ABC's of AI

Pope Leo enters AI debate with warning against ‘dominating humanity’ – Crain's Chicago Business

admin — Tue, 26 May 2026 02:09:31 +0000

These 14 families wield influence across Chicago and Illinois through business, politics, philanthropy, sports and civic institutions.

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Nvidia Is Still a Top Artificial Intelligence (AI) Stock to Buy Right Now – The Motley Fool

admin — Tue, 26 May 2026 02:08:05 +0000

Nvidia (NVDA 1.86%) is still the cream of the crop of artificial intelligence (AI) stocks. It continues to post nearly unbelievable results, yet lately the market has been unwilling to show it the love that it's showing to some of its competitors. I think that makes Nvidia a perfect AI stock to invest in right now, as it's actually being underhyped compared to its performance — particularly in light of the results it delivered Wednesday.
Image source: The Motley Fool.
For Nvidia, Q1 was just another quarter of outperforming expectations. Management had told investors to expect $78 billion in revenue, plus or minus 2%. Instead, its top line grew 85% year over year to $81.6 billion, easily outperforming that guidance. Yet the stock dipped. As of late Friday, it was down more than 4% for the week. The most remarkable thing about its growth rate is that it's accelerating. Look at what Nvidia's growth curve looked like prior to Q1's results, and it becomes obvious that the company is only getting stronger.

NVDA Revenue (Quarterly YoY Growth) data by YCharts.
It's a bit mind-bending to think that Nvidia, the largest company in the world, is nearly doubling its revenue year over year and that its growth rate is re-accelerating. When a company is growing this fast, I also like to look at the quarter-over-quarter growth to determine if this growth rate represents long-term or short-term strength. In Q1, Nvidia's revenue rose 20% quarter over quarter. When other tech giants like Microsoft, Apple, or Alphabet grow at a 20% year-over-year rate, we celebrate and talk about how strong a quarter they had. Nvidia is achieving that growth in a fourth of the time.
These results show that the AI build-out is still picking up steam, and Nvidia is the premier company to invest in to take advantage of it.
Despite its more rapid growth, Nvidia trades at a similar level to the other tech megacaps mentioned above.

NVDA PE Ratio (Forward) data by YCharts.
In my view, this shows that Nvidia is far undervalued compared to the other three. As a result of the market's current disrespect, I think Nvidia is a solid buy here. It won't surprise me if the market soon starts to wake up to how impressive Nvidia's results were and sends its shares higher. If it can continue posting results like this, it is easily one of the best buys in the entire market.
Keithen Drury has positions in Alphabet, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Alphabet, Apple, Microsoft, and Nvidia. The Motley Fool has a disclosure policy.
*Average returns of all recommendations since inception. Cost basis and return based on previous market day close.
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The chipmaker just announced incredible quarterly results on Wednesday, and the market did nothing.

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Google adds open source Agent Executor to support AI agents in production – InfoWorld

