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Analyst Charts Ethereum Long-Term Roadmap To $16,000 – There’s No Need To Panic

LSD News Szerkesztőség — Tue, 09 Jun 2026 07:03:16 +0000

Ethereum’s crash below $1,500 over the weekend has pushed sentiment into one of its most fearful phases since the previous bear market, but crypto analyst Crypto Patel believes the current selloff should be viewed through a longer lens. The analyst’s roadmap places ETH inside a broad accumulation range, with the chart showing that the same movement as previous Ethereum tops and bottoms is still playing out, and Ethereum might be declining into an accumulation zone.

Ethereum Enters Panic Zone As Price Revisits $1,500

Ethereum’s weekend drop has brought ETH close to $1,500, extending a painful correction that has already erased a large part of the gains since its August 2025 ATH. Recent market data from TradingView shows ETH briefly touched $1,505 on Saturday, June 6, during a crypto market-wide selloff, a move that has increased panic among traders, as evidenced by various posts on social media platforms.

Long-Term Roadmap To $16,000

Ethereum’s full price history, viewed through an Elliott Wave structure, shows the 2017 and 2021 peaks as major cycle tops within two separate cycles. The current price action is classified as a Wave 4 correction in a five-impulse wave count that started after the 2021 top. Wave 4 is a correction to a major accumulation point before a projected Wave 5 expansion phase into 2026 and 2027.

JK Tyre charts Rs 4,900 crore capex to scale up capacity by 24% through FY30

LSD News Szerkesztőség — Tue, 26 May 2026 23:25:56 +0000

JK Tyre & Industries Ltd has earmarked Rs 4,900 crore towards capital expenditure through FY30 as India’s third-largest tyre maker by revenue looks to significantly boost capacity.

Raghupati Singhania, chairman and managing director, said the capex outlay would help the company expand capacity by nearly a fourth—signalling a major bet on sustained demand across passenger and commercial vehicle segments. Singhania was speaking on a media call to discuss quarterly and annual financial results.

JK Tyre posted robust performance for the March quarter and FY26. Net profit surged 94% to Rs 199 crore in the three months ended March. Consolidated revenue rose 12% to Rs 4,233 crore. Ebitda grew 42% to Rs 546 crore, with margins expanding to 12.9%.

For FY26, net profit accelerated 52% to Rs 786 crore. Consolidated revenue touched a record Rs 16,384 crore, up 11%, while Ebitda rose 25% to Rs 2,089 crore and margins at 10.8%.

The latest expansion plan—spread over three phases through December 2029—will be weighted heavily towards Chennai, with about 90% of the investment deployed in the facility there, primarily to expand passenger car radial (PCR) capacity. The remainder will go towards expanding truck-bus radial (TBR) capacity in the Mysore facility.

“We have planned this expansion because we are seeing future demand…we expect demand generation to be quite healthy,” said Singhania.

JK Tyre is already in the midst of Rs 1,130 crore expansion for PCR and TBR capacity, set to be go onstream by the December quarter, with its plants running at 95% utilisation.Premium tyres—rims of 16 inches and above—currently account for 35% of the mix, up from 26%, with a target to cross 50% post-expansion.

The company meanwhile continues to battle headwinds like other tyre makers in the country. The West Asia conflict has increased raw material prices by 15–20% in the current quarter. JK Tyre has raised prices by 4–5% in the replacement market, and is preparing for another round of increase of around 6%. “Even then, the Ebitda margin will be impacted this quarter because the rise (in raw material prices) is very sharp,” Singhania cautioned.

Ethereum Looks To Bottom Against Bitcoin: What The Charts Are Saying | Bitcoinist.com

LSD News Szerkesztőség — Fri, 03 Apr 2026 13:40:28 +0000

Trusted Editorial content, reviewed by leading industry experts and seasoned editors. Ad Disclosure

Ethereum has spent the better part of recent months losing ground to Bitcoin, and this underperformance may now be approaching a turning point, at least according to a new technical outlook shared by crypto analyst CrediBULL Crypto. The technical analysis shows that the ETH/BTC pair is no longer breaking down and is now quietly settling down at a level that has always led to sell-off exhaustion in the pair.

