Amazon is expanding access to a program called Shop Direct that lets U.S. customers discover and buy products not sold in its own online store.

The retail giant on Wednesday said it will now support third-party product feeds, which merchants use to provide information about their inventory, pricing, and catalog to other partners. With this information, Amazon can direct shoppers to a merchant’s website via its search results or its AI shopping assistant, Rufus, and even let customers use AI to make a purchase.

The company has added support for third-party product feeds from Feedonomics, Salsify, and CEDCommerce, which provide Amazon access to merchants’ inventory and product information in real time. More feed providers will be supported in time, and an Amazon merchant portal with a merchant-direct feed is said to be coming soon.

In February 2025, Amazon began beta testing a new shopping feature that would link to a retailer’s website when its own search results didn’t include the product the customer was seeking. Customers would see the product information on Amazon, but could click through to the retailer’s site to learn more, check pricing, and view delivery options. Customers would be notified that they were leaving Amazon’s website so they wouldn’t be confused into thinking they were buying from the company itself.

The program was being offered to a range of brands and wasn’t limited to partners using Buy with Prime — a way to offer checkout using a customer’s saved payment information on Amazon.

While the move to be included on Amazon could certainly boost a brand’s exposure and potential sales, it could also give Amazon insights into which brands, products and price points are most appealing to customers. The company could use this information to improve its own business by providing data on competing products, tracking trends, identifying potential Buy with Prime partners and more.

It could also help Amazon solidify itself as the starting point for product search.

The company says it now supports Buy for Me, which has Amazon use an AI agent to complete purchases, on third-party merchant sites as well.

The AI bot handles the entire purchase process on the customer’s behalf, and the customer simply has to confirm their order details on the checkout page, including their delivery address, taxes, shipping fees, and payment method. Amazon’s AI then completes the checkout from the merchant’s website using the required information.

Customers can track these orders in the same “Your Orders” tab where they track their Amazon purchases, or in a special “Buy for Me Orders” tab.

Shop Direct is live for U.S. customers on Amazon.com, in the Amazon mobile app, and in Amazon’s Rufus AI assistant.

Elyena Lee’s personal journey into motherhood sparked the business idea

When Elyena Lee, 34, had her first child during COVID-19 in early 2020, she struggled to find baby products that matched her personal style.

Back then, she recalled, most options in Singapore leaned heavily toward “kiddish” aesthetics, and high-quality organic essentials from overseas often came at an extremely premium price point. 

Frustrated with the lack of accessible choices, she started her own baby brand with a university friend that same year: Soft Spot. Today, Soft Spot sells around 20,000 products annually—and interestingly, many of its products are also purchased by people without children.

We spoke to Elyena about how her personal journey into motherhood has grown into a global baby brand, with a presence in 13 countries.

It started as an online business

Soft Spot began as an online business after months of ideation. Its first product was the Soft Swaddle, made out of muslin fabric, available in one pear print and five solid colours.

But Elyena didn’t want Soft Spot to remain solely an online brand—she aimed to establish a physical presence as well. She reached out individually to retailers, and just a few months later, Mothercare outlets began stocking Soft Spot’s products.

As the brand grew, it gradually expanded its product range and introduced more colourways. After all, the founder’s main gripe with existing baby products was that they weren’t aesthetically pleasing.

“What differentiates us from other baby brands is that we identify the mom as the main character, rather than the baby,” Elyena explained, adding that Soft Spot’s products particularly speak to millennial and Gen Z mothers who still care about their sense of style and identity.

Hence, she introduces new products typically every three months.

Finding an audience beyond parents

Over time, Soft Spot has grown to 27 product lines—from bibs to baby apparel and even bed sheets—with over 300 different colourways and patterns, all created by a team of in-house designers Elyena has hired over the years. Prices start from S$29 for its products.

The brand also discovered an unexpected audience that went beyond babies. Thanks to its soft cotton material and pretty aesthetics, the products began to be used in a variety of ways beyond their original purpose.

Mothers and even non-parents began buying Soft Spot’s single bed sheets for their design, while couples without children purchased Soft Squares—originally burp cloths—as handkerchiefs or Soft Swaddles as bath towels for their suitability for sensitive skin. 