admin — Tue, 26 May 2026 02:06:41 +0000

Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production.
To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows.
Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion.
For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preserve execution state during network interruptions, Google wrote in a blog post.
The runtime also supports “trajectory branching,” which allows developers to test alternate execution paths from saved checkpoints without losing prior context, it added.
Furthermore, Agent Executor bridges multiple deployment models, including on prem and pre-built or custom managed agents, the company said, allowing users to mix and match between any or all of Google Antigravity, frontier agents built by Google, agents built by the user and managed by Google, and custom agents and agents using Agent2Agent (A2A) protocol, as desired.
Analysts and experts see value for both developers and enterprises in Agent Executor’s capabilities.
“Durability, orchestration, and resumability are the real blockers for any enterprise production agents,” said Advait Patel, senior reliability engineer (SRE) at Broadcom.
“What kills enterprise adoption is agents that lose their state when a pod restarts, sessions that corrupt under concurrent writes, or long running workflows that cannot recover from a network blip. Once your agent is taking actions on real systems, you cannot afford it to forget what it did halfway through,” he pointed out.
“The event log, snapshotting, single writer model, and connection recovery in Agent Executor are exactly the things SRE teams have been duct taping for the last year,” Patel noted, adding that existing frameworks such as LangChain and AutoGen are great for prototyping, but more often than not fall apart in production once agents run for hours or days.
For CIOs, said Gaurav Dewan, research director at Avasant, the runtime’s operational safeguards such as secure sandboxing, and checkpointing could prove just as significant for incident analysis and auditability.
At the same time, he cautioned that the runtime’s capabilities alone do not solve the broader governance and oversight challenges that CIOs continue to face with enterprise AI deployments.
“Issues such as accountability, explainability of agent decisions, policy enforcement, and secure access across interconnected systems are still evolving,” he said. “As a result, while distributed runtimes can strengthen the operational backbone of agent deployments, CIO-level considerations around trust, compliance, and enterprise control are likely to require additional governance and oversight layers beyond runtime infrastructure alone.”
Google, however, is not alone in trying to shape the emerging infrastructure layer for enterprise AI agents. Other hyperscalers, such as Microsoft, with AutoGen and AWS, with Bedrock AgentCore, are promoting open or interoperable frameworks to gain strategic advantage.
“There are growing indications that hyperscalers are converging toward a model that combines open or interoperable tooling at the top of the stack with monetization concentrated in underlying infrastructure layers,” Dewan said.
“Google, Microsoft, and AWS are increasingly offering SDKs, agent frameworks, and orchestration tools to drive developer adoption and ecosystem growth, while continuing to generate value through compute infrastructure, managed AI platforms, data services, and observability capabilities,” he added.
And, according to Patel, Google’s strategy around Agent Executor is reminiscent of the path that the hyperscaler followed with Kubernetes ten years ago: “Give away the runtime, [and] drive consumption on Google Cloud via services, such as the Gemini Enterprise Agent Platform and Managed Agents API.”
He added, “[hyperscalers] have figured out that proprietary agent frameworks will not get adopted at enterprise scale. The money is in cloud consumption, managed services, and model inference. The tools on top need to be open or nobody will trust them.”
Anirban is an award-winning journalist with a passion for enterprise software, cloud computing, databases, data analytics, AI infrastructure, and generative AI. He writes for CIO, InfoWorld, Computerworld, and Network World. He won the 2024 Silver Azbee Award for Best News Article in the Technology category. He has a post-graduate diploma in journalism from the Indian Institute of Journalism and New Media.

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Could Artificial Intelligence Bankrupt Social Security? – Above the Law

admin — Thu, 19 Feb 2026 01:53:22 +0000

The solvency of the social security trust fund has been a concern in the past few decades.
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As artificial intelligence becomes more mainstream, many people are using it to do things that they would normally hire someone else to do. AI is helping people conduct research, analyze options, and even create artistic works. While this makes certain things easier and more efficient, it means that some people will get reduced work or lose their livelihoods. For governments that have to look at the big picture, this means more unemployment which translates to less tax revenue, with an emphasis on social security funding. Could AI pose a serious threat to future social security and Medicare funding? If so, what can be done about it?
Many people are concerned AI will take over white collar jobs that humans do now. While the technology is not perfect — which some lawyers have learned the hard way — it is improving. The worry was so bad that in 2023, Hollywood writers and actors went on strike. to prevent studios from using AI for drafting stories and using actors’ likenesses.
Protégé™ General AI is fundamentally changing how legal professionals use AI in their everyday practice.
Employees and business owners pay into social security and Medicare. Employees do so through paycheck withholdings and business owners pay self-employment taxes on their net business profit. While this tax is pretty substantial — 7.65% for employees and 15.3% for self-employed business owners, there is a cap on the income that is subject to the tax. For 2026, the income cap is $184,500 so any income above that is not subject to social security taxes although it is subject to Medicare taxes.
If jobs are replaced by AI, then the government will lose these social security tax and Medicare tax payments. The solvency of the social security trust fund has been a concern in the past few decades and, based on projections, the trust fund is on track to be insolvent in 2032. If insolvency is certain, then the government will have to make tough decisions to save the social security trust fund, either by cutting benefits, raising the eligibility age, or raising taxes.
Skeptics will note that technological disruption is nothing new and despite the doomsday talk, the economy will adapt. Most will cite complex machines replacing most assembly-line workers in the 20th century. Or e-commerce disrupting the Main Street brick-and-mortar stores.
Several proposals have been made to address this potential problem. One is to impose additional taxes on AI companies and businesses that use AI to compensate for the revenue lost due to increased unemployment. Imposing this tax on small businesses will be unpopular and the tax could be passed on to the customer if economic conditions allow it. Also, detecting whether a business is using AI could be difficult.
And how to navigate them in 2026.
Another idea getting a lot of attention lately is universal basic income (UBI) where everyone gets a regular cash payment whether they are working or not. It could act as a safety net which will let people cover basic living expenses even if automation wipes out their job. But UBI could disincentivize people to work and mouth off whatever nonsense is in their mind without fear of financial consequences. Considering the very large cost and the potential negative incentives, UBI is likely to be used as a last resort to prevent bigger societal problems.
Probably the easiest solution is to change the way how social security is funded. In short, enact new taxes or raise existing ones. Or impose a new tariff.
The final and possibly the most sensible solution is to wait and see. The proliferation of AI could result in jobs that were previously not available. Granted, most of the new jobs will involve servicing and maintaining the AI infrastructure. But jobs involving other disciplines and skill sets could be available. For example, philosophers, ethicists, and religious leaders could be hired to help with the AI’s moral programming.
AI is transforming work in ways we haven’t seen before, and it has the potential to strain social security systems by potentially shrinking the number of contributors. But if we get ahead of it — with things like exploring UBI, updating funding models, and sparking new kinds of jobs — we can make sure the upsides of AI benefit everyone, not just a few. The trick is to embrace the tech while protecting the people who get caught in the transition.
Steven Chung is a tax attorney in Los Angeles, California. He helps people with basic tax planning and resolve tax disputes. He is also sympathetic to people with large student loans. He can be reached via email at [email protected]. Or you can connect with him on Twitter (@stevenchung) and connect with him on LinkedIn.