ETH/BTC Holds Range Lows As Selling Pressure Fades

The ETH/BTC 12-hour chart tells a story that has been unfolding since July 2025 and is now nearing a completion. The ETH/BTC chart shows a pair that has spent recent months grinding lower before finally reaching a support zone. As shown in the chart below, the ETH/BTC ratio has been in a sustained decline for the past few years from a peak near 0.0420 in mid-2025, which the analyst labels as wave 5 of a completed five-wave impulse.

The ratio worked its way down through a series of lower highs and lower lows throughout the second half of 2025 and January 2026. However, it has been compressed between February and March into what looks like a macro support zone between approximately 0.02143 and 0.02626.

This support was noted by CrediBULL Crypto as being important in this context, with the analyst pointing out with confidence that the ETH/BTC pair is bottoming here and is in a final stage preceding a true breakout from the current range.

Ethereum/Bitcoin Chart. Source: @CredibleCrypto On X

Reclaim Of Range Could Cause A 20% Outperformance Move

The Elliott Wave labeling on the chart frames the current structure on the ETH/BTC pair as a (w)-(x)-(y) correction after the previous five-wave impulse that peaked in mid-2025. Wave (w) has played out in full, and the projection is a wave (x) move that should see the Ethereum price going on a 20% move up on the Bitcoin price.

The most important step in this projected move is reclaiming the previous range lows around 0.0308-0.031, which have now flipped into resistance. Failure to reclaim the level would likely delay this scenario, but the current price action has been characterized by repeated attempts to push higher.

Switching to the ETH/USD 30-minute chart, the analyst overlays a Wyckoff Accumulation schematic to the current price action. The Ethereum/USD chart complements the ETH/BTC outlook, showing price trading in a range just above $2,000. This is above a notable support level around the $1,900-$1,950 range, where multiple reactions have occurred.

Ethereum Price Chart. Source: @CredibleCrypto

There’s also a pink resistance zone above, which is around roughly $2,120 to $2,200. CrediBULL Crypto’s projection, illustrated by the green arrows, envisions a brief retest of support below $1,900 before an upside resolution that pushes the ETH price above the pink resistance zone to $2,400 and maybe higher.

ETH bears continue to push down | Source: ETHUSDT on Tradingview.com

Featured image created with Dall.E, chart from Tradingview.com

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These four charts show how reliant Europe is on U.S. digital infrastructure

LSD News Szerkesztőség — Fri, 13 Feb 2026 09:15:30 +0000

As geopolitical tensions between the EU and the U.S. escalate, these charts show how reliant the continent is on American tech providers, despite pledges to become more independent.

Since returning to the White House last year, U.S President Donald Trump imposed tariffs on the continent and caused headaches and fear in Europe as he initially refused to rule out military action to acquire Greenland, a semi-autonomous Danish territory, before backtracking.

With the long-standing transatlantic alliance looking uncertain, European governments are increasingly moving to develop digital autonomy. Critics of Europe’s reliance on U.S. companies for digital infrastructure warn that U.S. law enforcement can request user data from American companies, regardless of where the data is stored, as part of the Cloud Act.

But tech providers from the other side of the Atlantic still dominate in Europe.

European cloud providers have been steadily losing ground to U.S. rivals over the past nine years, holding under 15% of the market in 2025, according to data from Synergy Research Group, a market analytics company.

“It will be incredibly difficult for European cloud providers to meaningfully reverse the market share trend,” John Dinsdale, the group’s chief analyst, told CNBC.

“This is a game of scale. In order to be a leading player, you have to be continually investing large amounts in research, service development, technical infrastructure, customer support and channel partners,” he added. “You also have to have the brand recognition and ability to operate globally or at least across multiple geographies.”

Amazon, Microsoft and Google control more than 70% of the European cloud market, while the European companies with the largest share of the market are German duo SAP and Deutsche Telekom, with 2% each, per Synergy data.

“If you were able to go back 10 years and rewrite history, maybe one or two European companies could and should have set out their stall to be a leading player in the cloud market, but they didn’t,” said Dinsdale.

Amazon gained a huge early mover advantage by acting first in the market, he said, adding that Microsoft and Google followed at a distance. “Oracle eventually got serious about the cloud and is now growing rapidly, and neocloud companies are targeting specific services with some success.”

While SAP commands the biggest share of Europe’s enterprise software market, at least 59% is held by U.S companies, according to data from a December report by the European Parliament, with Oracle and Microsoft controlling 18% and 10%, respectively. The enterprise software market data refers to Europe as a whole, including non-EU members such as the UK and Switzerland.