“Some customers even used swaddles as picnic mats or beach wraps!” Elyena exclaimed.

With demand rising, Elyena, who had spent eight years in the FMCG industry at multinational giants like Unilever and L’Oréal in brand and product development roles, decided to leave her full-time job in 2023 to run Soft Spot solo.

Expanding Soft Spot’s presence in Singapore and beyond

Over the years, more retailers in Singapore began stocking Soft Spot, including Tangs, Frankie & Fern’s, and A Greener Wood.

The brand also expanded its physical presence through pop-ups, such as the Christmas Atelier in 2024 and 2025, and a three-month test pop-up at Phoenix Park in Tanglin in Sept 2024.

Soft Spot has leveraged partnerships as well. One notable collaboration in 2025 paired its Soft Loaf Pouch with Anessa’s sunscreen.

That said, the brand still maintains a strong online presence, with products available not only through its own website but also via partner retailers like Stacked Store and Hipvan. This reflects Soft Spot’s vision of being “more than a baby brand,” extending its signature aesthetic into the modern home.

Internationally, Soft Spot first made its mark when it was stocked at French family concept store Smallable in 2022, which, according to Elyena, has a “strong online presence” in both European and US markets.

In addition, Soft Spot secured distributors and now stocks its products in 13 countries worldwide, with retailers spanning from Taiwan to Saudi Arabia.

A permanent retail store is not on the cards

Currently, Elyena shared that Soft Spot sells about 20,000 items a year.

Despite this growth, a permanent retail store is not on the cards for now due to Singapore’s challenging retail environment. For the time being, pop-ups and strengthening its online presence offer the right balance for the brand.

“As a small brand, our only advantage is speed and flexibility,” shared Elyena.

She hops on trends to stay relevant and takes an experimental approach, negotiating lower test quantities with suppliers to reduce risk. But while she moves quickly, she ensures that every release meets her standards.

Coming from a multinational corporation background, Elyena admitted that entrepreneurship was initially a culture shock for her.

“In an MNC, there’s always someone who specialises in every field,” she says. “As a founder, you have to go into all areas with no experience, such as vetting legal documents, accounting, IT glitches—everything—by yourself.”

There is no boss, no historical data to reference, and no one to dictate strategic direction. While it can get lonely at times, the journey has allowed her to measure her own success at her own pace, and she is proud of how far she has come.

Looking ahead, Elyena wants to expand beyond baby products into more categories. Her ultimate goal? To serve the “modern family” with more home and living items, on-the-go essentials, and potentially bags and clothing.

• Learn more about Soft Spot here.
• Read other articles we’ve written on Singaporean businesses here.

Featured Image Credit: Soft Spot

from the golden-era-for-weird-assholes dept

The President of Larry Ellison’s “new and improved” Paramount, Jeff Shell, has been conspicuously absent from recent events heralding the company’s problematic acquisition of Warner Brothers. The reason? Shell is being accused by a “whistleblower” and former partner of leaking company info, including early word of the company’s $7.7 billion August 2025 deal to obtain the exclusive rights to stream MMA fights.

Shell, previously fired by Comcast for sexual harassment allegations, allegedly had a… complicated relationship with the man, R.J. Cipriani. Cipriani claims to have been a “crisis communications” specialist who helped Shell plant favorable stories in the media in exchange for Shell’s promise to help fund a TV show. An internal Paramount investigation into the claims is ongoing.

But Cipriani is also now suing Shell $150 million for not following through on his promises:

“The plaintiff, R.J. Cipriani, alleges in the lawsuit that he had a relationship with Shell for 18 months, in which Cipriani would tip Shell off to forthcoming news articles and offer advice. The suit also alleges that Shell would share non-public information with Cipriani about Paramount’s plans.”

The whole story is an interesting read, and includes claims that Shell told Cipriani that Paramount significantly overpaid for Warner Brothers. And that Cipriani seeded the trade press with lots of information favorable to Paramount, including some allegedly peppered into this June 2025 story about a potential fight between South Park’s creators and Paramount.

Nobody in the story comes off as having particularly sound judgment. You also wonder, if Cipriani’s claims are true, who are the people at these media companies who are so easily manipulatable.