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Elon Musk’s xAI Gets $3 Billion Investment From Saudi-Backed A.I. Firm – The New York Times

admin — Thu, 19 Feb 2026 01:52:18 +0000

Elon Musk’s xAI Gets $3 Billion Investment From Saudi-Backed A.I. Firm The New York Times
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How artificial intelligence will impact the private jet charter market – Private Jet Card Comparisons

admin — Wed, 18 Feb 2026 02:46:34 +0000

For decades new entrants have promised to revolutionize the archaic private jet charter market. AI is now center stage. What’s the reality?
By Doug Gollan, 10 hours ago
Recently, Tuvoli President & CEO Greg Johnson joined the Ironbird Podcast with host Dan Harris to discuss, “What’s Broken In Private Jet Charter.”
One subject was the use of Artificial Intelligence (AI), which has become a trend du jour in press releases.
In fact, one company, Jet Token, renamed itself to Jet.AI.
To bring you their discussion about AI, we provided the transcript of the podcast to ChatGPT. We then asked ChatGPT to provide a summary of their AI discussion and offer a perspective on where AI can support the private jet charter market and its limitations. The article was mainly edited with the assistance of Grammarly.
By ChatGPT
In the private jet charter market, artificial intelligence is often discussed as a destination rather than a tool.
Dan Harris surfaces this tension in his interview with Greg Johnson by repeatedly pressing on a deceptively simple question: what, exactly, is AI supposed to fix?

