Pressure is mounting on the European Commission to act on spyware, after forensic evidence showed one of the EU’s own spyware investigators was hacked with Pegasus. Civil society groups issued a joint statement demanding the abuse be met with accountability, “not impunity”.

Citizen Lab confirmed last week that Stelios Kouloglou, a Greek former MEP, was infected in October 2022 and again in March 2023. The infections hit while he served on the PEGA committee, the European Parliament’s inquiry into exactly this kind of abuse.

The attacker remains unidentified, and Citizen Lab says it has no indication the Greek government was responsible. The same Pegasus-linked email address appeared in an earlier campaign against journalists across Europe, suggesting a customer authorised to deploy the NSO Group tool in multiple countries.

Whoever was behind the hack could have accessed confidential committee documents and deliberations. Lawmakers have described the incident as an attack on the rule of law, and the Parliament’s left grouping is demanding strict EU-wide limits on spyware use.

The Commission did not respond to requests for comment from TechCrunch. It has yet to publicly account for its implementation of the PEGA committee’s 2023 recommendations, the gap campaigners now want closed with a public roadmap.

A rap sheet, not an isolated case

The statement’s signatories list a pattern of European scandals: spyware used against exiled journalists in Latvia, Lithuania, and Poland, the targeting of the Parliament’s president with Predator, and Graphite infections in Italy, where spyware-laced fake WhatsApp apps have also surfaced. EU public money has meanwhile flowed to the surveillance industry itself, according to EUobserver.

Enforcement of the bloc’s dual-use export rules remains patchy, as leaked Bulgarian export licences to governments accused of repression showed. On paper the EU regulates spyware sellers, and in practice it sometimes funds them.

NSO Group has meanwhile explored selling Pegasus altogether, a prospect that raises its own accountability questions. The tool’s customers, whoever they are, keep finding European targets.

The PEGA committee spent two years documenting Europe’s spyware problem, and one of its own members was bugged while doing it. If that does not trigger the urgent response campaigners want, it is hard to imagine what would.

Microsoft’s Xbox division has kick-started a big reset today, a move it has been hinting at for weeks. The company has announced layoffs covering approximately 3,200 roles throughout 2027, of which nearly half of the roles are being terminated starting today. Additionally, the gaming arm is letting go of four studios, including Ninja Theory, which developed the smash hit Senua series of games. Notably, the company assures that none of the first-party games that have already been announced will be affected or cancelled.

What’s happening?

Xbox is entering a year-long restructuring phase, something that has been making the rounds of the rumor mill for a while now. The company argues that its operating margin is 3-10x lower than rival platforms (read: PlayStation and Nintendo). And to make matters worse, the install base was lower, and the cost of its ninth-generation platform was higher than ever. The company also notes that Xbox Game Pass and its multi-platform game strategy didn’t yield the kind of results they had hoped for.

Our business today is not healthy.

“I know this is painful. These changes will directly affect people who have poured their creativity into building XBOX. Many joined us through acquisitions, while others were recruited here, or sought us out because they loved this industry and loved XBOX. Today’s decisions do not reflect their talent or dedication,” Xbox chief Asha Sharma wrote in an official blog post. This is the second major lay-off following restructuring that happened back in 2024.

What about the studios?

The biggest shift that comes as part of the reset is the studio culling. Compulsion Games (South of Midnight, We Happy Few, and Contrast) and Double Fine Productions (Psychonauts 2, Kiln, Keeper, and Broken Age) are going independent, which means they are officially moving out of the Xbox Games Studio banner. These studios will also be moving out with their IP, catalog, and runway intact.

More importantly, Ninja Theory and Undead Labs are also finding new owners. Ninja Theory developed some of the most recognizable Xbox games of the past few years, including Senua, Senua’s Saga : Hellblade II, Hellblade: Senua’s Sacrifice, and Bleeding Edge, to name a few. Undead Labs, which developed the State of Decay series, has also been shown the exit door.

Apple has not announced its first foldable iPhone yet, but Caviar is already trying to sell a luxury version of it. The custom phone brand has revealed its “Flagship” collection for the rumored iPhone Ultra, giving Apple’s expected foldable a gold, silver, leather, and carbon fiber makeover months before the real device is likely to appear.

Caviar has made plenty of wildly expensive Apple accessories and custom phones before. We recently saw the company put a Tyrannosaurus fossil fragment into a $4,490 magnetic case for the iPhone 17 Pro Max. Its foldable iPhone Ultra collection is playing in the same absurdly expensive territory, only this time the luxury treatment is arriving before Apple’s own version.

