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Nvidia Appeals to Trump With a $5 Billion Intel Stake
Product Reviews

Nvidia Appeals to Trump With a $5 Billion Intel Stake

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Nvidia became one of competitor Intel’s largest shareholders on Thursday with a $5 billion stake. The two companies will collaborate to jointly develop PC chips and data centers.

Once the dominant name in chips, Intel had been in a steady decline in recent years as it failed to keep up with peers like Nvidia in the AI race.

The company started its official turnaround strategy last year by ousting its previous CEO Pat Gelsinger, who was eventually replaced by Lip Bu-Tan a few months later.

Last month was a whirlwind for both Bu-Tan and Intel: Trump initially called for the CEO’s immediate resignation, citing “conflicts of interest” due to alleged ties to China. Only a few days of lobbying after, Trump not only walked back his words and called Bu-Tan “a success” but also announced a deal that would have the U.S. government take a 10% stake in Intel.

Announcing the deal on Truth Social, Trump said that the “United States of America now fully owns and controls 10% of INTEL.”

That investment, it turns out, was the real comeback for Intel. In its effort to become more competitive in the chips space, Intel now has the help of Nvidia, the biggest name in AI chips. The announcement sent Intel’s stock soaring by more than 30%.

There’s likely more in it for Nvidia than just the stake. The deal is well-positioned to put Nvidia in favor with the government, crucial as the company tries to convince Trump to allow it to sell more advanced chips to China.

Nvidia has been on a months-long rollercoaster ride when it comes to its chip sales in China. The company had been selling lower-tech China-special chips to the region under Biden-era export controls. Trump imposed a blanket ban on all sales in April as part of a greater trade escalation between the two countries.

It took Nvidia CEO Jensen Huang a months-long schmoozing effort to get Trump to undo his ban. The effort involved a $500 billion investment in U.S. manufacturing, a trip with Trump to the UAE to announce a data center deal, and agreeing to give the government a 15% cut of the company’s revenue coming from chip sales in China.

Since the ban got lifted, Huang has been vocal about his desire to sell a higher-tech chip called B30A to China. It’s not yet certain whether Trump will allow that. Or where China stands on it, either.

Beijing is not happy about getting downgraded Nvidia chips and made that clear on Wednesday when the Financial Times reported that the Chinese government had banned the low-grade RTX Pro 6000D chip in China, one of the two chips that Nvidia is allowed to sell there. Instead of relying on Nvidia chips, Beijing now allegedly has confidence that domestic chip offerings are at par with Nvidia’s China-specific ones.

A Trump-led tech industry

Trump’s influence in tech is undeniable. Only eight months into his presidency, CEOs of major tech companies have shown solidarity with him time and again, from standing front stage at the inauguration to promising billions of dollars of investments and accompanying the President on state visits.

For example, Trump flew to meet with the UK Prime Minister on Tuesday and has been there since. His state visit to the UK included billions of dollars’ worth of UK investment deals announced by American big tech giants Nvidia, Google, Microsoft, OpenAI, and Salesforce. The trip is not even done yet.

Trump has so far responded in kind, advocating for these tech giants’ interests in diplomatic conversations, sometimes at the risk of disrupting ties with long-term American allies like the EU. The Trump administration has also dropped a third of all investigations into big tech.

The developments have raised some concern as the close ties between these tech CEOs and the Trump administration have been dubbed online as a “tech broligarchy.”



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Product Reviews

NVIDIA throws Intel a $5 billion lifeline to build PC and data center CPUs

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NVIDIA has today announced it will invest $5 billion in Intel as part of a new collaboration between the two companies. In a statement, NVIDIA said it would work with its ailing rival to “jointly develop multiple generations of custom data center and PC products.”

The partnership will focus on marrying NVIDIA’s class-leading GPU and AI chips with Intel’s ailing x86 CPUs. That includes Intel building “NVIDIA-custom x86 CPUs” for integration with the latter company’s AI products.

PC users, meanwhile, should expect to see Intel building and / or selling x86 chips that integrate NVIDIA’s RTX GPU chiplets. It’s not clear if this means the end of Intel’s in-house graphics silicon or if these products will focus on broadening access to NVIDIA’s high-end GPU technology.

