BUSINESS

Nvidia (NVDA) has spent the AI boom selling companies the computing power needed to generate things that didn’t exist before.

Now it wants your graphics card to do something similar while you’re playing a video game.

Nvidia’s new AI-powered graphics innovation DLSS 5 is officially rolling out on Sept. 3, starting with NBA 2K27. Nvidia claims it is the company’s largest computer-graphics advance since real-time ray tracing was introduced in 2018.

Nvidia calls it 3D-Guided Neural Rendering. The neural model analyzes scene objects to add more realistic lighting, materials, shadows, and other visual details.

The result sounds like an obvious technological upgrade. It also raises an unusual question for gamers and, eventually, Nvidia investors: At what point does a graphics card stop rendering a video game and start generating one?

Nvidia has dramatically reduced DLSS 5’s hardware requirements

But perhaps the most astounding figure in Nvidia’s release has nothing to do with picture quality.

When Nvidia demonstrated DLSS 5 in March, the technology required two flagship GeForce RTX 5090 graphics cards.

Nvidia believes DLSS 5 performance is 5x better and can run on a single RTX 50-series GPU in six months.

That includes the RTX 5060.

Nvidia states that just about every RTX 50-series GPU can run NBA 2K27 at 1080p with ray tracing and the Ultra option, utilizing DLSS 5 and other DLSS technologies.

Related: Bank of America doubles down on Nvidia stock

At the extreme end, Nvidia says an RTX 5090 can reach up to 370 frames per second at 4K with Ultra settings, ray tracing, and DLSS.

At 1440p, Nvidia lists the maximum performance as:

RTX 5090: 590 fps

RTX 5080: 410 fps

RTX 5070 Ti: 350 fps

RTX 5070: 260 fps

Those figures need some context.

DLSS is not only making the GPU do the work of rendering hundreds of full frames per second in the traditional sense. With Multi Frame Generation, AI creates extra frames between the ones that Nvidia normally renders. Current DLSS technology can create up to five more frames for every displayed frame.

DLSS 5 crosses another line

That’s not contentious about DLSS 5 though: frame generation is.

The major difference is in what occurs in those frames.

Nvidia claims its neural model understands scene objects and semantics and can produce more realistic lighting, material reactions and subsurface scattering, while consuming information from the underlying 3D environment. Developers have controls over structure and tone and semantic masks to govern how aggressively the algorithm modifies the picture.

Nvidia claims DLSS 5 in NBA 2K27 can adjust how light goes through a player’s ears, enhance the look of skin and facial hair, and produce more nuanced shadows on jerseys and basketballs.

Those may sound like tiny details. But they represent a significant philosophical change in how video-game graphics can be created. Traditionally, developers create an asset and the GPU renders it.

Enter DLSS 5, a new AI model that can look at that item and create more visual information around it in real time.

That difference is part of why there was a pushback to Nvidia announcing DLSS 5 back in March.

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Some of the earlier demos by Nvidia were derided for modifying characters too much, notably the look of Grace Ashcroft from Resident Evil Requiem, The Verge reported. The article likened the impact to running the game through a generative-AI filter in real time.

NBA 2K27’s AI is more focused on texturing, lighting, and shadows, according to The Verge.

Developers may also instruct DLSS 5 to leave certain sections, like faces, alone.

Nvidia’s newest AI feature comes with one ugly catchMATT RAMEY / Getty Images

Nvidia has another reason to make AI graphics indispensable

There is a commercial narrative behind the technical argument.

Nvidia’s Gaming unit recorded record sales of $16 billion in fiscal 2026, a 41% increase year-over-year.

This is a huge gaming company.

It is also modest in relation to what AI infrastructure has turned into for Nvidia.

The company just reported $96.2 billion of total revenue in a single quarter, up 106% year over year. Data Center alone generated $89 billion, up 117%.

It is an astounding comparison.

Nvidia’s last quarter of Data Center revenue was almost 5 times the total Gaming revenue for the entire prior fiscal year.

That doesn’t imply gaming is dead.

Instead, Nvidia is increasingly bringing the same basic economic proposition that transformed its data center business: more AI requires more Nvidia computing power, back to consumers.

DLSS 5 is one such case.

The technology is officially launching on RTX 50-series desktop and laptop GPUs and on Nvidia’s GeForce Now service. Nvidia said the next model upgrades should increase the performance further.

So, for a gamer with older hardware, some of the latest visual features are not just unlocked with a software update.

They can become another reason to upgrade the GPU.

Nvidia could change what a graphics upgrade actually means

For decades, PC gamers have been upgrading their graphics cards for a rather obvious reason.

A faster GPU would be able to produce more polygons, higher quality textures, sharper shadows and ultimately more realistic ray-traced lighting.

AI alters that equation.

If the neural networks can intelligently rebuild, augment or synthesize sections of the final product, the graphics card doesn’t have to brute-force every pixel and every frame in the conventional sense.

This might make the capabilities of AI more and more essential when buyers compare GPUs.

And Nvidia has been building just that edge into RTX.

Related: OpenAI’s agents breached Hugging Face. Nvidia wants it.

The RTX 50 series is based on the fifth generation of Nvidia’s Tensor Cores, a dedicated AI accelerator silicon. DLSS provides Nvidia with a consumer-facing application that can take those AI cores and convert them into something players will really see.

The strategic value could be more than one generation of graphics cards.

If game creators are able to start developing games with neural rendering at the core, rather than as an optional addition, AI technology might become as crucial to gaming GPUs as conventional rendering performance.

One game will now test Nvidia’s biggest graphics claim in years

There is a catch. At launch, the showcase is remarkably narrow. NBA 2K27 is the only confirmed game shipping with DLSS 5 in Sept. 3.

The games announced at Nvidia’s March event included Resident Evil Requiem, EA Sports FC, Starfield, and Hogwarts Legacy; however, The Verge reports that Nvidia did not provide an updated timeframe for DLSS 5 support in those titles.