Shell was placed in the president spot at Paramount shortly after the previous CBS owners bribed Trump to ensure that Skydance could acquire the company. Shell was highly representative of the new “anti-woke” bro culture at Ellison’s Paramount, which I think was dissected pretty well by this Hollywood Reporter piece last year:

“It’s an echo of the feelings-don’t-matter, no-coddling ethos that powers Silicon Valley, where Ellison was raised and watched his father, Larry Ellison, grow Oracle into one of the most valuable companies in the world (and make himself one of the richest people on the planet). Multiple sources say Ellison is building a more brash culture that’s defiantly upending the circumspect, politically correct style that has defined Hollywood in the post-#MeToo, post-George Floyd eras. It’s a studio reborn, where blunt feedback is the norm, canceled talent is welcome (cheaper on the dollar, and yearning to prove themselves) and no one is walking on eggshells.”

If Shell and Cipriani’s behaviors are any indication, it sounds like the decision to purge the company of ethics and empathy is going great so far. And this was before all the disastrous stuff by Bari Weiss at CBS News, the massive layoffs at CBS overseen by Shell, and the most recent decision by Larry Ellison to gift his nepobaby son with a second major Hollywood studio on the back of Saudi and Chinese cash.

I bring all of this up because the previous three mergers related to Warner Brothers (spanning two decades) have been absolute disasters. Usually because the people acquiring the company were broadly incompetent (see: AT&T), had terrible judgement, and bit off way more than they could chew in terms of both depth, collaborative creation, and competency.

With a mammoth $111 billion price tag for Warner Brothers, thrown atop the debt acquired through the CBS and other deals, this new Paramount is a towering mountain of financial obligation that’s going to result in dysfunction, layoffs, and chaos likely to make past Warner deals seem quaint. All overseen by people who apparently (and quite proudly) have some of the worst judgment imaginable.

Get your popcorn ready.

Filed Under: consolidation, david ellison, insiders, jeff shell, journalism, larry ellison, manipulation, media, mergers, rj cipriani

Companies: paramount

Toby, a game developer known online as Game of Tobi, has managed to port the Nintendo 64 classic The Legend of Zelda: Ocarina of Time to the Apple Watch. This legendary game is known for its massive world and devilishly difficult puzzles, all of which make it onto the tiny wearable in a version that pushes the device’s capabilities far beyond what most people would have thought.

Toby built on the ship code for the Ship of Harkinian project, also known as the Shipwright project on GitHub. This tool completely decompiles the original game code, allowing developers to transfer the game to newer systems without using traditional emulation. Given that the Apple Watch is essentially a little computer with access to some great graphics tools, Toby was able to adapt the codebase to operate on watchOS. He used SceneKit for visuals, which handled the game’s 3D world seamlessly on the small watch screen.

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The gameplay progresses quickly, with Link tramping around Hyrule Field, wielding his sword, and navigating the various environments with no notable slowdowns. The menus appear as expected, objects are used properly, and battle runs well. There is a settings option that allows you to pick between a full-screen display that fills the watch face and the original rendered output, which both work.

Controls are probably the trickiest part, since the watch is essentially just a touchscreen and a digital crown, so Toby came up with a virtual button system to handle things like jumping, fighting, and using items. Yes, your fingers will cover part of the screen, but it becomes second nature after a little practice. You can technically pair a Bluetooth controller, though the Apple Watch is really built for one hand, which makes two handed play a bit of an awkward experience.

There are a few bugs still hanging around though, most noticeably some inconsistent text rendering that can make dialogue and signs a little hard to read. Toby is working on ironing those out before any wider release. Interestingly, he had already ported Super Mario 64 to the Apple Watch before this, and that experience seems to have helped him sidestep a few of the pitfalls he ran into here.
[Source]

It’s always the same story: A new technology appears and everyone starts talking about how it’ll change everything. Then capital rushes in, companies form overnight, and valuations climb faster than anyone can justify. Then, many many months later, the warnings arrive, and people suddenly remember the dot-com crash or crypto.