The statement includes personal remarks from both NVIDIA CEO Jensen Huang, who says the deal “tightly couples” Intel’s x86 CPUs with NVIDIA’s AI technology. Intel CEO Lip-Bu Tan, meanwhile, says the deal will combine its CPU know-how, its “process technology, manufacturing and advanced packaging capabilities” with NVIDIA’s.

The partnership is interesting for a wide variety of reasons, including the fact few companies have opted to go with Intel’s foundry business to actually build chips. And that the momentum in the chip space has been pointed away from Intel for several years after several high-profile stumbles.

This breaking news story is developing, please refresh for more information.



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Nvidia CEO Jensen Huang speaking to journalists in China.
Product Reviews

Alibaba’s AI chip goes head-to-head with Nvidia H20 in state-backed benchmark demo

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Alibaba’s semiconductor unit, T-Head, has reportedly developed a new AI processor that it claims matches the performance of Nvidia’s H20 — the GPU built specifically for the Chinese market that’s currently stuck in geopolitical purgatory.

The demonstration aired Tuesday, September 16, on China Central Television (CCTV), during a broadcast covering Premier Li Qiang’s visit to China Umicom’s Sanjiangyuan Energy Intelligent Computing Centre in Qinghai. In the segment, T-Head’s new “PPU” accelerator was directly compared with Nvidia’s H20 and A800, as well as Huawei’s Ascend 910B, with a chart implying performance parity between the Alibaba and Nvidia parts.

The chip, an ASIC designed for AI workloads, features 96 GB of HBM2e, 700 GB/s chip-to-chip interconnect, PCIe support, and 400 W board power, according to the on-screen specs as reported by South China Morning Post. While the broadcast didn’t disclose the specifics of the testing methodology used or publish raw figures, it’s the first public benchmark placing Alibaba’s hardware in the same class as Nvidia’s datacenter GPUs.

According to Reuters, China Unicom has already deployed 16,384 of Alibaba’s PPU cards across its infrastructure, accounting for more than half of the almost 23,000 domestic accelerators currently installed at the Qinghai facility. Together, the cards deliver 3,579 petaflops of compute, with the site expected to scale to more than 20,000 petaflops once all phases are complete.

There’s just as much geopolitical context behind the CCTV demonstration as there is technical. Nvidia’s H20 was introduced to comply with U.S. export controls limiting the sale of high-performance silicon to China. Built on Hopper architecture but cut down to meet restrictions, the H20 ships with 96 GB of HBM3 and roughly 4.0 TB/s of memory bandwidth. That lends some perspective to Alibaba’s matching 96 GB HBM2e capacity, though not necessarily its real-world performance.

The biggest unknown right now is on the software side. While Alibaba is understandably eager to show it can meet AI hardware needs in-house, the company has not disclosed details about frameworks, toolchains, or compatibility with existing model stacks. Until independent benchmarks and developer support materialize, the PPU’s parity with Nvidia’s hardware is just a claim backed by Chinese state TV and endorsed by the Chinese government.

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Nvidia CEO Jensen Huang Is Bananas for Google Gemini’s AI Image Generator
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Nvidia CEO Jensen Huang Is Bananas for Google Gemini’s AI Image Generator

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Nvidia CEO Jensen Huang is in London, standing in front of a room full of journalists, outing himself as a huge fan of Gemini’s Nano Banana. “How could anyone not love Nano Banana? I mean Nano Banana, how good is that? Tell me it’s not true!” He addresses the room. No one responds. “Tell me it’s not true! It’s so good. I was just talking to Demis [Hassabis, CEO of DeepMind] yesterday and I said ‘How about that Nano Banana! How good is that?’”

It looks like lots of people agree with him: The popularity of the Nano Banana AI image generator—which launched in August and allows users to make precise edits to AI images while preserving the quality of faces, animals, or other objects in the background—has caused a 300 million image surge for Gemini in the first few days in September already, according to a post on X by Josh Woodward, VP of Google Labs and Google Gemini.

Huang, whose company was among a cohort of big US technology companies to announce investments into data centers, supercomputers, and AI research in the UK on Tuesday, is on a high. Speaking ahead of a white-tie event with UK prime minister Keir Starmer (where he plans to wear custom black leather tails), he’s boisterously optimistic about the future of AI in the UK, saying the country is “too humble” about the country’s potential for AI advancements.