That is, investors and gamers should not mistake the promise of the technology with the possibility for mass adoption.

Nvidia has shown that DLSS 5 can operate on much less hardware than it could six months ago.

Developers or players have yet to show that they want AI to change depicted scenes in broad strokes.

That’s why NBA 2K27 is more significant than it looks.

The discussion isn’t actually about whether artificial intelligence can make virtual bacon shinier or basketball players’ skin more lifelike.

It’s about where computer graphics are heading, according to Nvidia.

For decades, the graphics industry competed over which GPU could render reality most convincingly.

Nvidia is beginning to suggest that the next winner may be the GPU that can intelligently generate the missing pieces of reality instead.

Gamers can decide on Sept. 3 if that’s the future or if Nvidia’s AI has gone too far.

Related: Nvidia stock flashes unusual signal for investors 

When development companies put their funds behind a project, there is always the risk that things will not go the way they intended.

Two resorts in Miami Beach filed for Chapter 11 bankruptcy within a few weeks of each other in March 2026, while at the end of last year, the company behind well-known New York City hotels such as The Tuscany and Hotel 27 shut down operations in a situation that left guests from different parts of the world struggling to find last-minute accommodations at Manhattan rates.

The latest development company to file for Chapter 11 bankruptcy in the U.S. Bankruptcy Court for the Southern District of New York is the beleaguered Park Place Partners Development LLC.

Park Place Partners Development files for Chapter 11 bankruptcy

The owner of both the unfinished 66-foot 45 Park Place tower at the intersection of New York’s Tribeca and Financial District neighborhoods and the vacant lot next to it, the company run by Sharif El-Gamal started running into financial troubles after lower numbers of apartments in the property have gone under contract than expected and overseas lenders started to cut off funding.

The initial plan to put a 71-foot-tall, 16,000-square-foot Islamic cultural and prayer center adjacent to the main tower also caught national attention around 2010 due to its proximity to the Ground Zero site of the former World Trade Center and was eventually scrapped amid controversy.

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Amid both rising debts and multiple creditor efforts to foreclose, construction on the tower stopped in 2019, and the tower stood in a half-finished shell over the last seven years in one of the most well-recognized examples of a stalled project in New York and the U.S.

According to the bankruptcy filing, Park Place Partners Development reported $15 million in assets and $13.8 million in liabilities. Kevin Nash of the Goldberg Weprin Finkel Goldstein law firm is representing El-Gamal and Park Place Partners in the bankruptcy case.

Early renderings presented 45 Park Place as an ambitious luxury condo tower project.45 Park Place

How 45 Park Place became the most famous unfinished condo tower in New York

Park Place Partners Development could not be reached for comment on the filing; as a result, little other information on why the company filed for bankruptcy now, after years of setbacks, is currently publicly available.

Amid multiple foreclosure efforts, El-Gamal has previously threatened to tear down the top of the 667-foot tower that has already been built in order to make the project less valuable and “get back” at creditors that he felt were purposefully preventing him from getting ahead.

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In response, the lending group that included creditors like Malaysia’s Malayan Banking Berhad, Kuwaiti Warba Bank and Chicago-based MSD Partners accused El-Gamal of creating a “bad faith scheme” to “gain leverage in negotiations.”

“To the extent that Mr. El-Gamal and his companies seek to pursue this scheme, they will be held fully liable for any and all damages,” the group wrote in a letter through the attorneys representing them as reported by The Real Deal in 2020.

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For years, figuring out whether to redeem loyalty points or miles for a trip has been tedious, frustrating and a real time zapper.

Do you use miles? Pay cash? Transfer credit card points? And are you actually getting a good deal when you redeem those hard-earned rewards?

That’s why I became a fan of Points Path when it first launched. The free browser extension made it much easier to compare cash prices with airline miles right inside Google Flights.

But Points Path has gotten even better. You can now use it to compare hotel prices, evaluate certain Chase Ultimate Rewards redemptions through Chase Travel and even use Points Path on an iPhone or iPad.

To me, Points Path is a travel game-changer.

What Is Points Path?

Points Path is a travel tool designed to help answer one of the hardest questions when using travel rewards: Should I pay with cash or points?

It started as a browser extension that works with Google Flights. Once installed, Points Path adds award prices alongside the cash prices you already see when searching for airfare.

Instead of searching Google Flights for the cash price and then opening multiple airline websites to see what those same flights cost in miles, you can see much of that information in one place.

Points Path has since expanded beyond airfare. You can now use it with:

Google Flights to compare cash and airline award prices.

Chase Travel to evaluate certain Ultimate Rewards redemptions.

Hotel booking sites to compare rates for the same property.

Safari on an iPhone or iPad through the Points Path app.

The basic Points Path service is free, although the company also offers paid features.

How Does Points Path Work With Google Flights?

This is still one of my favorite Points Path features.

Once you install the extension, search Google Flights as you normally would. Points Path adds the number of miles you would need to book eligible flights alongside Google’s cash prices.

Points Path supports a growing list of airline loyalty programs, including major programs from American, Alaska, Delta, JetBlue, United and Air Canada, along with a number of international airlines.

But Points Path doesn’t just show you the award price. It also helps you determine whether using miles or paying cash offers the better value.

Points Path does that by comparing the cash price with its estimated value of the miles required for the flight. The result is a quick recommendation to “Use points,” “Use cash” or “Use either.”

Points Path bases those recommendations on its own valuations for each airline’s points or miles. You can see Points Path’s current points and miles valuations on its website.

That doesn’t mean you always have to follow its recommendation. Maybe you’re sitting on a mountain of miles and would rather save your cash. Or perhaps you’re saving those miles for a bigger trip.

But Points Path gives you the information you need to make that decision without doing nearly as much homework yourself.

Points Path Example: Atlanta to Paris

Here’s an example of why I like this tool so much.