Many political leaders are now “looking at technology in a way to gain sovereignty,” SAP CEO Christian Klein told CNBC’s “Squawk Box Europe” on Friday, in terms of “not only where do we store the data and how we manage the data, but also how to gain sovereignty on the software side.”

Customer relationship management software is a sector dominated by a single player in Salesforce, with SAP taking second spot, data from the European Parliament report, referring just to the 27 members of the European Union, showed.

Nvidia sales are ‘off the charts,’ but Google, Amazon and others now make their own custom AI chips

LSD News Szerkesztőség — Sun, 23 Nov 2025 11:52:43 +0000

Nvidia outperformed all expectations, reporting soaring profits Wednesday thanks to its graphics processing units that excel at AI workloads. But more categories of AI chips are gaining ground.

Custom ASICs, or application-specific integrated circuits, are now being designed by all the major hyperscalers, from Google’s TPU to Amazon’s Trainium and OpenAI’s plans with Broadcom. These chips are smaller, cheaper, accessible and could reduce these companies’ reliance on Nvidia GPUs. Daniel Newman of the Futurum Group told CNBC that he sees custom ASICs “growing even faster than the GPU market over the next few years.”

Besides GPUs and ASICs, there are also field-programmable gate arrays, which can be reconfigured with software after they’re made for use in all sorts of applications, like signal processing, networking and AI. There’s also an entire group of AI chips that power AI on devices rather than in the cloud. Qualcomm, Apple and others have championed those on-device AI chips.

CNBC talked to experts and insiders at the Big Tech companies to break down the crowded space and the various kinds of AI chips out there.

GPUs for general compute

Once used primarily for gaming, GPUs made Nvidia the world’s most valuable public company after their use shifted toward AI workloads. Nvidia shipped some 6 million current-generation Blackwell GPUs over the past year.

Nvidia senior director of AI infrastructure Dion Harris shows CNBC’s Katie Tarasov how 72 Blackwell GPUs work together as one in a GB200 NVL72 rack-scale server system for AI at Nvidia headquarters in Santa Clara, California, on November 12, 2025.

Marc Ganley

The shift from gaming to AI started around 2012, when Nvidia’s GPUs were used by researchers to build AlexNet, what many consider to be modern AI’s big bang moment. AlexNet was a tool that was entered into a prominent image recognition contest. Whereas others in the contest used central processing units for their applications, AlexNet reliance on GPUs provided incredible accuracy and obliterated its competition.

AlexNet’s creators discovered that the same parallel processing that helps GPUs render lifelike graphics was also great for training neural networks, in which a computer learns from data rather than relying on a programmer’s code. AlexNet showcased the potential of GPUs.

Today, GPUs are often paired with CPUs and sold in server rack systems to be placed in data centers, where they run AI workloads in the cloud. CPUs have a small number of powerful cores running sequential general-purpose tasks, while GPUs have thousands of smaller cores more narrowly focused on parallel math like matrix multiplication.

Because GPUs can perform many operations simultaneously, they’re ideal for the two main phases of AI computation: training and inference. Training teaches the AI model to learn from patterns in large amounts of data, while inference uses the AI to make decisions based on new information.

GPUs are the general-purpose workhorses of Nvidia and its top competitor, Advanced Micro Devices. Software is a major differentiator between the two GPU leaders. While Nvidia GPUs are tightly optimized around CUDA, Nvidia’s proprietary software platform, AMD GPUs use a largely open-source software ecosystem.

AMD and Nvidia sell their GPUs to cloud providers like Amazon, Microsoft, Google, Oracle and CoreWeave. Those companies then rent the GPUs to AI companies by the hour or minute. Anthropic’s $30 billion deal with Nvidia and Microsoft, for example, includes 1 gigawatt of compute capacity on Nvidia GPUs. AMD has also recently landed big commitments from OpenAI and Oracle.

Nvidia also sells directly to AI companies, like a recent deal to sell at least 4 million GPUs to OpenAI, and to foreign governments, including South Korea, Saudi Arabia and the U.K.

The chipmaker told CNBC that it charges around $3 million for one of its server racks with 72 Blackwell GPUs acting as one, and ships about 1,000 each week.

Dion Harris, Nvidia’s senior director of AI infrastructure, told CNBC he couldn’t have imagined this much demand when he joined Nvidia over eight years ago.