He cites the UK’s pedigree in themes as wide as the industrial revolution, steam trains, DeepMind (now owned by Google), and university researchers, as well as other tangential skills. “No one fries food better than you do,” he quips. “Your tea is good. You’re great. Come on!”

Nvidia announced a $683 million equity investment in datacenter builder Nscale this week, a move that—alongside investments from OpenAI and Microsoft—has propelled the company to the epicenter of this AI push in the UK. Huang estimates that Nscale will generate more than $68 billion in revenues over six years. “I’ll go on record to say I’m the best thing that’s ever happened to him,” he says, referring to Nscale CEO Josh Payne.

“As AI services get deployed—I’m sure that all of you use it. I use it every day and it’s improved my learning, my thinking. It’s helped me access information, access knowledge a lot more efficiently. It helps me write, helps me think, it helps me formulate ideas. So my experience with AI is likely going to be everybody’s experience. I have the benefit of using all the AI—how good is that?”

The leather-jacket-wearing billionaire, who previously told WIRED that he uses AI agents in his personal life, has expanded on how he uses AI (that’s not Nano Banana) for most daily things, including his public speeches and research.

“I really like using an AI word processor because it remembers me and knows what I’m going to talk about. I could describe the different circumstance that I’m in and yet it still knows that I’m Jensen, just in a different circumstance,” Huang explains. “In that way it could reshape what I’m doing and be helpful. It’s a thinking partner, it’s truly terrific, and it saves me a ton of time. Frankly, I think the quality of work is better.”

His favorite one to use “depends on what I’m doing,” he says. “For something more technical I will use Gemini. If I’m doing something where it’s a bit more artistic I prefer Grok. If it’s very fast information access I prefer Perplexity—it does a really good job of presenting research to me. And for near everyday use I enjoy using ChatGPT,” Huang says.

“When I am doing something serious I will give the same prompt to all of them, and then I ask them to, because it’s research oriented, critique each other’s work. Then I take the best one.”

In the end though, all topics lead back to Nano Banana. “AI should be democratized for everyone. There should be no person who is left behind, it’s not sensible to me that someone should be left behind on electricity or the internet of the next level of technology,” he says.

“AI is the single greatest opportunity for us to close the technology divide,” says Huang. “This technology is so easy to use—who doesn’t know how to use Nano?”





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Are SLMs the future of AI? Nvidia researchers think so. Here's why - 1
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Nvidia researchers call SLMs the future of AI: Here’s why

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Experts at Nvidia claim that Small Language Models (SLMs) are key to the future of the artificial intelligence (AI) sector.

However, most investments are still being made into Large Language Models (LLMs). If this situation persists, the industry may slow down and subsequently dent the U.S. economy. 

Summary

  • Most AI investors are attracted to companies working on LLM-based products.
  • SLM agents are cheaper and often more efficient for specific tasks than LLMs. 
  • Nvidia calls SLMs the future of AI and urges companies to work with smaller models.

SLMs vs. LLMs

SLMs are trained on up to 40 billion parameters, excelling at a narrow set of specified tasks while consuming significantly less resources. In other words, they’re cheaper.

LLMs are expensive. In April, OpenAI CEO Sam Altman famously said that his company’s flagship product, ChatGPT, costs OpenAI tens of millions of dollars when users say “please” and “thank you. It gives a clue to the costliness of LLMs. That’s where SLMs steal the show since they don’t require expensive data centers to complete tasks.

SLMs, for instance, can serve as client support chatbots and don’t need to learn much about a variety of topics.

According to a Nvidia research paper released in June, SLM agents are the future of AI, not LLM agents:

“…small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI.” 

LLMs also help to train SLMs so they don’t have to absorb all the data from scratch. They learn from large models efficiently and quickly, and become almost as good at solving specific tasks without having to spend many resources.

The tiniest language models are trained on one billion parameters and can operate on regular CPUs. 

Companies don’t need virtual human beings with encyclopedic knowledge. Instead, they need tools that solve certain tasks quickly and precisely.