I searched Google Flights for a nonstop, round-trip flight from Atlanta to Paris in January 2028. The lowest nonstop cash fare was $2,063 round trip.

But with Points Path installed, I could also see the award prices — and, even better, Points Path gave me a quick recommendation on whether to “Use points,” “Use cash” or “Use either.”

For one Delta flight, for example, I could pay $2,063 or redeem 175,000 SkyMiles plus $136 in taxes and fees. Points Path labeled that redemption “Use either,” meaning the value of using miles was close enough to paying cash that either option could make sense.

Other flights in my search were labeled “Use cash,” making it easy to spot the redemptions that wouldn’t give me particularly good value.

I especially like this feature because you don’t have to do the points-versus-cash math yourself. At a glance, you can see the cash fare, available award price and Points Path’s recommendation.

Maybe you’d rather keep more than $2,000 in your bank account and use the miles, even if the redemption isn’t an outstanding deal. Or maybe you’re saving those miles for a trip where they’ll stretch further.

Either way, Points Path gives you a helpful starting point.

From there, you can select the flight and follow Points Path to the airline’s website to review the available award options and complete your booking.

Take your hands off the keyboard!

You may see several screen changes while Points Path does its work and redirects you to the airline.

Just remember that airfare and award prices change constantly, so your results will depend on when you search.

Have Fun With It!

One of my favorite things about Points Path is simply playing around with possible trips.

You don’t even need to have enough miles in your account to search. That means you can use Points Path to get an idea of how many miles you might need for a future trip — whether you’re dreaming about the Caribbean, Europe or somewhere much farther away.

It can turn “I’d love to go there someday” into a much more concrete savings goal.

How Does Points Path Work With Chase Travel?

Points Path can also help eligible Chase cardholders evaluate redemptions inside Chase TravelTM.

Chase’s Points Boost program can give eligible cardholders increased value for their Ultimate Rewards points on select flights and hotels. But the value can vary from one booking to another.

That’s where Points Path comes in.

When you search Chase Travel using a supported web browser, Points Path can calculate the value you’re getting per Ultimate Rewards point on eligible Points Boost redemptions. This can help you decide whether using points through Chase Travel offers a good value.

That’s particularly helpful because a redemption labeled “Points Boost” isn’t automatically the best possible use of your points. You may get more value by transferring your points to an eligible airline or hotel partner — or you may decide the convenience of booking through Chase Travel is worth it.

The Points Path Chase Travel feature works with both flights and hotels.

You’ll need to enable the Chase Travel feature through your Points Path account. Go to “Additional Permissions” to enable Chase Travel browser permissions. You’ll need to activate it on each device where you want to use it.

One important note: Points Path information appears when you access Chase Travel through a supported web browser. You won’t see the Points Path overlay inside the Chase mobile app.

Can Points Path Help You Compare Hotel Prices?

Yes. Points Path isn’t just about airline miles anymore.

The tool has expanded into hotel price comparisons, which could save you from opening a bunch of tabs before booking a room.

When you’re viewing a hotel on a supported online travel agency, Points Path can look for rates for the same property from other booking sources. You can also use its Compare Price feature to check prices elsewhere, including rates available directly from the hotel.

That’s useful because the price for the same room can vary depending on where you book it.

Of course, the lowest advertised price isn’t the only thing to consider. Before booking, compare:

Taxes and resort fees.

Cancellation and refund policies.

Room types and included amenities.

Hotel loyalty points and elite-status benefits.

Credit card travel protections.

Booking through a third-party site may mean you won’t earn hotel points or receive your usual elite benefits. Still, I love the idea of having a quick price comparison available while I’m already shopping.

You’ll need to grant additional browser permissions to enable the hotel feature. As of this writing, Points Path says hotel comparisons work best on a desktop or laptop and aren’t fully supported on iOS.

Can You Use Points Path on Your Phone?

Yes! This is another big change since Points Path first launched.

Points Path now offers an app for iPhone and iPad. But there’s an important distinction: It isn’t a standalone travel-search app. The app allows you to install and use the Points Path extension in Safari on an iOS device.

After downloading Points Path from Apple’s App Store, follow its instructions to enable the extension in Safari. Then you can see Points Path results while searching Google Flights in your mobile browser.

A couple of limitations to keep in mind: You will need iOS or iPadOS 18 or later, and not every desktop feature is fully supported on mobile. For example, Points Path says its hotel comparison feature is currently optimized for desktop and laptop browsers.

Android users are still out of luck when it comes to a Points Path mobile extension. Points Path says browser extensions aren’t currently available on Android.

What Browsers Support Points Path?

On a computer, Points Path is available for Safari and through the Chrome Web Store for Google Chrome and many Chromium-based browsers, including Microsoft Edge, Opera, Brave and Arc.

On an iPhone or iPad, you can download the Points Path app and enable the extension in Safari.

That makes Points Path much more accessible than it was when I first started using it.

Is Points Path Free?

Yes. You can install and use the basic version of Points Path for free.

Points Path also offers paid options with additional features for more serious points-and-miles users. But you don’t need to pay just to see basic Points Path results alongside your Google Flights searches.

I’d start with the free version and see how often you use it before deciding whether any paid features are worth the money for you.

Final Thoughts

Points Path started out solving one very specific travel headache for me: Should I use miles or just pay for the flight?

Now it can help answer quite a few more questions.

Is that Chase Points Boost redemption really a good deal? Is the hotel cheaper somewhere else? How many miles should I be saving for that trip I’ve been dreaming about?

And you can answer those questions without jumping from website to website or doing all the math yourself.

You still need to pay attention to the details before booking — especially taxes and fees, cancellation policies and any hotel loyalty benefits you could give up by booking through a third party.

But anything that makes travel rewards easier to understand and saves me time is a win in my book.

Points Path does both.
The post Points Path Review: This Free Tool Makes Using Travel Points Much Easier appeared first on Clark Howard.

