Biometric authentication is no longer limited to fingerprints and face unlock. Researchers are now exploring whether your earbuds can recognize you simply by listening to the tiny vibrations created by your heartbeat.

A new study published on the arXiv preprint server introduces “AccLock,” a passive authentication system that uses standard earphone hardware to verify a user’s identity. Instead of relying on microphones or voice prompts, the system works through built-in accelerometers already found in many modern earbuds.

Your heartbeat may become your next password

The technology captures heartbeat-induced vibrations inside the ear canal, known as ballistocardiography (BCG) signals. These signals travel through bones and tissues, creating patterns unique to each person. That uniqueness is what makes the system interesting. Once the earbuds register a user’s BCG signal, they can continuously check whether the same person is still wearing them. If another user puts on the earbuds, the authentication fails automatically.

Unlike older earphone-based authentication systems, AccLock does not require users to actively interact with the device. The entire process runs quietly in the background, which could eventually make tasks like unlocking devices, approving payments, or entering smart homes feel almost invisible.

It works well — until too much movement is involved

To improve reliability, the researchers used a deep learning model and a multi-stage denoising system to separate user-specific heartbeat patterns from environmental noise and general body movement. In tests involving 33 participants, the system achieved false acceptance and false rejection rates of 3.13% and 2.99%, respectively, which is fairly promising for an experimental prototype.

However, heavy movement remains a major problem. Walking, talking, or shaking the head significantly increased error rates, showing that the system still struggles in real-world conditions. The researchers also tested the technology on Apple AirPods and found that it remained functional despite hardware limitations. While AccLock is far from becoming a commercial feature today, it offers a glimpse of a future where your earbuds quietly recognize you before you even unlock your phone.

I recently wrote an article on why I am excited about the upcoming MacBook Pro. One of the reasons mentioned there was the expected display upgrades, which will include the move to an OLED panel and quite possibly the addition of a touch screen. 

It seems that the OLED rumor is almost confirmed. According to TheElec, the OLED panels for MacBook Pro have already entered the mass production phase. Samsung Display has crossed a major manufacturing milestone, achieving a yield of over 90% for its 8.6th-generation OLED panels. 

What does yield even mean?

In simple terms, yield refers to the percentage of panels that come out of production without defects. The display industry considers anything around 90% to be the sweet spot for mass production. Samsung has not only hit that number but pushed some individual processes to 95%, which the company calls a “golden yield.”

The process had only a 80% yield last month, so getting here in just over a month is impressive. Higher yields mean lower production costs, which can eventually translate into more affordable products for consumers. Although if Apple’s past record is anything to go by, the cost benefit will not be passed on to its users. 

Samsung Display is currently running one production line at half its total capacity, producing 7,500 sheets per month. The panels are headed straight for Apple’s 14-inch and 16-inch MacBook Pro models, with shipments expected to begin as early as next month and a supply volume of around 2 million units estimated for this year.

Why is making laptop OLEDs so hard?

Laptop OLED displays are significantly harder to manufacture than smartphone panels. They are larger, stay on for longer periods, and require higher brightness, better lifespan, and brightness uniformity across a larger surface area. 

Samsung’s panel uses a two-stack tandem structure that layers two light-emitting layers on top of each other, which is one of the reasons securing high yield has been such a challenge. It’s the same tandem-OLED technology that Apple uses in its iPad Pro lineup, which packs some of the best OLED displays on the market. 

If sales of OLED MacBook Pros go well, Samsung is ready to activate its second production line, which would double output almost immediately. MacBook Neo has already become a runaway success for Apple, and Apple might be looking for a repeat. 

However, the upcoming MacBook Pros are also rumored to receive a significant price hike, which might curtail some enthusiasm.

It’s been 17 years since I sat in on the iconic weekly search quality meeting in the Ouagadougou conference room at Google’s Mountain View campus. That Thursday morning, around three dozen engineers, product managers, and executives sat at a table or sprawled on the floor to discuss why certain search queries or categories didn’t yield a perfect result and to suggest fixes. In 2010 those meetings led Google to make 550 changes to its search algorithm, a number that seemed impressive at the time.