A luxury iPhone before the real one

The collection includes four versions of the foldable iPhone Ultra. The Dark Cherry model uses purple crocodile leather and decorative elements plated in 24K gold. Caviar says the color is inspired by the Dark Cherry shade expected on the iPhone 18 Pro. The Titan model goes fully black, while the Silver version uses a silver upper panel, crocodile leather, and a three-dimensional Apple logo made from sterling silver.

The most lavish option is the Gold model, which uses carbon fiber and a three-dimensional Apple logo made entirely of 18K gold. Caviar says this version is dedicated to Apple’s 50th anniversary.

Some of the technical details on Caviar’s page appear to be based on rumors rather than official Apple information. The page mentions a titanium body roughly 4.5mm thick, an A20 Pro chip, 12GB of RAM, a 24MP under-display selfie camera, and two 48MP rear cameras. That camera detail is worth treating carefully, since other rumors have pointed to a punch-hole camera instead.

The expensive iPhone gets even more expensive

The regular foldable iPhone Ultra is already expected to be Apple’s most expensive iPhone yet. Analyst Ming-Chi Kuo has pointed to a price of around $2,300 to $2,500, and early supply could be extremely limited.

Caviar is pushing the price far beyond that. The Flagship collection will be limited to 19 units, and delivery is expected only after Apple launches the real iPhone Ultra. The brand has listed preorder pricing from $13,840, while the top Gold model with 1TB storage is priced at $16,270.

Every company starts with a few simple tools. You pick one app for messaging, another for projects, and a third for file storage. At first, this setup works well. Over time, however, these pieces create a fragmented picture that makes everyday work difficult. This is the hidden cost of scaling. As a business grows, departments adopt specialized software, and every new hire must navigate a dozen different platforms. Managing this sprawling tech stack eventually turns into a full-time job. More importantly, many of these tools charge per user, so every new employee increases costs, while flat-rate platforms such as Bitrix24 keep pricing predictable as you grow. At the same time, leaders are realizing that having fewer, more capable tools is just as important as the way those tools are priced. 

The Financial Drain of Per-Seat Billing

Growing a company often brings a persistent and frustrating tax on your SaaS and software budget. As the organization expands, monthly overhead swells quickly, and each additional seat adds another line to your bill. When these fees stack up across several different apps, expenses soon feel out of control. Bitrix24 directly solves this “scalability penalty” by offering fixed-price commercial plans such as Basic, Standard, and Professional that accommodate a set or even an unlimited number of users. Instead of watching costs hike every time your team grows, you can keep your software budget flat and predictable. 

Creating an All-in-One Digital Workspace

Instead of juggling multiple apps simultaneously, Bitrix24 places your entire operation in one environment. When your project management, CRM, team chat, contact center pipelines, and file storage all live in a single system, you can finally end the nonstop, tiring cycle of switching between tabs. Consolidation does more than reduce your monthly software bill. It easily removes the hassle of digital clutter that slows your team down. With one simple and unified interface, your team no longer wastes time trying to manage a complex software setup. People can focus on the work that actually helps the business grow. Because everything shares the same interface and design language, employees only need to learn one system in place of many. This shortens training time and helps teams adopt the software much faster. 

Unified Data Tracking for a Consistent Experience

Bringing everything together also keeps your data moving smoothly. When your marketing, sales, and management tools do not communicate with one another, crucial information becomes trapped in silos. This often leads to manual errors and missed opportunities. Bitrix24 prevents this by creating a connected path in which a form fill from your website becomes a live CRM lead, then a contact, then a project task, and finally an automated invoice. You do not need to move the data by hand. Such consistency makes new workflows easier to learn and reduces the frustration that comes from jumping between disconnected platforms.

Simplifying IT Security and Compliance

Managing numerous separate subscriptions creates a heavy burden for IT teams. Every platform becomes a potential vulnerability that needs to be monitored, patched, and regularly audited. A single consolidated platform significantly reduces this risk. IT departments spend far less time managing user permissions, offboarding employees, and running security audits when all data lives in one secure environment. This change not only protects company assets but also allows IT staff to focus on strategic initiatives rather than routine maintenance.

Why Bitrix24 Is Worth Considering

The era of chasing one more app for every problem is coming to an end. Modern businesses now prioritize integration over fragmentation. By shifting to a unified and flat-rate platform, organizations can escape the cycle of rising costs and declining efficiency. Scaling should feel like progress and not like a growing burden of subscriptions. When the tech stack is simple, businesses gain the agility they need to compete in a crowded market while keeping their attention on growth. As leaders search for ways to elevate their operations, the choice becomes clear. They can reduce the noise, streamline their tools, and invest in a system that grows with the business without adding unnecessary overhead.

The details are beyond fuzzy, so don’t hold your breath for this one.