A new show on Netflix has a 100% Rotten Tomatoes review score from critics and has been the #1 program on the site for over a week.

Presented by ContentfulMost marketing teams still measure visibility the same way they always have: rankings, click-through rates, and organic traffic. But buyers have moved on. Search tools and AI engines now synthesize answers directly on the screen, creating a world of zero-click searches where your website is entirely bypassed. The “old days” are not coming back. The question for marketers is no longer, How do we rank first? It’s How do we become part of the answer?The answer isn’t publishing more content; it’s making your knowledge impossible for AI to ignore.Brand visibility has a new dimensionShowing up is only half the battle. Where you appear inside an AI-generated response matters just as much.Think about the experience. If your brand is mentioned after multiple answer cards, product recommendations, follow-up questions, and community discussions, most people will never see it. Ranking reports won’t capture that.One way to think about this is pixel depth. Instead of measuring position on a search results page, measure how prominently your brand appears within the answer itself. Visibility increasingly depends on whether you’re seen before someone feels they’ve learned enough to stop reading. It’s no longer enough to rank at the top of search results. Now “share of visibility” models also weigh SERP features, ads, and AI Overview presence for a more holistic view of what kind of attention your company can expect to get.AI builds answers instead of indexing pagesSearch engines were designed to index pages. Large language models work differently.Rather than evaluating a page as a single unit, they connect facts, concepts, entities, and relationships from many sources to generate an answer. AI systems don’t treat pages as single units; they extract and recombine facts across sources. Your website becomes one source of evidence rather than the destination.That changes what makes content valuable.A polished landing page still matters for people. But before someone reaches that page, an AI system has already decided whether your information is clear, credible, and consistent enough to include in its response.AEO is really an information architecture problemMany organizations approach answer engine optimization as a writing exercise. In reality, it starts much earlier.AI systems need information they can understand. That depends on consistent terminology, structured content, clear metadata, well-maintained documentation, and a single source of truth across product pages, help centers, blogs, and FAQs.When the same product is described three different ways across your website, you create uncertainty. A customer might work through those inconsistencies. An AI system is more likely to move on to a source that’s easier to interpret.Kemberly Gong, VP of Marketing at Contentful, recently described what AI systems look for: structured content, clear context, authority, and validation from other trusted sources. AI doesn’t automatically accept what your brand says about itself. It looks for consistency across your own content as well as supporting signals from reviews, documentation, industry publications, and community discussions.The goal isn’t simply to publish more content. It’s to build a body of knowledge that holds together.Readability is now key to discoverabilityClear writing has always been good for readers. Now it’s also good for machines.Descriptive headings, concise paragraphs, clearly defined terms, logical structure, and scannable formatting all make it easier for AI systems to understand and reference your content. Those same qualities make life easier for human readers.Content that’s easy for answer engines to interpret shares four characteristics:Consistency: Use the same terminology across product pages, documentation, FAQs and blogs.Clarity: Define technical terms the first time they’re introduced, and keep each section focused on a single idea.Authority: Support your claims with original research, customer evidence, expert insights or other unique information.Structure: Organize content with descriptive headings, a logical hierarchy and standalone sections that answer engines can easily interpret and reference.Those principles don’t just improve readability. They also make your content easier for answer engines to interpret and include in AI-generated responses.Originality has become a competitive advantageThe web has no shortage of AI-generated summaries. What it lacks is information that exists nowhere else.Original research. Customer data. Benchmarks. First-hand expertise. Strong opinions backed by experience. Those are the assets AI systems can’t easily replace because they aren’t available everywhere else.That makes original thinking more valuable than ever.When ten companies publish the same advice, AI has little reason to favor one over another. When your organization contributes something genuinely new, you become the source others reference.Four questions every marketing leader should askBefore investing in another AEO checklist, step back and ask:Could an AI accurately explain what our company does?Do our product pages, documentation, and thought leadership describe the same concepts consistently?Is our expertise organized well enough to be cited?Are we contributing original knowledge or simply producing more content?The bottom lineStrong brands aren’t disappearing from AI answers because they lack expertise. They’re disappearing because their expertise is fragmented, inconsistent, or difficult for machines to interpret.The organizations that gain visibility over the next few years won’t necessarily publish the most content. They’ll make their knowledge easier to understand, easier to verify, and easier to trust. That’s good for AI systems, and even better for the people reading the answers.About Contentful: Contentful helps organizations turn content into a strategic asset. Its headless CMS gives teams the tools to create structured, reusable, and consistent content across every channel, helping brands improve customer experiences while preparing for an AI-driven future. Learn more at Contentful.com. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Microsoft AI on Thursday released MAI-Transcribe-2, a speech-recognition model the company says is faster, more accurate, and cheaper than anything OpenAI, Google, or ElevenLabs currently sells. Then it priced the thing at 10 cents per hour of audio.That figure deserves a pause. When Microsoft AI shipped the first model in this line just five months ago, it charged $0.36 an hour. Thursday’s early-bird price cuts that by roughly 72%. For an enterprise processing 100,000 hours of call-center audio a year — a modest volume for a large bank or telecom — the bill drops from $36,000 to $10,000. At that level, transcription stops being a line item anyone argues about.The release arrives as Microsoft executes a strategy that would have seemed implausible two years ago: building its own frontier-class models one modality at a time, then steadily swapping them into products that once ran on OpenAI’s technology. Transcription is the modality where that plan has moved fastest, and MAI-Transcribe-2 is its clearest proof point yet. It also offers a preview of how the world’s most valuable software company intends to compete in AI without depending on the partner it spent $13 billion to cultivate.What MAI-Transcribe-2 does and why the feature list matters to enterprise buyersThe model transcribes audio in 60 languages, up from 43 in June’s MAI-Transcribe-1.5 and 25 in April’s original release. It runs on Microsoft Foundry, the company’s model marketplace for developers, and in MAI Playground, its testing environment. Microsoft says it built the model for the messy audio that real businesses generate — background noise, low-quality recordings, overlapping speech — rather than clean studio conditions.More important than the language count is what Microsoft has bundled into the base product. Speaker diarization sorts out who said what in a multi-person recording, which is the difference between a wall of text and a usable meeting transcript. Word-level timestamps attach a precise time marker to every word, enabling search, editing, and alignment with video. Keyword biasing lets developers feed the model a list of drug names, product codes, or employee names so it stops mangling domain jargon. Automatic language identification means users no longer have to declare the language in advance.Two features stand out for their specificity. A configurable output style offers a “verbatim” mode that preserves every “um,” false start, and stutter for compliance and legal teams, alongside a “clean” mode that strips fillers for readable captions and notes. And code switching handles conversations that drift between languages mid-sentence; Microsoft explicitly names Hinglish and Spanglish, a nod to the Indian and U.S. Hispanic markets where a single customer-service call might toggle languages a dozen times. Specialty vendors have historically charged premiums for each of these capabilities. Microsoft is including all of them for a dime.How to read Microsoft’s FLEURS and Artificial Analysis benchmark claimsMicrosoft makes three performance claims, each resting on a different measuring stick, and technical buyers should understand what each one captures and what it misses.The first is that MAI-Transcribe-2 ranks number one on FLEURS across 60 languages with an average word error rate of 5.2%. FLEURS is a benchmark Google researchers published in 2022, built from native speakers reading roughly 2,000 sentences in each of 102 languages — about 12 hours of speech per language. It is the standard yardstick for multilingual speech recognition because it lets you compare a model’s Swahili against its Swedish on identical content. Word error rate, its metric, simply counts substitutions, insertions, and deletions against a human reference; 5.2% means roughly one word in 20 is wrong. But FLEURS is read speech, not conversation, and Microsoft’s average has actually risen from the 3.7% it reported for MAI-Transcribe-1.5 in June. That almost certainly reflects broader coverage rather than regression — averaging across 60 languages instead of 43 means folding in low-resource languages where every model struggles — but buyers should request the per-language breakdown.The second claim is that the model ranks second on the Artificial Analysis word-error-rate leaderboard and defines that firm’s accuracy-latency Pareto frontier. Artificial Analysis is an independent benchmarker that tests models through their public APIs, measuring what a customer actually gets. Its index blends simulated agent conversations, European Parliament speeches, and corporate earnings calls, weighting heavily toward English business speech. In June, the firm ranked MAI-Transcribe-1.5 third at 2.4% WER, behind Alibaba’s Fun-Realtime-ASR-preview and ElevenLabs’ Scribe v2, while calling it the fastest model in the top 10. Climbing to second suggests Microsoft has cleared ElevenLabs. “Pareto frontier” is the phrase practitioners should note: it means no rival beats the model on accuracy without being slower, and none beats it on speed without being less accurate.The third claim is raw speed — 10 times faster than OpenAI’s GPT-Transcribe, seven times faster than ElevenLabs’ Scribe v2, five times faster than Google’s Gemini 3.5 Transcribe, per Artificial Analysis evaluations. In batch transcription, speed matters less because anyone is waiting and more because throughput is cost. A model running at 300 times real-time needs a fraction of the GPU-hours of one running at 30 times. That efficiency is what lets Microsoft charge a dime and, presumably, still make money.Three speech models in five months: inside Microsoft AI’s rapid release cadenceThe pace is the story within the story. On April 2, MAI-Transcribe-1 launched with 25 languages at $0.36 per hour. On June 2, MAI-Transcribe-1.5 arrived with 43 languages, keyword biasing, and a third-place ranking on Artificial Analysis. Today, MAI-Transcribe-2 shipped with 60 languages, diarization, timestamps, code switching, a second-place ranking, and a price of $0.10.Three releases in five months, each expanding language coverage by roughly 40% while adding features competitors gate behind premium tiers. That cadence is characteristic of a team that has settled on a stable architecture and is now turning the crank on data and scale — the phase where speech models tend to improve quickly and predictably. It is also the cadence of a company that intends to make transcription a commodity before anyone else can.The organizational bet behind that speed is one Mustafa Suleyman, Microsoft AI’s chief executive, described to The Verge in April. He credited the first model to “a small, focused 10-person team” that had been “liberated from any of the bureaucracy,” with a larger surrounding group handling vendor management and data acquisition.He also told The Verge the model ran at “half the GPU cost of the other state-of-the-art models,” calling it “a huge cost-saving” for Microsoft. Meta, Amazon, Google, and Anthropic have all experimented with similar flattened structures, The Verge noted. Microsoft’s transcription line is the most visible test yet of whether the approach produces commercial results rather than research papers.Why Microsoft is building its own AI models despite its $13 billion OpenAI betMicrosoft has invested more than $13 billion in OpenAI, and hosts OpenAI’s models across Azure, Office, and Copilot. For most of the past four years, the obvious question about any Microsoft-built model has been: why bother? The answer has sharpened over the past year, and it begins with independence.When Microsoft hired Suleyman from Inflection AI in March 2024, along with most of Inflection’s staff, Salesforce CEO Marc Benioff read it as a declaration of intent. “Microsoft is building their own AI and I don’t think Microsoft will use OpenAI in the future. They’ll have their own frontier models,” Benioff told CNBC in January 2025. “That’s why they hired Mustafa Suleyman.” Benioff had his own motives — Salesforce competes with Microsoft and invests in Anthropic — but events have largely borne him out.In October 2025, Microsoft and OpenAI restructured their partnership in a deal that, per Microsoft’s own announcement, allowed Microsoft to “independently pursue AGI alone or in partnership with third