That’s why cheap SLM agents are much more lucrative investments than LLMs. Notably, GPT-5 uses several models, including small ones, depending on specific tasks. 

What happens if an AI sector takes a setback?

Crypto and blockchain firms are increasingly leveraging LLMs to streamline operations and enhance decision-making. DeFi platforms like Zignaly use LLMs to summarize trades and manage social investment insights, while infrastructure firms such as Platonic and Network3 employ them to support developers and optimize on-chain workflows.

Trading firms are also combining LLMs with other AI tools for market intelligence and predictive analytics.

But the biggest projects are Google’s Gemini, OpenAI’s GPT, Anthropic’s Claude, and xAI’s Grok. Each one requires massive data centers (a lot of electricity) and a ton of capital. 

The AI sector in the U.S. raised $109 billion in investments in 2024 alone. This year, American AI companies have already spent $400 billion on infrastructure. In August, it was reported that OpenAI is seeking to sell $500 billion worth of its stock. According to Morgan Stanley’s Andrew Sheets, AI companies may spend $3 trillion on data centers by 2029.

According to IDC Research, by 2030, each dollar spent on AI-based business solutions will bring $4.6 to the global economy.

Yet, a problem lingers. If there aren’t enough data centers being built, it may have a substantial impact on the economy and scare off big investors. Once investors reduce their allocations in AI companies, spending will decrease. 

The slowdown of AI companies using LLMs may be caused by factors such as troubled electricity supplies, high interest rates, a trade war, and growing demand for SLMs, among other reasons.

What’s worse, some note that inflating the data centers creates a bubble, and it’s not as lovely as the dotcom era that helped to propel the Internet to new highs. The problem with data centers is that they use chips that will eventually become obsolete.

It will take only a few years. Thus, while these chips are costly, they won’t be reused for other purposes.

How to avoid collapse

To avoid the collapse, Nvidia researchers recommend that AI companies opt for using SLMs and boost the specialization of SLM agents.

Such an approach will help to save resources and increase efficiency and competitiveness.

Researchers suggest that creating modular agent systems will help to keep flexibility and use LLMs only for complex reasoning.  



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OpenAI is reported as Broadcom’s fourth XPU customer, joining Google, Meta and ByteDance in designing chips to reduce reliance on Nvidia

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  • OpenAI (probably) joins Google, Meta and ByteDance in Broadcom’s custom ASIC partnership
  • Broadcom secures $10 billion AI rack orders as Nvidia faces new rivals
  • Nvidia’s largest customers pursue in-house chips with Broadcom guiding the transition

As we’ve reported more than a new times in the past, the AI hardware market is changing, with some of Nvidia’s biggest customers looking for ways to cut costs and gain more control over their systems.

Rather than relying solely on Nvidia’s costly GPUs, companies are beginning to design their own ASICs tailored to their workloads.

Broadcom is one of the bigger players in this space, offering the expertise needed to turn those custom designs into production-ready chips and systems.


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Reporting on Broadcom’s financial results for its third quarter, The Next Platform says the silicon supplier has now secured a fourth customer for its custom XPU program, adding to partnerships with Google, Meta, and ByteDance.

Industry reports and timing, suggest this newest client is OpenAI, which is developing its own inference processor known as Titan under the leadership of Richard Ho, a former Google TPU engineer.

Nvidia still dominates the market of course – by some way – with its Blackwell GB300 NVL72, but deploying such rackscale systems is expensive, and firms with massive AI models want hardware designed to better match their needs.

Custom ASICs are seen as a way to rein in costs while offering greater flexibility than an off-the-shelf GPU and Broadcom is well positioned to guide complex accelerator projects through design, production, and packaging.

On a call with Wall Street analysts, chief executive Hock Tan expanded on Broadcom’s unnamed fourth client (cough, OpenAI, cough), saying, “Now further to these three customers, as we had previously mentioned, we have been working with other prospects on their own AI accelerators.”

“Last quarter, one of these prospects released production orders to Broadcom, and we have accordingly characterized them as a qualified customer for XPUs and, in fact, have secured over $10 billion of orders of AI racks based on our XPUs,” Tan continued.

“And reflecting this, we now expect the outlook for our fiscal 2026 AI revenue to improve significantly from what we had indicated last quarter.”