That memory seems like a tintype. At Google’s I/O developer conference this week, a keynote speaker—head of search Liz Reid—officially down-ranked good old-fashioned search to virtual oblivion. This was a continuation of a process that began two years ago, when Google introduced “AI Overview,” its summaries that sit at the top of its search results page and literally lurk over the famous “10 blue links.” By then those links had already been degraded, so that all too often the most relevant ones were buried beneath aggregators, spam, and Google’s own shopping results and maps. Now, in what Reid described as the most significant change to the search box in the company’s history, users are in direct communication with the latest version of Google’s Gemini. Even the term “query” seems outdated, as human inputs are conversation starters for the AI to collaborate. The process can also incorporate personal information Google knows about you, which can be a lot. The answer to a query could be a bespoke presentation, maybe bolstered by AI agents that forage digital backroads to root out information. The transformation is complete. Onstage, Google said it out loud: “Google Search is AI Search.”

The search box used to be a portal to the web. The new “intelligent” box is an invitation to order up a Gemini-powered, customized response to a user’s queries, sometimes even creating on the fly a bespoke mini-publication with charts, bullet points, and even animations. Google used to pride itself on interpreting cryptic search terms to divine user intent. Now it encourages searchers to engage with Gemini in a conversational prompt-a-thon. To emphasize the change, Google representatives at the conference wore T-shirts saying “Ask Me Anything,” reflecting the prompt that Gemini offers. Just as with the computerized version, if you asked for directions from these smiling aides, the answer did not result in a click to a website.

Our digital life these days is perched at an uncomfortable transition point. AI seems to be driving every business model, and giants like Google are weaving AI into all their products and operations. At the same time, there’s rising resistance and even disgust as this powerful and scary technology worms its way into our lives. Just note the boos when commencement speakers mention AI. But as Google sees it, AI search—if you still want to call it that—is an inevitability that even AI haters will embrace.

I was among those who recoiled at the introduction of AI Overview in 2024. Now I acknowledge that Overview—and the deeper “AI Mode” that it encourages you to use—is simply better for many things, whether finding out if Saturday Night Live has a new episode, getting an explanation of an agentic harness, or even finding a link. When I searched for my WIRED article where I described the meeting in the Ouagadougou, the blue links were less than useful. But when I explained in plain language what I was looking for, I found it immediately.

So it’s working. Google claims that more than a billion people a month are searching with AI Mode, a separate tab on Google’s website where links are even more peripheral. AI Mode queries are doubling every quarter.

Investing your money at the right time and right place is key to fighting rapidly growing inflation. But when we invest our hard-earned money, it is essential to know where the money is going, who is managing it, and how. There are numerous schemes and ways to invest your money. While doing so, we often come across many heavy financial jargons, and one of them is AUM. You must have often heard this in mutual funds. So what is AUM in finance, and why is it so important? 

Keep reading to discover interesting financial facts about AUM. 

What does AUM mean?

AUM refers to Assets Under Management, which is the total market value of investments managed by an investment manager/organization on behalf of their investor, with their consent. These assets can be anything, including stocks, bonds, mutual funds, ETFs, and other investment options. 

The global asset under management (AUM) is expected to reach $200 trillion by 2030, growing at a CAGR of 6.2%. See the detailed market reports here. 

How to calculate AUM? 

It is calculated by adding the total current market value of all investments being managed.

AUM = ⅀ (Current Market Value of all Assets)

The formula, which is used to calculate the daily AUM value:

AUM_today = AUM_yesterday + Net Inflows + Market Profit/Losses

Here, net inflows are your new investments minus the redemptions, and market gains & losses are value changes of assets due to price movements. 

How is your money turned into assets? 

When you invest your money, it is converted into assets that are far more valuable than your paper money. These are the things bought with that money. And the value of these things keeps increasing faster than actual money. It can be anything, such as digital gold, ETFs, mutual funds, shares, stocks, and property. 

Types of AUM Assets

As discussed above, your money can be converted into different types of assets by using it to buy various things. Let me tell you about some of them.

• Equities or Stocks: These are shares, or say you buy a certain percentage of a publicly traded company, including large-cap, mid-cap, and small-cap stocks.
• Debt Funds: These assets are essential components of mutual funds and emphasize fixed-income securities such as government bonds, corporate bonds, treasury bills, and marketable securities. They often have lower risks and a predictive outcome. 
• Hybrid Funds: It is where investment managers diversify your money across assets like equities, debt, and sometimes even instruments like gold. It gives investors a wider portfolio and a good option for people choosing growth with less volatility.
• Thematic & Sectoral Investment: These are specified mutual funds for targeted industries or well-rounded investment themes. These have high growth potential but also come with significant risks due to condensed exposure. The risks are generally due to economic lows or sector-specific downfalls.
• IFs and ETFs: The index funds or exchange-traded funds are passive investments that track the performance of a specific market index. People like their simplicity and cost efficiency. Mostly, retailers and institutional investors prefer these investments. 
• Alternative Investments: These investment approaches are different than traditional ones. Here you can invest in land, real estate, commodities, and gold. This can be used to diversify a portfolio, offer great returns over time, but comes with high risk. 

