What Tariffs? Smartphone Market Grows 1% as More Phones Fold In AI
Much of the increase in sales is attributed to midrange devices like Samsung's Galaxy A36 and other smartphones that have started incorporating AI.
The report blamed a drop in demand for lower-end devices, including those built around Android OS, for weighing down the smartphone market. However, devices that incorporate AI-enabled features have sparked a curiosity that's lured customers back -- for the right price.
"Samsung was able to consolidate its market leadership and outperform the overall market achieving strong growth in the quarter driven by the sales of its new Galaxy A36 and A56 products," Francisco Jeronimo, vice president for client devices at IDC, said in a statement. "These new products introduce AI-enabled features to mid-range devices, which has been effectively used in retail stores to drive sales."
Starting at $400, the Galaxy A36 potentially offers a more affordable entry into AI for many people, including AI-powered photo editing tools and Google's Circle to Search.
Overall, says CNET mobile expert Mike Sorrentino, Samsung's Galaxy A line, especially its even cheaper $200 model, has been successful at eating into a US phone market that's otherwise dominated by Apple. "Samsung as well as its rival Motorola, with its Moto G series, have put a particular focus on getting the features people are looking for into lower-cost devices," Sorrentino said. "Those devices are often further subsidized by wireless carriers, making them particularly accessible albeit with a two- or three-year commitment to a carrier."
A recent CNET survey found that just 11% of people upgrade their phone for AI features. However, the survey also found that price is the biggest driver (62%), meaning a midrange phone with new features could prove to be alluring enough to make the leap.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
18 minutes ago
- Yahoo
India eyes global quantum computer push — and QpiAI is its chosen vehicle
QpiAI, an Indian startup that claims to integrate AI and quantum computing for enterprise use cases, has raised $32 million in a new funding round co-led by the Indian government as the company aims to expand its presence and develop utility-scale quantum computers for markets around the world. The Indian government's $750 million National Quantum Mission has co-led QpiAI's all-equity Series A round, alongside Avataar Ventures, at a post-money valuation of $162 million. The funding reflects India's broader push to establish itself as a quantum computing power. Launched in 2023, the National Quantum Mission is an Indian government initiative that views quantum computing as both an economic opportunity and a national security imperative. The program aims to help develop intermediate-scale quantum computers with 50–1,000 physical qubits within 8 years across platforms including superconducting and photonics. It also targets the development of satellite-based quantum communications, inter-city quantum distribution, multi-node quantum networks, magnetometers, and the design and synthesis of quantum materials, including superconductors, semiconductor structures, and topological materials for quantum device fabrication. QpiAI is one of eight startups selected by the National Quantum Mission, each receiving an initial grant of up to $3.5 million. Among these companies, the Bengaluru-headquartered startup, which has subsidiaries in the U.S. and Finland, says it has built India's first full-stack quantum computer, called QpiAI-Indus, which it launched in April with 25 superconducting qubits. Founded in 2019, QpiAI says it integrates quantum computing and AI to provide optimization capabilities in areas such as manufacturing, industrial, transportation, finance, pharma, and materials. The startup says it has developed specialized software, along with its proprietary hardware, to support real-world quantum applications in fields such as materials science and drug discovery. 'Quantum can really make sure AI is robust,' said QpiAI founder and CEO Nagendra Nagaraja in an interview. The startup views simulation, drug synthesis, and material discovery as some of the key use cases where the combination of AI and quantum could provide a competitive edge. 