• Electricity demand is now growing faster than energy suppliers can keep up with
• Volatile AI workloads cause unpredictable peaks and troughs in demand
• AI could actually help predict, despite also being the cause

With three in four (77%) electricity execs now believing that data center energy demand will grow faster than utilities can keep up with, two-thirds (68%) expect electricity shortages to become more commonplace as demand for AI soars.

New data from a Capgemini report reveals just how unpredictable AI energy demands can be, with 77% admitting they struggle to accurately forecast demand amid volatile AI workloads.

During this year’s World Cup, one scene repeats itself game after game: Several players take the field with holes in the calves of their socks. Social media is rife with theories about the supposed competitive advantage this might give them. But the practice isn’t new. It has been seen at the European Championships, the Olympic Games, and other international competitions over the past decade. Still, science has yet to find evidence that it improves performance.

Professional soccer socks are, by design, form-fitting. In addition to holding shin guards in place, they provide support to the ankle, the arch of the foot, and the calf; they help manage moisture and reduce foot movement inside the cleat to improve stability. This design principle has been used in professional soccer for decades. Although materials have evolved to become lighter and more durable, they are still primarily based on synthetic fibers such as polyester, nylon, and spandex.

But quite a few players have complained that the socks are too tight and cause a tingling and numb sensation in the calf area. The discomfort is so great that, halfway through a game, they cut several holes in the calf area to “release tension” and run better.

There is a biomechanical component to this sensation. During a sprint or a change of direction, the largest muscle in the calf contracts and increases in thickness to generate the force that propels the athlete forward. This change in shape occurs thousands of times during a game. For some, the repeated expansion of the muscle is enough to create a sensation of pressure when the sock exerts constant compression on the calf.

Over time, the practice of cutting holes in socks has taken on an almost intuitive explanation among the players themselves: splitting open the fabric allows the muscle to “breathe,” relieving pressure and reducing the likelihood of pain or cramps. However, specialists in sports medicine and recovery point out that there are no studies demonstrating that cutting holes in socks provides any benefit. In fact, much of the research on compression garments concludes that, when properly designed and fitted, they can help limit muscle inflammation after intense exertion.

Despite the lack of evidence regarding physiological benefits, the practice continues to spread among professional soccer players. Today, it is considered primarily an anecdotal phenomenon, based on each player’s personal experience rather than scientific evidence. Furthermore, the rules of the game do not prohibit modifying socks, as long as the equipment remains safe and the shin guards remain properly covered. (A soccer player, however, cannot play with a torn jersey.)

Given the lack of scientific evidence, several specialists believe that part of the phenomenon could be explained by the player’s own perception of comfort. In high-performance sports, the feeling of comfort can influence the confidence with which an athlete competes. If a soccer player believes a piece of clothing is restrictive, eliminating that perceived discomfort can make them feel freer to run, accelerate, or change direction—even if their performance remains objectively unchanged.

Though there is no evidence that cutting the socks provides a competitive advantage or reduces the risk of injury, that does not mean the sensation of discomfort is imaginary. The perception of pressure, restriction, or comfort depends on multiple factors, ranging from anatomy and individual sensitivity to the athlete’s past experiences. In other words, two players may react differently while wearing exactly the same equipment.

For now, it seems the cutting of socks will continue. The available evidence points to a mechanism similar to that of other sports rituals: Its effect is primarily psychological, not necessarily physiological.

There’s an important distinction between AI that just works today, and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they’re building the second.

Velocity without discipline and strategic direction is a liability, not an asset. The hardest part of building AI at scale isn’t getting a model to work once. It’s building systems that continue to work, scale beyond individual teams and use cases, and improve consistently over time.

Today’s AI systems do more than just predict and optimize. They converse, reason, and increasingly take action. An autonomous system making decisions on a traveler’s behalf creates a very different set of expectations around reliability, governance, and accountability. As AI takes on more of those roles, the principles behind how these systems operate matter more than ever.

We have spent years applying AI and machine learning (ML) across the traveler journey — from personalization, ranking, and recommendations, to fraud prevention, customer support, and, more recently, generative and agentic AI experiences. That depth of experience is what led us to develop a set of ML and AI principles to guide how we build, deploy, and evolve AI systems across our company.

The goal is simple: Make sure the systems we build create real business value, scale, and operate safely. These principles define how we measure, design, govern, and operate our systems.

From principles to practice

Publishing principles is the easy part. The harder and more important work is turning them into operating mechanisms: Recommendations, requirements, tooling, and release processes that teams actually use.

We have begun using ‘Agentic Release’ tollgates: A set of recommended and, in some cases, required checks before launching agentic AI features. These tollgates translate principles like clear ownership, risk-based governance, evaluation, safe rollout, and monitoring into concrete expectations for teams.