Each of these investment types has more in-depth subcategories.

Factors that Affect AUM

The AUM value often keeps fluctuating; sometimes you gain, and sometimes you lose. There are different factors behind this, and some of them are listed below.

1. Market Graphs

The market often experiences upswings and downturns, and your underlying assets increase and decrease in value, respectively. Highly volatile assets such as stocks, commodities, crypto currencies etc can be frequently impacted. 

2. Investor Activities

Here, AUM is affected by the investor’s action. Where there are inflows (new investments by investment) like buying new units, increasing capital, etc., the value of AUM increases. On the other hand, if there are outflows or redemptions by investors pulling out their money directly decreases the AUM. 

3. Distribution

When a fund pays out dividends or interest, the AUM reduces, and if these payouts are compensated or reinvested, the value increases. When the funds with better performance outperform, the benchmarks tend to attract people to invest more, and this leads to increased AUM. 

There are also some other factors, like sales and marketing. Different fund types, such as open-ended funds and different fund structures, also impact the AUM. 

How do AUM Managers Earn?

You must be wondering if someone is using the brains and resources to invest your money, then what do they benefit? So here is how asset management companies make money. 

These companies generally sell the investment solutions as products to their clients. They sell mutual funds, ETFs, and manage private accounts of other companies. In return, they either charge a fee or a percentage of assets under management. 

The charges consider some factors such as investment type, asset class, investment sector, and transaction complexity. For instance, when an investment strategy involves a cultured process and tools like trading or taking short positions, then the clients can be charged a high fee. 

Ongoing charge fee (OCF), performance fees, initial and exit charges, etc., are some charges incurred by companies that these agents charge to clients. 

Types of Asset Management Companies

Different types of investment are managed by different specialized companies for the same purpose. 

1. Mutual Fund Companies

These companies use the investor’s money to buy stocks, bonds, and other securities that align with the fund’s objective. These companies are best chosen by retail investors. The clients get fund units, and returns as per market performance. 

2. Hedge Fund Companies

This is most opted for by high-net-worth people and institutional investors, where they use plans like leverage, short selling, and derivatives. The aim is to gain high returns in all sorts of market fluctuations. This involves high risk but has fewer regulatory restrictions. 

3. Private Equity Firms

These are companies that invest directly in unlisted/private companies, or they pool capital from institutional investors and high-net-worth clients to take over, restructure, and improve private companies. Their goal is to increase their company’s worth over a period of time before selling it for a profit. 

4. Real Estate Investment Trusts (REITs)

The firms invest in income-generating assets of real estate, like commercial spaces. The investors earn returns from real estate without actually owning the property. These corporations manage high-value real estate portfolios. Leasing, selling, and collecting rents, and later distributing among the shareholders as their incomes and dividends. 

AUM vs. NAV

Benefits of Asset Under Management

• Shows you trust and scalability, a higher AUM indicates that the fund or company is a trusted one. It reflects credibility, market position, and investor confidence.
• Larger AUM shows that fund managers can help you diversify your investments. 
• When a company has bigger AUM, it can spread fixed costs for clients, which leads to lower expense ratios for investors. 
• Fund houses with larger AUMs have better negotiating power and broader investment opportunities.
• Better AUM indicates the stability of a fund management company. 

Conclusion

I hope this blog helped you understand what is AUM in finance. When you invest, you must know how and where the investment is happening. Learning and understanding about assets under management is the first step to doing so. We have discussed various aspects of it, such as types, risks involved, benefits, and how you can calculate AUM. I have also stated the clear difference between NAV and AUM, which often confuses investors. Keep reading, keep learning. And let me know in the comments, how you choose to invest your money? 

Related: How Blockchain Can Transform the Financial Services Industry

Dun & Bradstreet has spent over 180 years building a comprehensive commercial database. Its Commercial Graph, covering 642 million businesses and their relationships, corporate hierarchies and risk profiles, was designed for people. Credit analysts, risk managers and sales professionals who could wait for query results and work through ambiguous entity matches. AI agents cannot do any of those things.