“When we were talking to people about building a system that had eight GPUs, they thought that was overkill,” he said.

ASICs for custom cloud AI

Training on GPUs has been key in the early boom days of large language models, but inference is becoming more crucial as the models mature. Inference can happen on less powerful chips that are programmed for more specific tasks. That’s where ASICs come in.

While a GPU is like a Swiss Army Knife able to do many kinds of parallel math for different AI workloads, an ASIC is like a single-purpose tool. It’s very efficient and fast, but hard-wired to do the exact math for one type of job.

Google released its 7th generation TPU, Ironwood, in November 2025, a decade after making its first custom ASIC for AI in 2015.

Google

“You can’t change them once they’re already carved into silicon, and so there’s a trade off in terms of flexibility,” said Chris Miller, author of “Chip War.”

Nvidia’s GPUs are flexible enough for adoption by many AI companies, but they cost up to $40,000 and can be hard to get. Still, startups rely on GPUs because designing a custom ASIC has an even higher up-front cost, starting at tens of millions of dollars, according to Miller.

For the biggest cloud providers who can afford them, analysts say custom ASICs pay off in the long-run.

“They want to have a little bit more control over the workloads that they build,” Newsom said. “At the same time, they’re going to continue to work very closely with Nvidia, with AMD, because they also need the capacity. The demand is so insatiable.”

Google was the first Big Tech company to make a custom ASIC for AI acceleration, coining the term Tensor Processing Unit when its first ASIC came out in 2015. Google said it considered making a TPU as far back as 2006, but the situation became “urgent” in 2013 as it realized AI was going to double its number of data centers. In 2017, the TPU also contributed to Google’s invention of the Transformer, the architecture powering almost all modern AI.

A decade after its first TPU, Google released its seventh generation TPU in November. Anthropic announced it will train its LLM Claude on up to 1 million TPUs. Some people think TPUs are technically on par or superior to Nvidia’s GPUs, Miller said.

“Traditionally, Google has only used them for in-house purposes,” Miller said. “There’s a lot of speculation that in the longer run, Google might open up access to TPUs more broadly.”

Amazon Web Services was the next cloud provider to design its own AI chips, after acquiring Israeli chip startup Annapurna labs in 2015. AWS announced Inferentia in 2018, and it launched Trainium in 2022. AWS is expected to announce Trainium’s third generation as soon December.

Ron Diamant, Trainium’s head architect, told CNBC that Amazon’s ASIC has 30% to 40% better price performance compared to other hardware vendors in AWS.

“Over time, we’ve seen that Trainium chips can serve both inference and training workloads quite well,” Diamant said.

CNBC’s Katie Tarasov holds Amazon Web Services’ Trainium 2 AI chip that fill its new AI data center in New Carlisle, Indiana, on October 8, 2025.

Erin Black

In October, CNBC went to Indiana for the first on-camera tour of Amazon’s biggest AI data center, where Anthropic is training its models on half a million Trainium2 chips. AWS fills its other data centers with Nvidia GPUs to meet the demand from AI customers like OpenAI.

Building ASICs isn’t easy. This is why companies turn to chip designers Broadcom and Marvell. They “provide the IP and the know-how and the networking” to help their clients build their ASICs, Miller said.

“So you’ve seen Broadcom in particular be one of the biggest beneficiaries of the AI boom,” Miller said.

Broadcom helped build Google’s TPUs and Meta‘s Training and Inference Accelerator launched in 2023, and has a new deal to help OpenAI build its own custom ASICs starting in 2026.

Microsoft is also getting into the ASIC game, telling CNBC that its in-house Maia 100 chips are currently deployed in its data centers in the eastern U.S. Others include Qualcomm with the A1200, Intel with its Gaudi AI accelerators and Tesla with its AI5 chip. There’s also a slew of start-ups going all in on custom AI chips, including Cerebras, which makes huge full-wafer AI chips, and Groq, with inference-focused language processing units.

In China, Huawei, ByteDance, and Alibaba are making custom ASICs, although export controls on the most advanced equipment and AI chips pose a challenge.

Edge AI with NPUs and FPGAs

The final big category of AI chips are those made to run on devices, rather than in the cloud. These chips are typically built into a device’s main System on a Chip, SoC. Edge AI chips, as they’re called, enable devices to have AI capabilities while helping them save battery life and space for other components.