Some of these recommendations and requirements are already being automated and integrated into the software development lifecycle (SDLC). Over time, the goal is for these expectations to become embedded in how we design, evaluate, approve, launch, and monitor AI systems from the start.

Outcomes: Measuring what actually matters

The first test for any model is whether it improves a business outcome and, ultimately, the traveler experience — not whether it just improves a technical metric.

1. Align models to metrics with business impact: Every ML effort must tie directly to a key business outcome or traveler experience metric. Technical optimizations are useful midpoints, not end goals.

2. Optimize for return on cost: The value a model creates has to justify what it costs to develop, train, and monitor, plus the operational complexity it adds. Favor solutions that deliver lasting impact relative to what they cost to run.

3. Justify complexity against strong baselines: Complexity should be earned, not assumed. Start with a strong baseline: An existing general model, a simple heuristic, an off-the-shelf solution. Reach for specialized models or more complex architectures only when simpler options genuinely can’t meet the bar.

4. Require both offline and online evaluation: No model goes to broad deployment on offline validation alone or jumps straight to A/B testing. Every model must perform in both offline and online evaluations. Over time, our offline evaluations should reliably predict what we see online.

Design: building systems that scale beyond the teams that build them

Getting a model to work is one challenge. Making its value extend beyond a single team or use case is the harder one.

1. Build on shared foundations; specialize only when justified: Favor shared, platform-wide foundations for core capabilities, data representations, and model building blocks. Specialization should build on those foundations, not spin up isolated stacks, so when the foundation improves, the gains flow across the organization.

2. Treat data as a first-class product: A model’s quality is bounded by the quality of its data. We need to maintain robust pipelines, clear lineage, reproducibility, and reusable features built with documented ownership, clear schemas, and SLAs that other teams can rely on.

3. Prioritize generality over local optimization: When two approaches perform similarly, favor the one whose learnings, assets, and operating patterns can be reused across teams, brands, and use cases. We should optimize not just for local performance, but for how quickly improvements can diffuse across the company and compound over time. 

4. Minimize and sunset manual business rules: Manual rules are sometimes necessary for policy, safety, or compliance, but they should be explicit and reviewed regularly, never silent patches for weak models or a source of permanent maintenance debt.

5. Reproducibility and traceability by default: Training data, features, configurations, evaluation results, deployment versions, and key decisions should all be documented and recoverable. That’s what lets you debug a production issue months later and hand off ownership without losing institutional knowledge.

Trust: ownership, governance, and operating responsibly at scale

The bar for deploying AI isn’t just “does it work?” It’s “can we stand behind it?” Trust isn’t something you add at the end; it’s earned over time and maintained across the full lifecycle of every model we ship.

1. Assign clear ownership and accountability: Every model needs defined ownership across its lifecycle — a business owner, a product owner, an AI owner, and an operational owner. These don’t need to be four people, but the responsibilities must be explicit. Who’s accountable for outcomes? Who responds if the model drifts? Who answers the incident at 2 a.m.? Without this in place, models become orphaned and problems surface with no one to own them.

2. Adhere to standards and governance: AI and ML models must use approved platforms and comply with established company standards, release gates, and governance processes. Operating outside these guardrails requires a clear, defined path to remediation or deprecation, rather than an open-ended exception. 

3. Govern proportionally to risk: The level of review, evaluation rigor, and human oversight should scale with a model’s impact. A customer-facing model that affects pricing or availability for millions of travelers demands a far higher bar than an internal tool used by a small team. For high-impact, safety-sensitive, or highly autonomous systems, human-in-the-loop checkpoints are built in from the start. 

4. Design for fairness, privacy, and transparency: We actively test for unintended bias, have strong data guardrails, and favor explainability when decisions meaningfully affect users. These are incorporated from the start, not added on.

5. Design for safe rollout, rollback, and control: Deployments are progressive, with rollback paths, fallback mechanisms, and circuit breakers ready before launch. The ability to safely undo a deployment matters as much as the ability to ship it.

6. Monitor continuously and adapt: Once live, teams must actively monitor quality, drift, latency, cost, and business performance and retrain or recalibrate when the data shifts. A team should always be able to explain how its model is performing now, not just how it performed when it launched.

These principles do more than define how we build. They define what we’re willing to ship and how we stand behind it. In a world where AI systems are increasingly consequential and make real decisions for real travelers and partners, these standards matter. Applied consistently, they build responsible AI that lasts.

Xavi Amatriain is Chief AI and Data Officer at Expedia Group

Xavier will share more details about Expedia’s architecture during his session at VB Transform on July 14 at 11:10 am PT. He will discuss: “Expedia’s blueprint for building autonomous agents for high-stakes transactional systems.”

Interested in attending VB Transform 2026? Register here. A select number of complimentary passes are also available to senior technology leaders. Contact us to get yours.

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