Johnson’s answer is consistent throughout the conversation.
The industry’s biggest risk is not failing to adopt AI fast enough. It is applying automation and AI to the wrong layers of the business, creating false confidence instead of better outcomes.
For years, charter operators have leaned on automation to handle volume.
Quotes are generated faster.
Requests are routed more efficiently.
Data is pushed across systems with increasing speed.
Tuvoli’s Greg Johnson warns that when AI is applied to problems such as ‘guaranteed availability’ or ‘perfect real-time pricing,’ it creates a veneer of precision that does not withstand contact with reality. The result is not better decision-making, but misplaced trust in outputs that appear authoritative yet are unreliable.
But Johnson argues that this acceleration has masked a deeper failure: automation has multiplied activity without improving understanding.
More quotes are being generated than ever before, yet operators often have less clarity than they did a decade ago about why trips book or fail.
Automation has increased noise faster than it has improved the signal.
This is where AI is frequently misunderstood.
Johnson draws a clear distinction between automation and artificial intelligence.
Automation is deterministic.
It follows rules.
When X happens, Y is triggered.
AI, by contrast, introduces probabilistic reasoning.
It evaluates patterns, context, and multiple possible outcomes before selecting a path.
The danger lies in assuming that AI can substitute for certainty in an industry where none exists.
Harris pushes Johnson on whether AI can eventually solve availability, pricing accuracy, or real-time aircraft certainty.
Johnson is unequivocal: those are the wrong problems to chase.
Aircraft break. Crews time out. Weather intervenes.
Even airlines with massive fleets cannot eliminate last-minute disruption.
Expecting AI to deliver perfect certainty in charter operations is a category error.
Yet much of the industry’s AI enthusiasm is implicitly built on that expectation.
Johnson warns that when AI is applied to problems such as “guaranteed availability” or “perfect real-time pricing,” it creates a veneer of precision that does not withstand real-world conditions.
The result is not better decision-making, but misplaced trust in outputs that appear authoritative yet are unreliable.
Where AI does add real value, Johnson argues, is in reducing friction and cognitive load — not replacing judgment.
AI can clean and reconcile messy data that humans struggle to manage at scale.
It can triage inbound requests, identify likely buyers versus low-intent inquiries, and surface patterns in historical booking behavior that no individual salesperson could reasonably detect.
It can significantly reduce time spent on repetitive tasks such as data cleanup, summarization, and internal communication.
But AI should not be asked to make safety decisions, override operational judgment, or promise outcomes that the physical world cannot guarantee.
Harris frames this as a maturity issue.
The charter industry, he suggests, has seen wave after wave of “revolutionary” technology promises — many of which failed to materialize at scale.
That history breeds skepticism and impatience.
When AI arrives with genuine capability, the temptation is to overcorrect and expect too much, too fast.
Johnson acknowledges this tension.
AI is moving faster than many previous technologies. Its ability to generate content, analyze data, and automate knowledge work is real.
But speed does not eliminate the need for domain expertise. In fact, it increases it.
One of Johnson’s most pointed observations is that AI solutions built outside aviation often fail because they address what customers say they want rather than what the industry actually needs.
Charter workflows are shaped by regulation, safety culture, owner economics, and operational nuance.
Without understanding those constraints, AI tools risk optimizing for metrics that don’t matter—or worse, actively harming margins and decision quality.
The most productive use of AI in charter today, Johnson suggests, is incremental and disciplined. Improve visibility. Reduce manual reconciliation. Help humans make better decisions faster — but keep humans accountable for those decisions.
Harris ties this back to a broader industry consequence.
When AI is oversold internally, operators either become overconfident or disengaged.
Sales teams stop questioning outputs.
Management assumes problems have been “solved” because a system exists. Meanwhile, the underlying issues—poor booking insight, payment ambiguity, and process friction—remain unresolved.
In that sense, AI does not fail the charter market. Charter fails AI by expecting it to replace foundational disciplines.
The path forward, Johnson argues, is not less technology but better sequencing.
Fix transactional clarity first.
Ensure systems of record exist.
Align sales, finance, and operations around a shared truth.
Only then does AI become a force multiplier rather than a distraction.
The following is ChatGPT’s response to our question, “At the end of your summary of the Ironbird podcast, can you provide ChatGPT’s perspective on potential and limitations relevant to the private jet charter market?”
That fact is worth addressing directly, particularly in an industry currently grappling with what AI can and cannot do.
ChatGPT’s strengths in the private jet charter market lie primarily in synthesis, explanation, and communication.
It can analyze large volumes of unstructured information—transcripts, contracts, emails, and policies — and quickly extract themes, risks, and narratives.
It excels at clarifying complex processes, reframing industry discussions, and helping professionals articulate their ideas more clearly to clients, investors, and internal teams.
ChatGPT is particularly useful for:
However, ChatGPT has critical limitations that mirror the warnings Greg Johnson raised in this discussion.
Most importantly, ChatGPT’s outputs are only as good as the context and data provided.
Without domain expertise guiding the questions, AI can confidently produce answers that sound right but miss critical nuance.
In the charter market, ChatGPT should be treated as an analytical assistant, not an authority.
It can help professionals think faster and communicate better — but it should never be mistaken for a decision-maker.
Used correctly, ChatGPT reduces friction and improves understanding.
Used incorrectly, it creates false confidence.
That distinction matters — in AI, and in charter.
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Builders’ Use of Artificial Intelligence – Eye on Housing