That $10 billion figure refers to complete AI rack systems, not Broadcom’s share for the underlying chip design.

Revenue from those orders is scheduled to begin in the third quarter of fiscal 2026.

It’s clear that Nvidia’s biggest buyers are no longer content to depend solely on GPUs, and by investing in ASICs they are betting that custom hardware will bring efficiency and control. With its expertise, Broadcom is positioning itself as the company that can make those designs a reality.

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Nvidia Is Not Happy With the Gain AI Act, Says As Much

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In a move drawing considerable attention across the tech industry, Nvidia Corporation has publicly critiqued the recently proposed Gain AI Act, emphasizing its potential to stifle competition in the rapidly evolving artificial intelligence sector.

The GAIN AI Act, which stands for Guaranteeing Access and Innovation for National Artificial Intelligence Act, was introduced as part of the U.S. National Defense Authorization Act, with the goal of ensuring that the United States is the dominant market force for AI.

It has not yet passed and remains a hotly debated policy topic both here and abroad because of the restrictions it looks to enact.

Backers say it aims to protect American market interests by prioritizing domestic orders for advanced AI chips and processors, as well as secure supply chains for critical AI hardware, and theoretically reduce our reliance on foreign manufacturers.

So it’s no huge surprise that Nvidia, a Chinese corporation and currently the world’s biggest company, would take aim at a law that might potentially restrict the competitiveness of foreign technology.

The company said as much during a recent industry forum.

“We never deprive American customers in order to serve the rest of the world. In trying to solve a problem that does not exist, the proposed bill would restrict competition worldwide in any industry that uses mainstream computing chips,” an Nvidia spokesperson said.

Is the Gain AI Act a good idea for innovation?

It depends on who you ask.

Essentially, the law seeks to strengthen national security and economic competitiveness by ensuring that key AI components remain accessible to American companies and government agencies before they are supplied abroad.

Its language takes a hard line on what the priority should be for the United States government.

“It should be the policy of the United States and the Department of Commerce to deny licenses for the export of the most powerful AI chips, including such chips with total processing power of 4,800 or above and to restrict the export of advanced artificial intelligence chips to foreign entities so long as United States entities are waiting and unable to acquire those same chips,” the legislation reads.

Nvidia’s critique reflects broader industry anxieties about regulatory environments that might hinder innovation. As global competition intensifies, particularly with formidable advances in AI from regions such as China, firms like Nvidia are closely watching how regulatory frameworks are taking shape abroad.

But it’s not just foreign companies. American market players, too, have said it could hit many domestic operations hard.

“Advanced AI chips are the jet engine that is going to enable the U.S. AI industry to lead for the next decade,” Brad Carson, president of Americans for Responsible Innovation (ARI), a lobbying group for the AI industry, said in a widely distributed statement.

“Globally, these chips are currently supply-constrained, which means that every advanced chip sold abroad is a chip the U.S. cannot use to accelerate American R&D and economic growth,” Carson said. “As we compete to lead on this dual-use technology, including the GAIN AI Act in the NDAA would be a major win for U.S. economic competitiveness and national security.”

‘Doomer science fiction’

Nvidia didn’t stop there. It then took aim at an earlier attempt to make the U.S. more competitive in the chipmaker market, a policy called the AI Diffusion Rule, which ultimately failed.

The company minced no words in a follow-up statement, saying that the past attempts by legislators to control market forces based on protectionist policies was ultimately a bad idea.

“The AI Diffusion Rule was a self-defeating policy, based on doomer science fiction, and should not be revived,” it read.

“Our sales to customers worldwide do not deprive U.S. customers of anything—and in fact expand the market for many U.S. businesses and industries,” it said. “The pundits feeding fake news to Congress about chip supply are attempting to overturn President Trump’s AI Action Plan and surrender America’s chance to lead in AI and computing worldwide.”

The challenge will be creating laws that are as dynamic as the technologies they aim to govern, fostering a climate where innovation and ethical accountability are not mutually exclusive, but rather mutually reinforcing.

We’ve tried this before

Nvidia’s mention of the AI Diffusion rule was no accident. That ill-fated policy had many of the same political goals but ultimately stumbled at the finish line and was a relatively toothless attempt to rein in some of the world’s most competitive companies.

