” ​SpaceX said in the filing. “We believe we have identified the largest actionable total addressable market in human ​history.”

TAM, of course, assumes capturing every possible customer. But even a small slice of a $28.5 trillion market would be enormous.

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Rani Molla

Tesla Cybercab production has begun

On Tesla’s earnings call earlier this week, CEO Elon Musk said production of the company’s steering-wheel-less Cybercab had begun. Since then, Musk and Tesla have posted videos showing the gold two-seater rolling off the line at its Texas Gigafactory and onto the road.

The Cybercab — meant both for consumers and Tesla’s Robotaxi network — is widely seen as central to the company’s future. “The future of the company is fundamentally based on large-scale autonomous cars and large scale and large volume, vast numbers of autonomous humanoid robots,” Musk said last year.

Whether these cars actually make it to consumers is another question. For now, regulations generally require steering wheels, and Tesla still has to prove the vehicles can reliably drive themselves.

On the earnings call, Musk said production would be “very slow” but would ramp up and go “kind of exponential towards the end of the year and certainly next year.”

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Rani Molla

Meta signs deal to use Amazon Graviton chips

Meta said it will deploy “tens of millions” of Amazon Web Services Graviton CPU cores to power so-called “agentic” AI systems — tools that can reason, plan, and act on their own. The move makes Meta one of the largest customers of Amazon’s in-house chips.

The deal also underscores a broader shift in AI infrastructure, as companies move beyond Nvidia GPUs and use different chips for different tasks.

Meta, which is working on its own custom inference chips, also has chip deals with Advanced Micro Devices and Nvidia.

Meta Partners With AWS on Graviton Chips to Power Agentic AI

Meta Partners With AWS on Graviton Chips to Power Agentic AI

The deal also underscores a broader shift in AI infrastructure, as companies move beyond Nvidia GPUs and use different chips for different tasks.

Meta, which is working on its own custom inference chips, also has chip deals with Advanced Micro Devices and Nvidia.

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Rani Molla

Oracle rises after Wedbush’s Dan Ives calls the stock a buy with 25% upside

Oracle extended its premarket gains Friday after Wedbush Securities’ Dan Ives initiated coverage with an “outperform” rating and a $225 price target — about 25% upside to its pre-initiation level — calling the enterprise software and cloud infrastructure company a “foundational infrastructure provider for the AI revolution.”

Ives argues investors are misreading Oracle’s heavy capital spending and negative free cash flow as risky, despite being backed by a massive $553 billion backlog of contracted demand. He says the company’s “secret sauce” is a two-part strategy: building high-performance cloud infrastructure for AI workloads while connecting those models directly to companies’ own data.

“We believe Oracle is in the early innings of a significant repositioning as it executes on this generational opportunity,” Ives wrote.

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