“You’ll be able to do that right on your phone with very low latency, so you don’t have to have communication all the way back to a data center,” said Saif Khan, former White House AI and semiconductor policy advisor. “And you can preserve privacy of your data on your phone.”

Neural processing units are a major type of edge AI chip. Qualcomm, Intel and AMD are making NPUs that enable AI capabilities in personal computers.

Although Apple doesn’t use the term NPU, the in-house M-series chips inside its MacBooks include a dedicated neural engine. Apple also built neural accelerators into the latest iPhone A-series chips.

“It is efficient for us. It is responsive. We know that we are much more in control over the experience,” Tim Millet, Apple platform architecture vice president, told CNBC in an exclusive September interview.

The latest Android phones also have NPUs built into their primary Qualcomm Snapdragon chips, and Samsung has its own NPU on its Galaxy phones, too. NPUs by companies like NXP and Nvidia power AI embedded in cars, robots, cameras, smart home devices and more.

“Most of the dollars are going towards the data center, but over time that’s going to change because we’ll have AI deployed in our phones and our cars and wearables, all sorts of other applications to a much greater degree than today,” Miller said.

Then there’s field-programmable gate arrays, or FPGAs, which can be reconfigured with software after they’re made. Although far more flexible than NPUs or ASICs, FPGAs have lower raw performance and lower energy efficiency for AI workloads.

AMD became the largest FPGA maker after acquiring Xilinx for $49 billion in 2022, with Intel in second thanks to its $16.7 billion purchase of Altera in 2015.

These players designing AI chips rely on a single company to manufacture them all: Taiwan Semiconductor Manufacturing Company.

TSMC has a giant new chip fabrication plant in Arizona, where Apple has committed to moving some chip production. In October, Nvidia CEO Jensen Huang said Blackwell GPUs were in “full production” in Arizona, too.

Although the AI chip space is crowded, dethroning Nvidia won’t come easily.

“They have that position because they’ve earned it and they’ve spent the years building it,” Newman said. “They’ve won that developer ecosystem.”

Watch the video to see a breakdown of how all the AI chips work: https://www.cnbc.com/video/2025/11/21/nvidia-gpus-google-tpus-aws-trainium-comparing-the-top-ai-chips.html

Dalal Street Week Ahead: Technical charts signal bullish bias despite mild fatigue

LSD News Szerkesztőség — Sat, 01 Nov 2025 15:11:56 +0000

The markets traded in a largely rangebound manner through the previous week and ended on a mildly negative note. After testing a high of 26097.85, the Nifty failed to hold on to its gains and pared back, ending slightly lower on a weekly basis. The Index oscillated in a narrow 386.65-point range over the week. India VIX edged higher by 4.85% to 12.15, indicating a slight rise in risk perception. While the Index lost 73.05 points (-0.28%) on a weekly basis, Nifty ended the month of October with a strong gain of 1111 points or 4.51%.

Agencies

The current technical structure remains firmly bullish but shows signs of minor fatigue near the upper resistance zone. The Index has broken above the falling trendline pattern drawn from the 2021 highs, confirming a bullish breakout. However, it faces overhead resistance from the upper Bollinger band at 25949, and this zone could act as a supply area in the immediate near term. The markets are not trending aggressively yet, but the setup favors a bullish bias. Any sustained move above 26100 would push Nifty into a fresh trending up-leg, while a close below 25450 may delay this breakout further and invite mild corrective moves.

Heading into a truncated week with Wednesday being a holiday on account of Gurupurab, the markets may open on a tepid note. The Index is likely to face resistance at 25950 and 26100 levels. Supports come in at 25600 and 25450 zones. Given the tight trading range of the past week and the lack of strong directional momentum, the Index may consolidate further before making a decisive move.

The weekly RSI stands at 60.57; it remains neutral and does not show any divergence against the price. The MACD remains in a buy mode as it stays above the signal line. No significant bearish or bullish candle was formed on the weekly chart, and the structure reflects a consolidation near highs rather than exhaustion.From a pattern analysis perspective, the Nifty has clearly broken out of a long symmetrical triangle consolidation spanning over 12 months. The Index has tested the breakout area and is now pushing higher again. It trades comfortably above all key moving averages – the 50-, 100-, and 200-week MA – confirming a structurally sound setup. The breakout remains valid as long as the Index sustains above the 25400–25450 range.Given the current context, market participants should stay selectively bullish while being mindful of overhead resistance. Aggressive buying should be avoided until a clear directional move is established. It would be prudent to protect profits at higher levels and remain stock-specific in approach. A cautiously optimistic stance with a preference for sectors showing relative strength would be an ideal strategy for navigating the

coming truncated week.