'Because the design space of a quantum chip is very, very big, and to actually get optimal qubits, which is important when we integrate thousands of qubits to get logical qubits for error correction, AI plays a bigger role there,' Nagaraja told TechCrunch. QpiAI plans to launch its 64-qubit quantum computer in November, with availability to customers expected by the second or third quarter of next year, Nagaraja said. The company is also working to start manufacturing its quantum hardware locally in 2026. Currently, it assembles 80% of its machines in-house. QpiAI employs a 100-person team, comprising 25 PhDs from international institutions or from Indian institutes. About 50 of its employees are based in India. The company says it has around 20 customers in India and the U.S., including the Indian government, which uses its infrastructure to test algorithms. According to the company, QpiAI has been profitable at EBITDA level for the past three years, with approximately 60% gross margins and 20% to 30% net margins, Nagaraja told TechCrunch. With the fresh funding, QpiAI plans to enter new markets including Singapore and the Middle East. It also aims to allocate funds for its local manufacturing plans and scale its operations to develop a 100-logical qubit system by 2030. In June of last year, QpiAI raised $6.5 million in a pre-Series A round led by Yournest and SVCL at a post-money valuation of around $30 million. Nagaraja told TechCrunch that QpiAI has a three- to four-year runway, even without accounting for its profitability. He also said the startup is considering an initial public offering in either 2026 or 2027. The National Quantum Mission's support extends beyond QpiAI. The Indian government's program is also backing startups such as QNuLabs (working on quantum-safe networks), Dimira Technologies (cryogenic cables), Prenishq (diode-laser systems), and QuPrayog (optical atomic clocks). It has also supported Quanastra (cryogenic systems and superconducting detectors), Pristine Diamonds (diamond-based sensing materials), and Quan2D Technologies (single-photon detectors for quantum communication). 'We plan to continue to support home-grown product companies like QpiAI to help them expand into large enterprises and position India as global leaders in quantum technologies,' said Ajai Chowdhry, chairman of India's National Quantum Mission, in a prepared statement.
Tom's Guide
19 minutes ago
- Tom's Guide
Surprise! Samsung Galaxy Z Fold 7 found to be thinner than 'world's slimmest foldable'
Last year when Honor released its Magic V3 foldable phone it took home the honor of being the thinnest foldable on the market at 9.2mm closed and 4.34mm unfolded. This year Honor is claiming that the Magic V5 is the "world's slimmest foldable," but a recent comparison video between the V5 and Samsung's Galaxy Z Fold 7 seems to show that the newest Fold is microns thinner. We unfortunately can't get the Honor Magic V5 in the U.S., but Honor's marketing materials claim that the Ivory White color way is 8.8mm when folded, which is 0.1mm thinner than Samsung's Z Fold 7 measuring in at 8.9mm. There is no accurate measuring tool, but we can still draw a rigorous conclusion: Galaxy Z Fold7 is the thinnest folding mobile phone in the world.I switched the positions of two mobile phones during the test, and the results remained the same, which showed that the factors of… 15, 2025 A recent video from regular leaker Ice Universe popped up on X this week that shows the Magic V5 side-by-side with the Galaxy Z Fold 7. At first glance, the two devices look about the same thickness, though .1mm isn't that great a difference, but does it make the Z Fold 7 one of the best foldable phones? However, a card placed on top of the phones appears to show that Honor's device is slightly taller than Samsung's. They further emphasized this by putting a ping pong ball on the card with the tilt going toward the Z Fold 7. At this point, we're splitting micrometers difference here. They do have another comparison video that includes the Xiaomi MIX Fold 5, vivo X Fold 5 and the OPPO Find N5, which Honor's device seems more of a match. Get instant access to breaking news, the hottest reviews, great deals and helpful tips. Let's get some caveats out of the way here. One, Ice Universe is a regular, and seemingly only, leaker of Samsung products, specifically its phones. So there could be some bias there. Two, with no wider shot or level on the table, it's not clear if the surface is actually even enough for a fair test. In a comment to another X user they claimed, "My test is very rigorous. I switched the position of my mobile phone, and I have ruled out the uneven desktop. You can get the correct conclusion no matter what, even without a ruler." To be fair, they did post a follow up with a digital ruler that appears to show the Magic V5 measuring at 8.95mm and the Z Fold 7 at 8.79mm. Lastly, according to Honor's own metrics the 8.8mm measurement only applies to the Ivory White variant and that other colors may be thicker. Though Ice Universe does claim they tested the "white version." "Data comes from the HONOR Lab. 8.8mm refers to the thickness of the Ivory White model in the folded state," Honor's website says. "The actual data may vary depending on configurations, manufacturing processes and measurement methods." We're not going to cast aspersions on Honor about whether or not they're lying. At best it's stretching the truth, since they go out of the way to caveat their own hype, but it does seem like Samsung may hold the thinness crown for now. Though when you're measuring at micrometers, outside of marketing, is there really a difference?