When D&B’s customers started pushing agents into credit, procurement and supply chain workflows, the Commercial Graph that had reliably served nearly 200,000 customers globally became a problem. The systems built to serve human analysts were the wrong architecture for machines. So D&B rebuilt.

“We need to think about agents as our new consumer category, evolving from our standard credit analysts or sales and marketing professionals, et cetera, to also now catering to these customers’ agents,” Gary Kotovets, Chief Data and Analytics Officer at Dun & Bradstreet, told VentureBeat.

What broke when agents started querying

The Commercial Graph was not a single database. It was a collection of separate systems built for different use cases and different markets, held together by custom integrations. Human analysts navigated that fragmentation through SQL queries or pre-built interfaces. Agents could not.

The scale of the underlying data compounded the problem. The database had nearly doubled in five years, expanding from more than 300 million to more than 642 million business records, with 11,000 fields per record, according to D&B. The firm now runs approximately 100 billion data quality checks per month as records move through its systems. Querying that at the sub-second latency agents require, against a fragmented architecture, was not workable.

The relationships the graph tracked were also the wrong kind. Legacy systems recorded static connections between entities. A CEO was linked to a company. That was the line. Agents working on credit assessments or third-party risk need dynamic relationships: when that CEO leaves for a new company, which organization does their track record follow? When a subsidiary changes ownership, how does that propagate across a corporate hierarchy? Those questions required custom analyst work before. Agents cannot wait for custom analyst work.

The broader problem is not unique to D&B. Kotovets said he has spoken with hundreds of CDOs and CIOs over the past six months and consistently heard the same constraint: they could not build what they wanted in AI because their data foundations were not standardized, normalized or agent-queryable. D&B had that foundation, built over decades to serve human analysts. It still had to rebuild for agents.

What they actually built

The rebuild started with consolidation. D&B migrated its fragmented databases to cloud infrastructure, redesigned the underlying schema and built a data fabric layer that normalizes records across markets while preserving regional compliance requirements. The result is a unified knowledge graph that tracks billions of relationships across 642 million companies, continuously updated and enriched by AI-driven data processing.

On top of that graph, D&B built a structured access layer for agents. Raw SQL access at agent query volumes and latency requirements was not the answer. Instead, D&B created a set of tools and skills available through MCP that package data with context and route agents to the right records for specific queries. A match and entity resolution engine sits behind every query, confirming that when an agent asks about a company, the answer resolves to a verified, specific entity rather than a name match.

D&B solved agent identity from both directions

Rebuilding the graph and adding MCP access solved the data retrieval problem. It did not solve the identity problem. Agents are not humans, and the authentication model built for human users did not extend to machines.

D&B built a new registration model for agents. They must map to a verified IP address and register an individual access key, treated as an authenticated identity in the same pipeline as a human user.

“We actually have a concept of Know Your Agent, similar to know your customer, that does those additional verifications,” Kotovets said.

That handles the inbound problem: knowing which company an agent belongs to and what data it is entitled to query. But D&B also built for the outbound problem: what happens when a customer’s own multi-agent workflow loses track of which company it is analyzing.

In a workflow that chains a credit check agent, a KYC agent and a third-party risk agent, each queries D&B at a different step. Without a mechanism to confirm they are all referencing the same entity, a workflow can complete while operating on divergent records.

“They have to come back to our verification agent to ensure that they’re still talking to each other about the same entity,” Kotovets said. “It’s almost like a digital handshake, in a sense.”

D&B’s business verification agent can be embedded into any workflow as a persistent reference point and is available on Google’s A2A protocol regardless of which orchestration tool a customer uses.

Four things enterprises must get right before deploying AI agents

The rebuild exposed requirements that go beyond D&B’s own stack.

1. Data foundations come before agent infrastructure. The CDOs and CIOs Kotovets spoke with over the past six months consistently hit the same wall: they cannot build what they want in AI until their data is clean, normalized and consolidated. D&B had that foundation already. Most enterprises do not, and they will feel it.

2. Design for dynamic relationships, not static ones. Enterprise data systems typically record point-in-time connections: a person belongs to a company, an asset belongs to a subsidiary. Agents working on credit, risk or supply chain decisions need to reason across relationships that shift over time. If the underlying data only captures the static line, the agent will too.