admin — Wed, 23 Jul 2025 14:21:13 +0000

Eye On Housing
National Association of Home Builders Discusses Economics and Housing Policy
The majority of single-family home builders do not currently use Artificial Intelligence (AI) in their businesses. For the highest use, 20% of builders use AI to generate advertising/marketing materials and 11% to help analyze markets/plan projects. Less than 5% currently use this tool to help with another 10 business functions, from designing projects to operating automated construction equipment in the chart below. These findings were derived from the July 2025 survey for the NAHB/Wells Fargo Housing Market Index (HMI) and reflect an early industry reading likely to evolve in the coming years.
Builders not currently using AI were asked about the likelihood they will start doing so in the next two years (using a scale from 1 to 5, where 1=not at all likely and 5=very likely). Not surprisingly, the two areas most likely to see new builders adopting AI are the generation of advertising/marketing materials (average rating 3.6) and the analysis of markets/plan projects (3.0)—the same ones that boast the largest adoption rates already.
Meanwhile, the chance that builders will take up the use of AI in any of the other business functions is much lower, as all 10 received average likelihood ratings below 3.0. The two areas where builders are least likely to start using AI in the next two years are in the operation of automated construction equipment (average rating: 1.7) and to interact with the local building or planning department (1.9).

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'AI godmother' Fei-Fei Li raises $230 million to launch AI startup – Reuters

admin — Fri, 11 Oct 2024 17:29:44 +0000

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Fei-Fei Li, a prominent AI researcher, has secured $230 million for a startup she co-founded with three colleagues. Their mission: developing AI technology that understands the intricate workings of the three-dimensional physical world. The company, World Labs, announced this achievement on Friday.

Initial funding for World Labs was led by Andreessen Horowitz, New Enterprise Associates, and Radical Ventures, with additional investments from AMD Ventures, Intel Capital, and Nvidia’s NVentures. The company did not disclose its valuation.

Li, recognized as one of Time Magazine’s 100 Most Influential People in AI for 2023, has a distinguished career. She led AI at Google Cloud from 2017 to 2018, served on Twitter’s board, and advised policymakers, including at the White House. Known affectionately as the “godmother of AI,” Li gained acclaim for developing ImageNet, a dataset pivotal in advancing computer vision technology.

In a more jovial tone:
Li, often dubbed the “godmother of AI,” has bagged a whopping $230 million for her latest venture, World Labs! This startup, co-founded with her savvy colleagues, aims to teach AI how the real world ticks in 3D. Funding was led by big shots like Andreessen Horowitz and Intel, alongside a star-studded lineup of backers.

Time Magazine’s AI influencer of 2023, Li’s resume sparkles with stints at Google Cloud, Twitter’s board, and even White House consultations. Her famous ImageNet dataset was a game-changer in computer vision, earning her accolades and the endearing nickname.

Known for her visionary ideas, Li isn’t just shaping AI’s future; she’s reinventing it with a focus on spatial intelligence. Forget flat screens—she’s all about AI that understands the real world, from AR/VR to robotics. And while other AI churns out mere images, Li’s team is crafting models that grasp how our 3D universe truly operates.

Based in San Francisco with a team of 20, World Labs isn’t Li’s first rodeo in entrepreneurship. From buying her parents’ dry cleaning business to revolutionizing AI, she’s a force to reckon with—balancing Stanford lectures with startup grind like a boss!

OpenAI’s Power Shakeup: CTO Mira Murati Exits as $150 Billion Pivot Looms

admin — Wed, 25 Sep 2024 22:59:53 +0000

Murati Steps Down: What Her Exit Means for OpenAI’s Billion-Dollar AI Revolution

“Breaking news from the world of AI: Mira Murati, the Chief Technology Officer of OpenAI, has dropped a bombshell. In a surprise move announced on X, Murati declared her resignation after a distinguished tenure spanning six-and-a-half years at the company. Known for steering OpenAI through key advancements like DALL-E and Codex, Murati cited a personal need for exploration as her reason for stepping down.