parties” for the first time. Suleyman told The Verge that renegotiation “unlocked [Microsoft’s] ability to pursue superintelligence,” and Microsoft announced its MAI Superintelligence team weeks later. In April 2026, the companies amended the deal again, ending Microsoft’s exclusive access to OpenAI’s models and eliminating Microsoft’s revenue-share payments, according to reports at the time. Each amendment loosened the tie. Each one was followed by more MAI models.How Microsoft’s in-house models are cutting costs across Teams, Word, and ExcelThe second half of the answer is margin. Every prompt Microsoft routes to an OpenAI model carries a cost. Every prompt it routes to its own model on its own GPUs carries a smaller one. In July, Bloomberg reported that Microsoft had begun using MAI models to answer a portion of user prompts in Word and Excel — products it had previously advertised as powered by OpenAI and Anthropic. TechCrunch framed the shift as part of a broader industry pullback on AI spending, with Amazon, Uber, Meta, and Accenture all reportedly trimming.Transcription is the natural first target for this substitution because the problem is bounded and the metric is objective. Microsoft owns Teams, which generates an enormous volume of meeting audio. It owns Nuance, whose clinical documentation business runs on speech recognition. It owns the Azure speech services that thousands of enterprises already call. Every one of those workloads is a candidate to move onto MAI-Transcribe-2, and every hour that moves is an hour Microsoft no longer pays anyone else for.Suleyman has been unusually candid that this is the point. Superintelligence, he told The Verge in April, “is really about, ‘Are these models capable of delivering product value for the millions of enterprises that depend on us to deliver world-class language models?'” Whatever one thinks of applying the word “superintelligence” to a transcription API, the commercial logic is plain: build the capability once, deploy it across a dozen products, and stop writing checks to a partner that is increasingly a competitor.MAI-Transcribe-2 vs. OpenAI, Google, and ElevenLabs: the competitive pictureMicrosoft’s release names four rivals: OpenAI’s GPT-Transcribe, Google’s Gemini 3.5 Transcribe, OpenAI’s older Whisper V3-Large, and ElevenLabs’ Scribe v2. It does not mention Deepgram, AssemblyAI, Speechmatics, or Rev — the specialists that have sold transcription to enterprises for a decade. Microsoft is positioning against the frontier labs, not the incumbents.That framing is partly marketing and partly true. The frontier labs have treated speech as a checkbox feature of broader platforms, priced accordingly, and a dedicated model that beats them on speed by five to 10 times while matching their accuracy is a genuine differentiator. But the specialists will feel the price pressure most acutely. At $0.10 an hour, Microsoft is pricing at or below where many of them sell high-volume enterprise contracts, and it is bundling diarization, timestamps, and 60 languages into the base rate. The specialists’ remaining moat is domain depth — medical vocabularies, legal formatting, industry-specific integrations — and Microsoft’s keyword biasing feature is aimed squarely at it.The one competitor Microsoft conspicuously does not claim to beat on accuracy is Alibaba, whose models have posted leading numbers on independent leaderboards for much of 2026. TechCrunch reported in July that some U.S. companies had begun evaluating Chinese models as cheaper alternatives despite security concerns. Microsoft’s pitch to those buyers is implicit but unmistakable: comparable accuracy, faster inference, lower price, and a vendor your compliance team already trusts.The questions technical decision makers should ask before switching transcription vendorsFor all its specificity on benchmarks, the release leaves several practical questions open. The first is duration: Microsoft calls $0.10 per hour a launch offer without naming an end date or a standard rate, and anyone building a cost model should get both in writing. The second is streaming. The release emphasizes batch throughput and long-form audio but says nothing about real-time transcription, which voice agents and live captioning require. Artificial Analysis maintains a separate streaming leaderboard, and Microsoft’s silence on it is notable.The third is per-language accuracy. A 5.2% average across 60 languages could mean 3% on major languages and 12% on low-resource ones, so buyers with specific needs should test those languages directly. The fourth is diarization quality. Word error rate does not measure speaker attribution; a transcript can have near-perfect WER and still assign every other sentence to the wrong person. The release offers no diarization error rate or comparable metric.The fifth is data handling. Enterprise transcription touches medical records, legal privilege, and financial disclosures, and the release says nothing about data residency, retention, or whether audio submitted to Foundry feeds future training. Microsoft’s April announcements described training data as a mix of human-curated recordings, contractor-recorded noisy audio, and “vast amounts of data from the open web,” per The Verge — a description that should prompt pointed questions from regulated industries. None of these gaps is unusual for a launch announcement, but they are exactly the questions that separate a leaderboard win from a production deployment.What Microsoft’s speech model strategy reveals about its broader AI ambitionsStep back from the speech-recognition details and a pattern emerges that extends well beyond transcription. Microsoft’s AI unit now ships models for images, voice, transcription, code, reasoning, and cybersecurity. At Build in June, it announced seven new MAI models in a singlekeynote. Each follows the same playbook: target a well-defined modality, optimize aggressively for inference cost, price below the frontier labs, distribute through Foundry, and quietly swap the model into Microsoft’s own products.This is not an attempt to build one model that beats GPT or Gemini at everything. It is an attempt to build a portfolio of specialized models that, in aggregate, let Microsoft serve most of its enterprise workloads without paying anyone else — and to sell the surplus capacity to everyone else at prices the specialists cannot match. Transcription happened to be the first modality where the approach fully matured, but the release notes for MAI-Transcribe-2 read less like a product announcement than a template.Suleyman has spent two years talking about “humanist superintelligence” and AI assistants that are “accountable to them, on their side.” The vocabulary is lofty. The execution is a spreadsheet. Five months ago, Microsoft charged 36 cents to turn an hour of speech into text. On Thursday it charged a dime, threw in six features its rivals sell separately, and claimed the top spot on the industry’s standard multilingual benchmark. The company that spent $13 billion learning what frontier AI costs has decided it would rather own the factory than rent the output — and now it is selling the output for less than the rent.MAI-Transcribe-2 is available now through Microsoft Foundry and MAI Playground.