Yahoo
39 minutes ago
- Yahoo
Watch These Nvidia Price Levels as Stock Surges on News That China Chip Exports to Resume
Key Takeaways Nvidia shares jumped Tuesday morning after the AI investor favorite said it plans to resume selling one of its most popular chips to China. After reclaiming the 200-day moving average in mid-May, Nvidia shares have continued to trend higher, though trading volume and volatility have contracted. Trend analysis, which takes the stock's move higher throughout June and repositions it from this month's low, projects a near-term price target of around $178. Investors should watch key support levels on Nvidia's chart around $159, $150 and $ (NVDA) shares jumped premarket trading on Tuesday after the AI investor favorite said it plans to resume selling one of its most popular chips to China. The chipmaker, which recently became the first company in the world to reach a $4 trillion market capitalization, said it's filing applications with the U.S. government to resume sales of its H20 graphics processing unit (GPU) and has been given assurances that licenses will be granted. Nvidia also unveiled a new chip specifically for China called the RTX Pro GPU, which the company said is fully compliant with U.S. export controls and suitable for use in smart factories and logistics. Through Monday's close, Nvidia shares had gained nearly 90% from their early-April low and were up 22% since the start of the year, as major tech companies continue to ramp up spending on AI infrastructure that depends on the company's chips. The stock was up more than 4% at around $171 before the opening bell on Tuesday. Below, we take a closer look at Nvidia's chart and use technical analysis to identify key price levels worth watching out for. Uptrend Continues, Trading Volume Contracts After reclaiming the closely watched 200-day moving average (MA) in mid-May, Nvidia shares have continued to trend higher, a move that has coincided with the relative strength index staying near its overbought threshold for most of that time, signaling strong price momentum. In another win for the bulls, the 50-day MA crossed back above the 200-day MA in late June to form a golden cross, a bullish chart pattern that points to a new trend higher. However, trading volume and volatility have contracted in recent months, suggesting market participants may be waiting for the chipmaker's earnings report next month before deploying further capital. Let's analyze the technicals on Nvidia's chart to project where the shares may be headed if the uptrend continues and also identity support levels worth watching during potential retracements in the stock. Where are Nvidia's Shares Headed Next? Investors can apply bars pattern analysis to predict how the stock's current trending move may play out. When applying this technique to Nvidia's chart, we take the stock's trend higher throughout June and reposition it from this month's low. This projects a price target of around $178, implying about 8% upside from Monday's closing price. We selected this earlier trend as it followed a minor profit-taking dip, closely mimicking the current price action after a similar brief pullback at the start of July. Key Support Levels Worth Watching The first lower level worth watching sits around $159. Retracements in the stock could initially find support near last month's high. Selling below this level could see the shares touch support near the key $150 level. Investors may look for buying opportunities in this location near a series of peaks that formed on the chart between November and January. Finally, a more significant pullback in Nvidia shares opens the door for a retest of lower support around $143. This area may attract buying interest near a consolidation period that developed on the chart in mid-June, which also aligns with a range of corresponding trading activity stretching back to late October. The comments, opinions, and analyses expressed on Investopedia are for informational purposes only. Read our warranty and liability disclaimer for more info. As of the date this article was written, the author does not own any of the above securities. Read the original article on Investopedia Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