You’ve probably seen it before. And if you have, you probably think AI is the next bubble. Humans are great at pattern-matching. We’ve evolved to see patterns, so when something familiar emerges, we instinctively map it onto the closest story we already know. We think we’ve seen it before, and we’re confident we know how it ends.

But that instinct can mislead us. AI feels like a bubble because we’re forcing something genuinely discontinuous into a familiar story. The idea that everything that rises quickly must ultimately collapse sounds prudent. But it doesn’t mean it’ll always be true.

Why markets keep overshooting

Every major technological shift produces the same outward symptoms: Inflated expectations, followed by high-visibility failure. Dot-com, mobile, and crypto all went through a phase where the world lost its sense of proportion.

Why does this keep happening? Because markets don’t have a framework for discontinuous change. Discounted cash flow models assume steady, stable growth, and comparable companies assume the category already exists. So people assume the near future looks like the recent past, but that doesn’t work when the underlying category itself is changing.

Most valuation tools are designed for incremental progress, so analysts look at quarterly forecasts and incremental improvements. They don’t know what to do with step changes, and they can’t model nonlinear adoption.

So when you see capital overshooting or extreme dispersion of outcomes, that’s the market trying to value decade-long bets using quarterly logic. (Which doesn’t work.) And that’s what a bubble actually is: An indication that no one yet knows how to price what’s coming. That uncertainty looks like invalidation, but it just exposes the limits of existing frameworks.

The category error we keep making

When something new arrives, we reach for comparisons.

AI is like electricity.

AI is like computers.

AI is like the internet.

AI is like mobile.

These comparisons are comforting because they all produced massive, economy-wide change, and attracted enormous capital. They changed how work got done.

They also share something deeper. Every one of those technologies extended human capability without replacing human cognition. Electricity powered machines, but humans still decided what to build. Computers processed data, but humans interpreted it. The internet moved information, but humans decided what mattered. Mobile put computing in your pocket, but human attention remained the scarce resource. In every case, human intelligence anchored everything. It was also the bottleneck.

AI is different because it performs cognitive work. And if that makes you uneasy, it should. Because if AI can actually think, then a lot of what we’ve built our careers on, like our expertise and our hard-won skills, might not be as defensible as we thought. The junior engineer who spent years developing intuition now works alongside a tool that has it instantly. So does the financial analyst known for their variance analysis. People aren’t completely sure where value actually lives anymore, and that’s terrifying.

I talk to CFOs every week. Six months ago, they asked me abstract questions like “what is AI?” and “should we have an AI strategy?” Now the questions are concrete: “Which parts of my team’s work no longer need to be done this way?” That shift happened so quickly, it’s already changing how resources get allocated.

For example, a founder I know started using Claude to write SQL queries that used to take her analyst a couple of days. Did she replace the analyst? Of course not. But she removed the bottleneck, and doesn’t have to depend on him anymore for quick answers. Then her analyst’s role changed completely. He went from spending 60% of his time writing queries to 10% checking them and 90% on strategic recommendations. The company didn’t reduce headcount or costs, and the analyst went from supporting three stakeholders to supporting fifteen.

This is where historical comparisons really start to fail. Tools like GitHub Copilot are compressing expertise. A junior engineer can now operate at a level that once required years of work experience. And every time the tool is used, it learns. A hammer doesn’t improve just because you built a house with it, but AI tools do. And when tools get better through use, the rate of improvement compounds. That dynamic doesn’t fit cleanly into any prior technological analogy, which is why the instinct to call this a “bubble” misses the actual point.

Previous technologies assumed a fixed ceiling on human cognition. They made us faster and stronger, but the limiting factor was always the same: How many smart people could we put on a problem? AI stretches that ceiling way beyond what we’re used to. Before, understanding your business better usually meant one of three things: More data, more analysts, or more experienced leaders. The constraint was how much human attention and judgment you could afford. With AI, that constraint shifts. When analysis that once took days appears in seconds, the new constraint is knowing what to look for. What questions matter? The limiting factor stops being talent and starts being judgment.