ProShares S&P 500 Dividend Aristocrats ETF (NOBL), barely moved on September 1 while the broader market sold off hard. Wall Street’s first trading day of September turned into a rout, and the fund’s calm stood out against it.

The selling followed a fresh escalation between the United States and Iran that pushed oil prices sharply higher and sent government bond yields climbing, according to a market recap from Alain Guillot.

Every major index closed lower, but NOBL fell only a fraction of what stocks broadly lost.

Related: Schwab SCHD draws $679M as dividend ETF climbs 2.39%

NOBL barely reacted while stocks sold off hard

The S&P 500 dropped 0.7% on September 1 to close at 7,631.47, and the Dow Jones Industrial Average lost 419 points, or about 0.8%, to finish at 52,766.88, according to CNBC’s recap.

The Nasdaq Composite fell roughly 1% to close at 26,099.77. Technology stocks led the decline, with cybersecurity firms among the hardest hit.

NOBL closed the session at $57.64, down about 0.4%, according to ProShares’ own fund data. That decline was roughly half the size of the S&P 500’s drop and less than half the size of the Nasdaq’s, a gap that shows dividend-focused strategies picking up support just as tech sentiment turned sour.

ProShares’ Dividend Aristocrats ETF fell just 0.4% on September 1 as the Nasdaq dropped 1% and the S&P 500 lost 0.7% in a tech-led selloff.TIMOTHY A. CLARY / Getty Images

The fund’s structure limits its tech exposure

NOBL’s resilience traces back to how it is built. The fund equally weights all 69 of its holdings rather than letting a handful of giant companies determine its returns, and no single sector can exceed 30% of the index, according to ProShares.

That structure is why the fund was not dragged down by the same mega-cap technology weakness that hit the Nasdaq hardest.

That construction pushes technology down to roughly 3% of the portfolio, while consumer staples and industrials each make up about a fifth of the fund, according to a July analysis from 24/7 Wall Street.

None of the Magnificent Seven companies qualify for the index, since two of them (Amazon and Tesla) do not pay a dividend at all.

More Dividend Stocks:

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Does Sandisk pay dividends? Will it split its stock?

Dividend growth matters more than headline yield

NOBL’s 30-day SEC yield sits at 2.14%, modest next to some high-yield ETFs, and the fund charges a 0.35% expense ratio while holding $11.76 billion in net assets, according to ProShares.

What the underlying index screens for is not yield, but a 25-year streak of dividend increases, a bar that filters out companies with shaky cash flow.

The S&P 500 Dividend Aristocrats Index returned 5.89% over the three months ended July 31 and 11.05% year to date, trailing the broader S&P 500’s stronger tech-driven gains during that stretch, according to ProShares.

That lag is the tradeoff investors accept for smoother days like September 1.

A few additional data points frame the broader shift toward dividend strategies:

Dividend-focused ETFs pulled in $24.1 billion in the first quarter alone, a pace that could set a record for the full year, according to The Motley Fool.

A 10-year Treasury yield near 4.76% makes bonds more competitive with expensive growth stocks, pressuring the same technology names that have carried the market for years, according to Yahoo Finance.

NOBL trades close to its 52-week high near $58, even after years of lagging the S&P 500’s tech-heavy gains, according to 24/7 Wall Street.

A quiet rotation could reshape market leadership

One calm trading day does not prove dividend aristocrats have taken over market leadership.

But the pattern fits a broader shift already visible in fund flows, where investors increasingly want proof of durable cash generation rather than a promise of future AI payoffs.

If oil prices and rate worries keep pressuring growth stocks through September, historically the weakest month for stocks, funds like NOBL may keep drawing steady interest.

Not because they are exciting, but because they are boring in exactly the way nervous investors want right now.

Related: Dividend ETFs paying 2% to 3.7% for your portfolio

Southwest Airlines has officially entered its “premium brand” era.

The long-time family-friendly airline has already decidedly shifted away from the “free checked bags, open seating and discount pricing” model that had made it a favorite of budget-conscious travelers over recent decades.

And now it is poised to take the next major step in its evolution into a full-fare, premium airline.

Southwest announced it will construct new airport lounges and launch an accompanying premium credit card in 2027.

Major domestic airline competitors Delta, United and American each have similar “premium credit card for luxury lounge access” programs in place.

The details of Southwest’s new ambitions in this space, as first reported by The Wall Street Journal, are somewhat limited at this time. Let’s take a look at what we know so far.

Southwest Reveals Plans for New Airport Lounge Network

Southwest has started construction on a new network of premium lounges designed to lure more premium travelers into its customer base.

The plan is to open the first wave of lounges by late 2027, according to the Wall Street Journal report.

The first four airports to receive a Southwest lounge will be:

Austin, Texas

Baltimore, Maryland

Nashville, Tennessee

Honolulu, Hawaii

The longer-term plan includes a network of “at least 11 lounges” that will expand to additional airports not yet named.

According to the WSJ story: “The airline believes it can match—or exceed—the level of luxury its rivals offer, with signature bars and chef-inspired meals.”

Chase Will Back New Premium Co-Branded Southwest Credit Card

Alongside the announcement that the lounges are coming in 2027, Southwest also revealed it will have an accompanying premium travel credit card.

Per the report, the lounges will “primarily be available to travelers with the new Southwest premium credit card.”

Southwest will partner with credit card giant Chase to co-brand this new travel credit card.

Details on what the card will offer in terms of rewards, benefits and perks are still a mystery. So, too, is how much the card will cost Southwest loyalists each year.

But we can expect that it won’t come cheap.

For example, the Chase Sapphire Reserve®, which offers cardholders access to its network of Sapphire-branded lounges, has a $795 annual fee.

And premium credit cards from competing airlines that offer lounge access cost the following:

Delta SkyMiles® Reserve American Express Card: $650

United ClubSM Card: $695

Citi® / AAdvantage® Executive World Legend Mastercard®: $695

These numbers are quite a jump from the annual fees of Southwest’s existing trio of co-branded consumer credit cards offered by Chase, which range from $99 to $229.