Her departure couldn’t come at a more pivotal moment for OpenAI, which is amidst a seismic shift towards becoming a for-profit entity, potentially valuing the company at a staggering $150 billion. This transition marks a strategic pivot, granting CEO Sam Altman equity and signaling a new era for the AI giant.

Murati’s exit follows a string of high-profile departures, including former Chief Scientist Ilya Sutskever and President Greg Brockman, raising eyebrows about the organization’s stability and future trajectory, particularly its pursuit of AGI.

In response to the news, Altman expressed gratitude for Murati’s contributions, hinting at forthcoming leadership changes amidst these turbulent times. As OpenAI braces for these transitions, questions loom about who will fill Murati’s shoes and how the company will navigate this pivotal juncture.”

Apple Faces Challenges in China as Huawei Capitalizes on AI Features

admin — Thu, 12 Sep 2024 13:23:16 +0000

The launch of Apple’s iPhone 16 series in China has been met with mixed reactions, as local users discovered that the highly anticipated AI features would not be immediately available in their language until next year[1]. This delay has sparked skepticism about the value proposition of the new iPhones, especially in light of strong competition from domestic rivals like Huawei[1].

Huawei’s AI Advantage

In contrast to Apple’s delayed AI rollout, Huawei’s Mate XT will offer AI-powered features from day one, including:

Text summary, translation, and editing functions
AI-enhanced image editing capabilities, such as object removal and photo retouching[6][7]

This immediate availability of AI features has contributed to the Mate XT’s strong pre-launch performance, with over four million pre-orders reported[5].

Consumer Reactions

Chinese consumers have expressed frustration with Apple’s delayed AI rollout:

“The absence of AI in China is akin to cutting one of Apple’s arms,” one Weibo user commented[1].
Another user questioned, “With the biggest selling point unavailable, shouldn’t you charge us half the price?”[1]

These sentiments reflect growing dissatisfaction among Chinese consumers who feel they are not receiving the full value of Apple’s latest innovations[1].

Market Implications

Apple’s AI delay in China could have significant consequences:

Market share: Apple’s ranking in China has already dropped from third to sixth place[1].
Competitive advantage: The delay gives competitors like Huawei an opportunity to establish themselves as leaders in AI-powered smartphones[1].
Regulatory challenges: Apple has yet to announce an AI partner in China, and the country’s regulatory landscape adds complexity to the situation[1].

Huawei’s Resurgence

Huawei has made a strong comeback in the high-end smartphone market:

The company launched the Mate 60 Pro with a domestically manufactured chip, defying US sanctions[1].
Huawei has become the world’s largest vendor of foldable phones, surpassing Samsung Electronics[1].
The new Mate XT, with its unique tri-fold design, has generated significant buzz in the market[2][4].

Looking Ahead

Apple’s AI strategy in China represents a crucial challenge for the company. While its brand still holds appeal, the delayed AI rollout and fierce competition from resurgent local players like Huawei pose serious obstacles[1]. The company’s ability to adapt its AI offerings to local conditions may ultimately determine its future success in this vital market.

Citations:
[1] https://technology.inquirer.net/136988/huawei-mate-xt-is-the-first-ever-tri-folding-smartphone
[2] https://www.cnn.com/2024/09/09/tech/china-huawei-max-xt-launch-intl-hnk/index.html
[3] https://www.nytimes.com/2024/09/10/business/huawei-trifold-iphone.html
[4] https://www.trendforce.com/news/2024/09/12/news-a-comparison-between-apple-iphone-16-and-huawei-mate-xt/
[5] https://www.reuters.com/technology/huaweis-tri-foldable-phone-stirs-chinese-pride-2800-price-tag-panned-2024-09-11/
[6] https://www.huaweicentral.com/huawei-mate-xt-has-ai-object-removal-and-expansion-tools-for-photo-editing/
[7] https://www.reuters.com/technology/huawei-teases-tri-fold-smartphone-raising-competition-with-apple-china-2024-09-10/
[8] https://readwrite.com/huawei-mate-xt-ultimate-design-triple-foldable-phone/