In our look at Relative Rotation Graphs®, we compared various sectors against the CNX500 (NIFTY 500 Index), representing over 95% of the free-float market cap of all the listed stocks.

Agencies

Relative Rotation Graphs (RRG) continues to show the presence of three Sectors, Nifty Auto, Metal, and PSU Bank, inside the leading quadrant. While the Auto is seen giving up and slowing down in relative terms, the PSU Bank and the Metal Index are rotating strongly. These groups will continue to relatively outperform the broader Nifty 500 Index.

The Nifty Midcap 100 Index stays inside the weakening quadrant. It might put up stock- specific shows, but may lag in terms of overall relative performance.

The Nifty Pharma Index has rolled inside the lagging quadrant. The Nifty Commodities, Consumption, and Media languish badly inside the lagging quadrant. The Realty, Bank Nifty, Infrastructure, and Financial Services Indexes are also in this quadrant, but they are sharply improving in relative momentum.

The Energy, Services Sector, and PSE Indices have rolled into the Improving quadrant and hint at the beginning of a phase of relative outperformance. The IT and FMCG Indices are also in this quadrant; they are seen slowing in their relative momentum.

Important Note: RRGTM charts show the relative strength and momentum of a group of stocks. In the above Chart, they show relative performance against NIFTY500 Index (Broader Markets) and should not be used directly as buy or sell signals.

Palantir’s astronomical growth in 3 charts

LSD News Szerkesztőség — Mon, 18 Aug 2025 07:32:16 +0000

Alexander Karp, CEO and co-founder of Palantir Technologies Inc.

Scott Eelis | Bloomberg | Getty Images

Palantir‘s astronomical rise since its public debut on the New York Stock Exchange in a 2020 direct listing has been nothing short of a whirlwind.

Over nearly five years, the Denver-based company, whose co-founders include renowned venture capitalist Peter Thiel and current CEO Alex Karp, has surged more than 1,700%. At the same time, its valuation has broken new highs, dwarfing some of the world’s technology behemoths with far greater revenues.

The artificial intelligence-powered software company continued its ascent last week after posting its first quarter with more than $1 billion in revenue, reaching new highs and soaring past a $430 billion market valuation.

Shares haven’t been below $100 since April 2025. The stock last traded below $10 in May 2023, before beginning a steady climb higher.

Retail investors are a key part of the stock’s strength.

Last month, retail poured $1.2 billion into Palantir stock, according to data from Goldman Sachs.

Here’s a closer look at Palantir’s growth over the past five years and how the company compares to megacap peers.

Government money

Government contracts have been one of Palantir’s biggest growth areas since its inception.

Last quarter, the company’s U.S. government revenue grew 53% to $426 million. Government accounted for 55% of the company’s total revenue but commercial is showing promise. Those revenues in the U.S. grew 93% last quarter, Palantir said.

Still, one of the company’s oldest customers is the U.S. Army.

Earlier this month, the company inked a contract worth up to $10 billion for data and software to streamline efficiencies and meet growing military needs. In May, the Department of Defense boosted its agreement with Palantir for artificial intelligence-powered battlefield capabilities by $795 million.

“We still believe America is the leader of the free world, that the West is superior,” Karp said on an earnings call earlier this month. “We have to fight for these values; we should give American corporations, and, most importantly, our government, an unfair advantage.”

Beyond the U.S.

The U.S. has been a key driver of Palantir’s growth, especially as the company scoops up more contracts with the U.S. military.

Palantir said the U.S. currently accounts for about three-quarters of total revenues. Commercial international revenues declined 3% last quarter and analysts have raised concerns about that segment’s growth trajectory.

Over the past five years, U.S. revenues have nearly quintupled from $156 million to about $733 million. Revenues outside the U.S. have doubled from about $133 million to $271 million.

Paying a premium

Palantir’s market capitalization has rapidly ascended over the past year as investors bet on its AI tools, while its stock has soared nearly 500%.