The skeptics are right about the hype, and wrong about what it means

Let’s take the strongest version of the bubble argument at face value. Maybe AI actually is overhyped, and most of these companies will fail. Maybe we’re early, and real impact takes another five or ten years. All of that could be completely true, and it still wouldn’t change the core point, which is this:

Even if the majority of AI startups fail, and even if adoption is way slower than expected, AI is still the first technology that can perform knowledge work. That doesn’t disappear because markets overshoot or expectations reset. The skeptics are right that the hype is inflated. But they’re wrong that inflated hype makes the technology irrelevant. We’ve seen this before: The dot-com bubble was real, and Pets.com crashed and burned, but the internet still changed everything. Both things were true at the same time.

The finance leaders I’m working with are beyond arguing about whether AI matters. Now they’re trying to understand which workflows change first, and how fast they need to adapt. That conversation is happening quietly, underneath all the noise.

And the workflows collapsing first share three properties:

1. They require expertise, but they’re repetitive.

2. They’re bottlenecks to strategic work.

3. They’re easy to verify but hard to generate.

These workflows are important enough to pay for, but not so strategic that automating them threatens competitive advantage. They require skill, but that skill doesn’t compound dramatically with repetition, which makes them economically fragile, and explains why they’re already being automated away.

Where humans still matter (for now)

AI is great at recognizing trends, and terrible at knowing which ones actually matter. It can generate variance analysis, but it can’t tell you whether a 12% swing in spend signals healthy growth or a deeper problem. It can draft strategies, but it can’t tell you which strategy fits this market and this team in this exact moment. Judgment under uncertainty, and high-stakes tradeoffs where the downside is catastrophic, remain human responsibilities. For now.

When the constraint is no longer “do we have enough smart people,” the problem becomes one of priority. What deserves attention? What’s worth building next? That’s where I see many founders get stuck. They ask if this is a bubble and if they’re too early, but those aren’t the most useful questions. The right one is: “What can I build in the next year that creates real value, regardless of what valuations do?”

The companies that last will be the ones quietly iterating and embedding AI into actual workflows that solve actual problems. Take CFOs, for example. They’re buying AI because their board wants faster variance analysis, and they’re tired of hiring analysts who quit after six months. That’s a real-world problem that companies need to solve.

And the same is true for investors. The ones who succeed long-term will be those who tolerate uncertainty long enough to see what actually works.

This time is actually different

In the short term, AI will disappoint. Many use cases won’t deliver what they promise, and a lot of companies formed in this wave won’t survive. But the technology will. And, over the long term, AI will reshape every field that depends on knowledge work. Not all at once, and not evenly, but a decade from now, it will be difficult to find a knowledge-based industry that looks the same as it does today.

AI is different because intelligence itself, which was historically the core constraint of human innovation, has now become scalable. That’s an observable fact with measurable consequences. The conversation about bubbles will fade, as it always does, and what will remain are the systems that quietly adapted while everyone else argued about valuations. The skeptics will have been right about the excess, and wrong about what actually mattered, because, five years from now, we’ll probably look back at today’s panic the same way we look back at people who dismissed the internet because a handful of companies failed. And the winners will be those who were building while everyone else argued about valuations.

In time, those are the only stories anyone remembers.

Siqi Chen is co-founder and CEO of Runway.

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Hewlett Packard Enterprise (HPE) has patched multiple security vulnerabilities in the Aruba Networking AOS-CX operating system, including several authentication and code execution issues.

AOS-CX is a cloud-native network operating system (NOS) developed by HPE subsidiary Aruba Networks for the company’s CX-series campus and data center switch devices.

The most severe security flaw today is a critical authentication bypass vulnerability (tracked as CVE-2026-23813) that attackers without privileges can exploit in low-complexity attacks to reset admin passwords.

“A vulnerability has been identified in the web-based management interface of AOS-CX switches that could potentially allow an unauthenticated remote actor to circumvent existing authentication controls. In some cases this could enable resetting the admin password,” HPE said.

“HPE Aruba Networking is not aware of any public discussion or exploit code targeting these specific vulnerabilities as of the release date of the advisory.”