The Biden administration’s AI Diffusion rule, enacted in January 2025, represented a significant shift in U.S. export controls targeting cutting-edge artificial intelligence technology.

Designed to curb the spread of advanced AI tools to rival nations, the regulation mandated licensing for the sale of high-end AI chips and imposed strict caps on computing power accessible to foreign recipients. Its goal was to slow the diffusion of sensitive AI capabilities that could enhance military or strategic applications abroad.

However, the Trump-era approach to export controls, which focused on a more targeted, bilateral framework, was poised to replace the Biden administration’s broader strategy.

President Trump had announced plans to rescind the AI Diffusion rule, criticizing it as overly bureaucratic and potentially hindering U.S. innovation. Instead, his administration favored engaging in country-specific agreements to control export practices, aiming for a more adaptable, case-by-case approach.

Though the AI Diffusion rule was ultimately rolled back, the Bureau of Industry and Security (BIS) signaled a renewed emphasis on enforcing existing regulations. The agency issued a notice reinforcing actions against companies with a “high probability” of violations, warning that increased scrutiny would be applied to entities with knowledge of potential breaches.

Whether this latest attempt to advance American interests meets a similar fate remains to be seen.



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Apple, Nvidia, Amazon Among Mag 7 Exposure Coming to Coinbase

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Coinbase Derivatives said it will introduce a new type of equity index futures contract later this month, offering investors exposure to both leading U.S. technology stocks and cryptocurrency exchange-traded funds (ETFs) in a single product.

Launching Sept. 22, the Mag7 + Crypto Equity Index Futures will be the first U.S.-listed derivatives contracts to combine traditional equities with digital assets, according to a blog post.

The move, said the company, marks expansion beyond single-asset derivatives into multi-asset offerings designed to give investors thematic exposure to innovation and growth sectors.

The new index includes ten components weighted equally at 10% each. It consists of the so-called “Magnificent 7” stocks — Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta and Tesla — along with Coinbase’s own stock and two crypto ETFs: BlackRock’s iShares Bitcoin Trust (IBIT) and iShares Ethereum Trust (ETHA). MarketVector, known for its crypto and thematic indexes, will serve as the official index provider.

Contracts will be monthly and cash-settled, with each representing $1 multiplied by the index level. At an index value of $3,000, for example, the notional value of one contract would be $3,000. The index will be rebalanced quarterly to restore equal weighting across all components.

Coinbase framed the product as a way for investors to manage multi-asset risk more efficiently while gaining exposure to both sides of the innovation economy — Silicon Valley tech leaders and blockchain-native assets.

“Equity index futures mark the next evolution of our product suite and pave the way for a new era of multi-asset derivatives,” the company said in its announcement.

The launch comes amid growing investor appetite for crossover products that bridge traditional finance and crypto markets. Coinbase said it plans to expand availability of the contracts to retail users in the months ahead, though they will initially trade on partner platforms.



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‘There’s no such thing as a laser-resistant weed’ says company employing dozens of Nvidia GPUs to fry weeds with lasers from above

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Do farmers traditionally wear sunglasses? If so, Alex Sergeev, the chief technology officer of Carbon Robotics, presumably took them off when telling Nvidia, “There’s no such thing as a laser-resistant weed.”

As reported by Tom’s Hardware, the LaserWeeder from Carbon Robotics is a module you attach to a tractor that uses dozens of Nvidia GPUs to snipe out weeds as it drives by.

This means farmers can avoid any chemicals to remove weeds, and opt for the natural approach of, well, blasting them with highly technological lasers. Ah, just like my grandma used to do.


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Nvidia’s Jetson Thor, a Blackwell-based AI module, is being made available for the mass market, and Nvidia is currently showing off a few of the 2 million developers + working with it. The LaserWeeder G2 is Carbon Robotics’ new 12-module device, and each module has two GPUs. That means 24 GPUs are in each G2. That’s gotta be at least enough power for a LAN game of Counter-Strike 2.

The Nvidia blog says this “compute muscle lets it identify and incinerate up to 10,000 weeds per minute.” Notably, the machine also gathers that data and then feeds it back to a labelling tool, in order to contribute to an image data set of over 65 million images. The data harvested (get it?) then continues to train further generations of models to more efficiently whack the weeds (or perhaps I should say ‘blast them from above’).