3. Build entity consistency checks into multi-agent workflows. When multiple agents touch the same entity at different steps, there is no guarantee they are all referencing the same record by the time the workflow completes. That gap needs to be engineered for explicitly. Entity verification is a workflow design requirement, not an optional guardrail.

4. Embed lineage from the start, not as an afterthought. Every agent-produced answer should carry a traceable path back to its source. In credit, risk and supply chain decisions, the cost of an error is concrete. Lineage needs to be built in before scaling, not added after problems surface.

“You could always click and see where it came from, and validate it all the way back to the original source,” Kotovets said. “That’s been the key for us in unlocking a lot of other capabilities, because we have that level of certainty in the things that we’ve done.”

The London-based beauty and wellness booking platform has joined the UK unicorn club at a $1bn-plus valuation, in a deal that lands while the broader SaaS complex is busy arguing about its own funeral.

Fresha, the London-based booking and payments platform for salons and spas, has raised $80m from funds managed by KKR in a deal that values the company at more than $1bn, the company said on Thursday.

The round, structured as primary growth capital, takes Fresha to unicorn status and lifts the total raised since 2015 to $285m.

The cheque comes from KKR’s Next Generation Technology Growth fund, the firm’s growth-equity arm, which writes into companies that are already past the product-market-fit stage and are looking for scale capital rather than runway.

The numbers Fresha disclosed alongside the announcement explain the appetite. The platform is used by more than 130,000 beauty and wellness businesses across the UK, Australasia, the Gulf, North America and parts of South-East Asia, and processes more than 35 million appointments a month, or roughly 420 million a year, against $15bn in annual gross merchandise value.

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Annual revenue run-rate stands at more than $140m, growing at over 60% a year, and the business is profitable. The last time Fresha disclosed a valuation, in a Series C extension in late 2021, the figure was $640m.

Founded in 2015 by William Zeqiri and Nick Miller, Fresha has spent the past five years quietly displacing the older booking incumbents in its core markets and pushing into payments, capital, and, more recently, AI-driven scheduling and marketing tools.

Zeqiri, in a statement, called reaching unicorn status “a proud milestone” and said the round would fund further global expansion and AI investment.

Miller, the company’s chief product officer, framed the round as validation from customers, who he said were already using the platform as their primary operating layer.

KKR’s diligence ran for more than a year and included surveys of over 1,000 beauty and wellness businesses across the US, UK, Ireland, the EU and Australia, plus interviews with customers, former employees and competitors.

The research, according to KKR, ranked Fresha first across software quality, ease of use, support, set-up and marketplace strength, with an average score of 8.1 out of 10 against a competitor average of 6.7.

Patrick Devine, a partner on KKR’s Tech Growth team, said Fresha had built “a differentiated platform combining software, financial services and marketplace capabilities with embedded AI.”

Marta Szczerba, a director on the same team, said she had followed the founders for years and had been “highly impressed with the consistent performance.”

The deal lands at an awkward moment for the SaaS category Fresha sits inside. Salesforce is down roughly 30% year-to-date and the broader software complex has spent 2026 absorbing the argument that per-seat pricing is the wrong shape for the AI era.

A vertical platform earning revenue from payments and marketplace fees as well as subscriptions is, on the face of it, the kind of business that argument is least worried about, which is presumably part of what KKR’s diligence team concluded.

Fresha said the new capital will go toward expansion in the US, continental Europe, Africa and South-East Asia, and toward AI features across booking automation, marketing, accounting and workforce management. The company did not disclose a planned timeline to an IPO or any further fundraising.

Cyber-Crime

MFA? No problem, says crimeware that tricks users into handing attackers the keys to M365

The FBI has issued a public service announcement warning about a new phishing kit that’s stealing Microsoft OAuth tokens at an alarming rate.

OAuth token theft is a serious headache for organizations because stolen tokens can bypass multi-factor authentication (MFA) and grant access to privileged accounts within an organization without needing to know their credentials. 

Think corporate espionage, data theft, maybe even ransomware.

The main culprit is Kali365, described as a phishing-as-a-service platform that’s being peddled on Telegram, first spotted by crimefighters in April 2026.

“Kali365 lowers the barrier of entry, providing less-technical attackers access to AI-generated phishing lures, automated campaign templates, real-time targeted individual/entity tracking dashboards, and OAuth token capture capabilities,” the FBI said in its announcement.

Phishing kits aren’t new. Different flavors are always in development, but the good ones can be especially problematic for organizations.