IT admins who can’t immediately apply today’s security updates to patch vulnerable switches can take one of the following mitigation measures:

• Restrict access to all management interfaces to a dedicated Layer 2 segment or VLAN to isolate management traffic.
• Implement strict policies at Layer 3 and above to control access to management interfaces, allowing only authorized and trusted hosts.
• Disable HTTP(S) interfaces on Switched Virtual Interfaces (SVIs) and routed ports wherever management access is not required.
• Enforce Control Plane Access Control Lists (ACLs) to protect any REST/HTTP-enabled management interfaces, ensuring only trusted clients are allowed to connect to the HTTPS/REST endpoints.
• Enable comprehensive accounting, logging, and monitoring of all management interface activities to detect and respond to unauthorized access attempts.

HPE has yet to find publicly available proof-of-concept exploit code or evidence that attackers are abusing the vulnerabilities in the wild.

In July 2025, the company also warned of hardcoded credentials in Aruba Instant On Access Points that could allow attackers to bypass standard device authentication.

One month earlier, HPE patched eight vulnerabilities in its StoreOnce disk-based backup and deduplication solution, including another critical-severity authentication bypass and three remote code execution flaws.

More recently, in January, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) flagged a maximum-severity HPE OneView vulnerability as exploited in attacks.

HPE has over 61,000 employees worldwide, has reported revenues of $30.1 billion in 2024, and provides services and products to over 55,000 enterprise customers worldwide, including 90% of Fortune 500 companies.

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The deal pairs one of the world’s most powerful chip companies with the AI startup founded by OpenAI’s former CTO, and the compute commitment alone runs to tens of billions of dollars.

When Mira Murati left OpenAI in September 2024, she declined to say much about what came next. What has become clear, roughly 18 months on, is that she was building something with serious ambitions,  and that she has found in Nvidia a partner prepared to back them at a scale that would have seemed extravagant even a year ago.

On March 10, 2026, NVIDIA and Thinking Machines Lab announced a multiyear strategic partnership under which Murati’s startup will deploy at least a gigawatt of NVIDIA’s next-generation Vera Rubin systems to train its models.

NVIDIA has also made what both companies describe as a “significant investment” in Thinking Machines, though neither has disclosed the figure.

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According to the Financial Times, the chip supply arrangement alone is worth tens of billions of dollars. Nvidia CEO Jensen Huang has previously said that one gigawatt of AI data centre capacity costs up to $50 billion.

Thinking Machines Lab, which Murati founded in February 2025, has now raised more than $2 billion since its inception. Investors include Andreessen Horowitz, Accel, and NVIDIA,  alongside, somewhat unusually, the venture arm of AMD, NVIDIA’s principal chip rival. The company has grown from roughly 30 employees a year ago to about 120 today.

A lab built on customisability

The company’s stated mission is to build AI systems that are, in its own words, “more widely understood, customizable and generally capable.” The emphasis on customisability is pointed: Murati and her team appear to be positioning

Thinking Machines as something distinct from OpenAI and Anthropic, which sell relatively fixed products, by building infrastructure that companies and developers can shape to their own requirements.

The partnership with NVIDIA includes technical collaboration as well as compute supply, specifically the optimisation of Thinking Machines’ products for NVIDIA’s hardware. That kind of close integration at the chip level has historically proved valuable,  it is, in rough terms, part of what allowed OpenAI to move as quickly as it did in the GPT era.

“NVIDIA’s technology is the foundation on which the entire field is built,” Murati said in a statement accompanying the announcement. “This partnership accelerates our capacity to build AI that people can shape and make their own.”

What this signals about the compute race

Thinking Machines is not the only frontier lab signing gigawatt-scale compute agreements. The broader AI industry is locked in a race to secure the infrastructure necessary to train the next generation of models, and the deals being signed now, in some cases before the hardware even exists,  reflect a bet that whoever secures the most compute earliest will have a durable advantage.

For NVIDIA, the investment serves a dual purpose: it generates revenue from chip sales while also giving the company a stake in a lab it clearly views as a potential long-term customer and strategic partner. NVIDIA has made similar investments in other AI companies, building a portfolio that tracks the industry’s frontier.

Murati, for her part, turned down an acquisition offer from Meta’s Mark Zuckerberg last year. The NVIDIA partnership suggests she intends to remain independent, and that she has secured the resources to make that case credibly. Whether a 120-person lab can genuinely compete with organisations ten times its size remains to be seen. But she is no longer short of compute to try.