Will you consider jumping to the premium Southwest lounge space for your future travels? Or are you frustrated with the direction of the brand? We’d love to hear your thoughts in the Clark.com community.

All information about Chase Sapphire Reserve®, Delta SkyMiles® Reserve American Express Card, United ClubSM Card and Citi® / AAdvantage® Executive World Legend Mastercard® has been collected independently by Clark Howard, Inc and has not been reviewed or provided by the issuer or provider of this product or service.
The post Southwest Reveals Plans for New Airport Lounges and Premium Credit Card appeared first on Clark Howard.

One crazy thing I have learned from technology companies is that the first narrative is rarely the final one. Cybersecurity has proved that. When AI took the podium and every headline, the fear was that it would eat into the very businesses built to defend against increasingly sophisticated threats. 

For a while, it looked like the security vendors had more to fear from AI than to gain from it. Nikesh Arora, the CEO of the global AI cybersecurity leader, Palo Alto Networks (PANW), himself admitted as much on Mad Money Sep. 2 night.

Nine months ago, I said we were guilty and convicted of near-death because AI was going to eat our lunch, breakfast and dinner.

Nikesh continued to tell Jim Cramer, “It seems like that’s not the case. Seems like we’re going to have to have the feast with them.”

The twist is that AI did not kill cybersecurity as most of us thought. It radicalized the threat environment, which means the demand for serious, AI-native security platforms is actually accelerating. And Arora had a number that should make every IT executive stop scrolling.

Also Read: Palo Alto Networks Inc. Latest News and Stories

The $1 trillion problem Arora is describing

Cramer put the warning as: “You’re talking about the need for accelerating the urgency to modernize $1 trillion of legacy security. That just means hackable security, $1 trillion worth.”

Nikesh explained the math. 

“If the average life is seven years and you’re spending $200 to $300 billion a year, you’ve got $1 trillion of security infrastructure that’s out there,” he said. “Nothing that was deployed seven or 10 years ago is prepared or ready to handle AI at machine speed.”

And that is the structural argument behind Palo Alto’s entire growth thesis right now. Legacy firewalls, security information management systems, and endpoint tools built before generative AI existed were not designed for adversaries operating at machine speed. 

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Bad actors are already using AI to generate attacks faster, more variably, and at greater scale than human security teams can respond to manually.

And then Nikesh added a second number on top of the first: “On top of that, you’re going to see $5 trillion of capex spend in the next five years with people building AI data centers and having tons and tons of agents running around. You also have to build a net-new security stack for that.”

So the addressable market is not just modernizing the existing $1 trillion of legacy infrastructure. It is also securing the $5 trillion of new AI infrastructure being built simultaneously.

Palo Alto’s Q4 fiscal 2026 showed a company catching that wave

The Q4 results, reported September 1, validated the demand environment Arora described on Mad Money.

Total revenue grew 34% year-over-year (YOY) to $3.41 billion. 

Next-Generation Security ARR (NGS ARR) grew 63% YOY to $9.10 billion, adding nearly $1 billion in a single quarter. 

Remaining performance obligations grew 34% to $21.2 billion. Source: Palo Alto Networks Q4 and Full Year 2026 Results

CFO Dipak Golechha said the “profitable growth framework continues to scale effectively,” reinforcing confidence in a 40% adjusted free cash flow margin target for fiscal 2028.

That means we are more likely to see the stock continue surging, too. Currently, PANW is up 76.66% year-to-date, according to Yahoo Finance.

For Q1 fiscal 2027, Palo Alto guided total revenue of $3.30-$3.31 billion, up 33%-34% YOY, with NGS ARR of $9.54-$9.56 billion, continuing the 63% growth rate. Based on 45 analysts’ ratings of Palo Alto Networks in the past 3 months, 38 recommended buy, 7 hold, and 0 sell, TheStreet reports.

Related: Jim Cramer has a strong message for Nvidia stock investors

Full-year fiscal 2027 guidance calls for $14.10-$14.20 billion in revenue, up 23%-24%, and NGS ARR of $11.075-$11.175 billion on the path to the $20 billion fiscal 2030 target.

On the same earnings day, Palo Alto announced the acquisition of Console. It is an AI-native agentic platform designed to help organizations resolve alerts and security issues at machine speed. 

This addresses the “AI at machine speed” threat Arora described. Palo Alto also completed the acquisition of Chronosphere in Jan. 2026. This one focused on AI-driven observability, targeting the AI security operations and IT operations market.

Palo Alto Networks reports a 78% growth in AI usage over the last 12 months, with 94% of enterprises currently utilizing Generative AI software.Shutterstock

The phone calls Arora is now receiving and what they mean

There is a detail from the Mad Money interview I find most revealing about where enterprise technology priorities sit right now, and where they’re headed. Why?

Arora said he is now on the receiving end of calls rather than making them. More than 2,000 companies have reached out about cybersecurity in the context of AI deployment. 

Every organization rushing to build AI infrastructure is realizing, often for the first time, that deploying AI agents across a corporate environment without a modernized security architecture is a serious risk.

“The Mythos moment has just become a net new beginning for the cybersecurity industry,” Arora noted, “because the world has realized that we have to pay attention to cyber because AI is going to be weaponized by bad actors.”

And I think the transition from selling to receiving calls is the leading indicator that the $1 trillion modernization cycle has actually begun. 

Palo Alto is the largest pure-play cybersecurity platform company in the world, with the most comprehensive portfolio to address both the legacy refresh and the new AI security stack Arora is describing.Palo Alto Networks reports a 78% growth in AI usage over the last 12 months, with 94% of enterprises currently utilizing Generative AI software. The stock is up 76.66% year-to-date, suggesting the market already agrees. The $1 trillion problem and the $5 trillion capex wave say the story is not finished.

Related: Jim Cramer has strong message for Micron stock investors

There’s nothing risk-free in dollars, their digital derivatives, or the securities that pay out dollar income streams.