Kali365 lets attackers send convincing phishing emails that impersonate “trusted cloud productivity and document-sharing services,” – Adobe Acrobat Sign, DocuSign, and SharePoint – according to security shop Arctic Wolf.

That email contains a device code and instructions for the target to enter the code into a legitimate Microsoft page, a hyperlink for which is included in the email.

Entering that code registers the attacker’s device to the unwitting target’s M365 account, effectively surrendering access to emails, Teams, and all the rest of it. No MFA required.

Arctic Wolf published a deep dive on Kali365 back in April, noting that it also offers adversary-in-the-middle (AitM) capabilities that are distinct from the device code phishing described by the FBI.

The second attack Kali365 enables leads to the same outcome, accessing Microsoft accounts while bypassing MFA, just through slightly different mechanics.

Victims are sent an initial phishing email containing a cookie-based lure, which transparently proxies their browser via attacker-controlled infrastructure, Arctic Wolf said. Requests are then forwarded to a real Microsoft login page, and responses are beamed back to the victim, who authenticates the typical way using their valid credentials, passing Microsoft MFA.

Session cookies, related artifacts, and other session information are scooped up during this process and stored in the Kali365 attacker panel. From there, attackers can generate scripts to replay those sessions in their own environment, effectively borrowing the genuine user’s session.

The researchers’ analysis of Kali365 revealed three distinct tiers for subscribers.

The lowest Client Tier is for individual attackers, who can change the branding on the panels to give each a bespoke look while sporting the same underlying powers. The Agent Tier is for resellers who can provision and manage their own branded Kali365 panels and Client Tiers. The Admin Tier is reserved for Kali365’s developers.

Kali365 has a simple pricing structure: $250 per month per tenant, or $2,000 for a year. It supports an array of languages: Arabic, Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, and Turkish.

Since emerging in April, Kali365 has often been mentioned in the same breath as EvilTokens, another device code phishing platform that hit headlines weeks earlier after Microsoft confirmed hundreds of compromises each day.

“Each campaign is distributed at scale, targeting hundreds of organizations with highly varied and unique payloads, making pattern-based detection more challenging,” Tanmay Ganacharya, VP of security research at Microsoft, told The Register.

“We continue to observe high-volume activity, with hundreds of compromises occurring daily across affected environments.”

Both Arctic Wolf and the FBI suggested organizations at risk should use conditional access policies to block device code flow where not required.

Defenders should also consider blocking authentication transfer policies, which let users move authentication between devices such as PCs and phones.  ®

Samsung could finally be preparing the compact flagship Galaxy fans have been asking for.

A new leak suggests the company is working on a Galaxy S27 Pro model that would bring some of the Ultra’s premium features into a noticeably smaller phone.

According to reports from Korea, Samsung is developing a new 6.47-inch Galaxy S27 variant that would sit between the larger Galaxy S27 Plus and Ultra models alongside the standard Galaxy S27. If accurate, it would mark the first time Samsung has used this specific screen size in its flagship lineup.

More importantly, though, the Galaxy S27 Pro reportedly won’t just be another mid-tier option with a fancier name. The leak claims it could share “most” of the Galaxy S27 Ultra’s specs. However, it may drop features like the S Pen to keep the device smaller and more manageable.

That would make it a pretty interesting shift for Samsung. Right now, if you want the company’s best cameras, battery tech, and performance upgrades, you’re usually stuck buying the biggest phone in the lineup. The rumoured S27 Pro sounds closer to Google’s recent Pixel strategy. The Pixel 10 Pro offers nearly all the same flagship features as the larger Pro XL. However, it is in a more pocket-friendly size.

The reported 6.47-inch display would still make the S27 Pro larger than the regular Galaxy S26, which sits at 6.3 inches. However, it would be noticeably smaller than the Plus and Ultra models. That could hit a sweet spot for users who want flagship specs without carrying around something that feels tablet-adjacent.

Of course, there will probably be some compromises. A smaller phone usually means less internal space. Unless Samsung adopts newer silicon-carbon battery technology by then, battery capacity could end up being one of the trade-offs compared to the Ultra.

Still, this leak feels more believable than some of the earlier “Pro” rumours surrounding the Galaxy S27 series, largely because the market has shifted. Compact premium phones are quietly making a comeback, and Samsung doesn’t really have a true answer to devices like the Pixel 10 Pro yet.

There’s no official confirmation from Samsung for now. However, if the Galaxy S27 Pro does happen, it could end up being the most interesting model in the lineup — not the Ultra.