Despite the pretty scary trailer music, it does seem like a pretty neat device, and being able to analyse pictures of weeds for better recognition in the future means that it can get better with time. Or, at least a robot shooting highly targeted lasers seems like a good idea now. I think there may be movies, books, games, etc, warning about this somewhere…

The LaserWeeder reportedly stemmed from a conversation founder Paul Mikesell had with a farmer. Weed control was seemingly their single biggest problem. To tie to this, and as appears to be the prerogative of AI creators right now, the next step from here is the Carbon AutoTractor, “an autonomous retrofit for existing machines.”

As pointed out by Nvidia, more than 25% of edible crops in the US aren’t harvested due to the lack of labour. The AutoTractor is designed to patrol fields, but also allows for easy human takeover “if, say, a deer wanders into the field.” It’s also worth noting that any efforts made to remove powerful chemicals from farming is probably a good thing.

According to Nvidia, Carbon Robotics has rolled out over 150 LaserWeeders since its inception in 2018 and has managed to take out over 30 billion weeds. I assume AI was also responsible for counting this figure. The page for the G2 600 says it “Shoots 7,500+ weeds” every minute and “Outperforms a hand crew of 75 people”. Sorry, weeds, you never stood a chance.

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Bitcoin, Solana Rise as Investors Weigh Nvidia Earnings, Strong GDP Data

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In brief

  • Nvidia disclosed $46 billion in second-quarter profit.
  • The U.S. economy grew at an annual rate of 3.3% in Q2.
  • Solana was up 2.3%, as one analyst pointed to interest from treasury firms.

The price of Bitcoin and other cryptocurrencies rose on Thursday as investors mulled the strength of Nvidia’s earnings and signs of a stronger-than-expected U.S. economy.

Bitcoin changed hands around $113,000, a 0.9% increase over the past day, according to crypto data provider CoinGecko. Solana meanwhile rose 2.3% to $212. SOL was up nearly 5% at one point before retreating. 

Ethereum and XRP dropped dropped 2.7% and 0.8%, respectively. ETH was trading near $4,500, well off its all-time high set over the weekend.

Nvidia disclosed record profits on Wednesday alongside its ninth straight quarter of year-over-year revenue growth of over 50%. The company took in $46 billion during the second quarter, despite not selling any of its advanced AI chips to China over the period.

The chipmaker’s shares fell 1.3% on Thursday to $179, according to Yahoo Finance. They are still up 2.6% on the week and 34% year-to-date, signaling that conviction in artificial intelligence is continuing to drive sky-high valuations on Wall Street.

For Bitcoin, Nvidia’s fortunes are relevant. The chipmaker has an 8.8% weighting in the S&P 500, so any swing in the $4.4 trillion company’s stock price could affect the market’s top cryptocurrency by market value, given the correlation between crypto and equities.

The U.S. Commerce Department said on Thursday that gross domestic product rose at an annualized rate of 3.3% in the second quarter. Economists initially expected the U.S. economy would grow at a 3.0% annualized rate, suggesting that the U.S. economy performed better under the president’s trade zig zags on tariffs and other trade policies.

Solana’s performance was notable on Thursday, considering that the latest rally in crypto prices has been marked by Ethereum’s strength and climb to a new all-time high.

Since Aug. 10, however, Solana has shown “relative strength” against Bitcoin and Ethereum, with price ratios recovering from recent lows, according to Jake Ostrovskis, an OTC transfer at the crypto market maker Wintermute.



The cryptocurrency has returned to focus amid “growing interest in treasuries targeting the asset,” Ostrovskis said. Earlier this week, The Information reported that venture capital firm Pantera Capital is seeking to raise $1.25 billion for a Nasdaq-listed vehicle that would hold Solana.

Solana treasury firms have the potential to absorb defunct crypto exchange FTX’s vesting supply of tokens, which equates to around 609,000 SOL each month. The bankrupt exchange started making repayments to customers in February.

“By converting this ‘overhang’ into staked, treasury-held assets, effective circulating supply shrinks, countering downward pressure and setting the stage for sustained upside,” Ostrovskis said.

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