The meteoric rise placed Palantir among the top 10 U.S. tech firms and top 20 most valuable U.S. companies. But Palantir makes a fraction of the revenue of the companies in those lists.

Last quarter, Palantir reported more than $1 billion in quarterly revenue for the first time, and its forward price-to-earnings ratio has surged past 280 times.

By comparison, Apple and Microsoft posted revenue of $94 billion and $76 billion during the period, respectively, and carry a P/E ratio of nearly 30 times.

Forward P/E is a valuation metric that compares a company’s future earnings to its current share price. The higher the P/E, the higher the growth expectations or the more overvalued the asset. A lower price-to-earnings ratio suggests slower growth or an undervalued asset.

Most of the “Magnificent Seven” stocks, except for Nvidia and Tesla, have a forward P/E that hovers around the 20s and 30s. Nvidia trades at more than 40 times forward earnings, while Tesla’s sits at about 198 times.

At these levels, investors are paying a jacked-up premium to own shares of one of the hottest AI stocks on Wall Street as its valuation has skyrocketed to astronomical heights.

“This is a once-in-a-generation, truly anomalous quarter, and we’re very proud,” Karp said on an earnings call following Palantir’s second-quarter results. “We’re sorry that our haters are disappointed, but there are many more quarters to be disappointed.”

— CNBC’s Gabriel Cortes contributed to this story.

Why is Tata Consumer is looking attractive on charts for a buy?

LSD News Szerkesztőség — Tue, 15 Apr 2025 01:50:41 +0000

Tata Consumer stock has shown excellent follow-up after the breakout on a weekly basis and surpassed its previous rejection zone of 1075 levels. Traders can buy for a target of Rs 1135 and beyond in the next 2-3 weeks,” Shivangi Sarda, Analyst, Derivatives and Technical Research, Motilal Oswal Financial Services Ltd, said.

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Nvidia positioned to weather Trump tariffs, chip demand ‘off the charts,’ says Altimeter’s Gerstner

LSD News Szerkesztőség — Thu, 03 Apr 2025 19:31:14 +0000

Altimeter Capital CEO Brad Gerstner said Thursday that he is moving out of the “bomb shelter” with Nvidia and into a position of safety, expecting that the chipmaker is positioned to withstand President Donald Trump’s widespread tariffs.

“The growth and the demand for GPUs is off the charts,” he told CNBC’s “Fast Money Halftime Report,” referring to Nvidia’s graphics processing units that are powering the artificial intelligence boom. He said investors just need to listen to commentary from OpenAI, Google and Elon Musk.

President Trump announced an expansive and aggressive “reciprocal tariff” policy in a ceremony at the White House on Wednesday. The plan established a 10% baseline tariff, though many countries such as China, Vietnam and Taiwan are subject to steeper rates. The announcement sent stocks tumbling on Thursday, with the tech-heavy Nasdaq down more than 5%, headed for its worst day since 2022.

The big reason Nvidia may be better positioned to withstand Trump’s tariff hikes is because semiconductors are on the list of exceptions, which Gerstner called a “wise exception” due to the importance of AI.

Nvidia’s business has exploded since the release of OpenAI’s ChatGPT in 2022, and annual revenue has more than doubled in each of the past two fiscal years. After a massive rally, Nvidia’s stock price has dropped more than 20% this year and was down almost 7% on Thursday.

Gerstner is concerned about the potential of a recession due to the tariffs, but is relatively bullish on Nvidia, and said the “negative impact from tariffs will be much less than in other areas.”

He said it is key for the U.S. to stay competitive in AI. While the company’s chips are designed domestically, they are manufactured in Taiwan “because they can’t be fabricated in the U.S.” Higher tariffs would punish companies such as Meta and Microsoft, he said.

“We’re in a global race in AI,” Gerstner said. “We can’t hamper our ability to win that race.”

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Weekly winners: Nifty, Sensex top Asian charts with over 4% weekly surge – Indian Markets lead weekly gains among Asian peers

LSD News Szerkesztőség — Sun, 23 Mar 2025 00:14:43 +0000

Indian equity benchmarks topped the list of Asian indices in terms of weekly gains for the period ended March 22. The Nifty and Sensex recorded the strongest performance among regional markets, as per the data compiled by ET Bureau.

Indices from other Asian economies saw varied performance, with some posting solid gains and others ending the week in the red. The weekly change is measured as the percentage gain or loss over March 13.