Top Tech News Today, January 29, 2026

It’s Thursday, January 29, 2026, and today’s tech cycle is being shaped by a single force: AI moving from feature to infrastructure. Google is pushing Gemini directly into Chrome as a hands-on browsing agent, while OpenAI and Meta’s latest funding and capex signals show how quickly compute, power, and data centers are becoming the real battleground. At the same time, Amazon and Nvidia are fighting over the next layer of advantage: silicon control, with custom chips and geopolitical approvals now influencing who can scale AI fastest.

Security is the other headline theme. From Fortinet perimeter exposure and OpenSSL’s broad patch-blast radius to retail breach pressure and Olympic cyber planning, the day’s stories underscore a hard truth: the more digital and automated the world becomes, the more attackers target the seams. Layer on rising investor scrutiny over AI spend, the coming depreciation wave, and workforce restructuring at Amazon, and you get a clearer picture of where the industry is headed: bigger bets, tighter discipline, and higher operational risk across the stack.

Here are 15 handpicked technology news stories with global impact, spanning AI agents, Big Tech strategy, cybersecurity, policy dynamics, and the next wave of startup funding.

Technology News Today

Google Adds Gemini AI-powered ‘Auto Browse’ to Chrome, Turning Gemini Into a Hands-On AI Agent

Google is rolling out a Gemini-powered “auto browse” capability in Chrome that can perform multi-step tasks — like researching options, filling out forms, and coordinating actions across services — shifting the browser from a passive tool to an active agent layer. The move is aimed at keeping Chrome central as “agentic” browsing becomes a competitive battlefield, especially as AI systems increasingly mediate how users discover products, plan travel, and complete online workflows.

The strategic implication is bigger than convenience. Once AI handles browsing, the leverage moves to whoever controls the agent interface and its default pathways. That raises fresh questions about user consent, transparency, and the economic impact on publishers and e-commerce sites when AI can summarize, compare, and transact without conventional click-through behavior. Google is also positioning Gemini more deeply within its ecosystem, which could tighten integration for users while amplifying concerns about platform power for regulators already monitoring search and browser dominance.

Why It Matters: If AI becomes the primary browsing interface, the browser turns into a distribution gatekeeper for the next generation of internet commerce.

Source: The Verge.

Big Tech Weighs a Massive OpenAI Funding Round as AI Infrastructure Costs Surge

Nvidia, Microsoft, and Amazon are reportedly in talks around a potential investment package that could total as much as $60 billion into OpenAI, underscoring how capital-intensive frontier AI has become. The reported talks reflect a broader reality: model development and deployment now require not just talent and data, but sustained access to GPUs, networking, energy, and data center capacity.

If such a deal lands, it would also highlight the deepening interdependence between foundation model builders and platform companies that sell (or subsidize) the compute. That interdependence is strategic, but it is also a concentration risk: large investors can become essential partners in infrastructure, distribution, and go-to-market. For the broader ecosystem, another mega-round would pressure startups to differentiate through proprietary data, deeper workflows, or industry specialization—because competing head-on on model scale is becoming unrealistic.

Why It Matters: The AI race is shifting from “who has the best model” to “who can finance and supply the compute.”

Source: TechStartups via The Information.

Meta Plans Up to $135B in 2026 Capex as AI Data Center Arms Race Escalates

Meta reported strong quarterly results and said it expects capital expenditures to jump sharply in 2026, driven by AI infrastructure buildout. The company is signaling that its next phase of growth depends on more compute: training and serving larger models, expanding recommendation systems, and supporting AI features across Facebook, Instagram, WhatsApp, and Threads.

The scale matters because Meta’s plan is not happening in isolation. Across Big Tech, AI capex is increasingly dictating strategy, timelines, and investor narratives. Meta’s results suggest that advertising cash flow can fund the buildout—at least for now—but the long-term test is whether AI spending translates into durable product advantages, higher ad yield, or new revenue streams. For startups, the takeaway is clear: hyperscalers are building capacity and will continue to push AI capabilities into core products, raising the bar for differentiation and distribution.

Why It Matters: Meta’s spending plan is a loud signal that AI infrastructure is now the main battlefield for platform dominance.

Source: Associated Press.

Amazon’s Custom AI Chip Push Adds New Pressure on Nvidia’s Data Center Stronghold

Amazon Web Services continues to expand its strategy of using in-house silicon to reduce cost and improve control over AI performance and supply. The goal is not just faster training, but predictable capacity and pricing as demand spikes and GPU availability remains a bottleneck for many customers.

This matters because custom accelerators can reshape cloud economics. If AWS can deliver compelling performance per dollar, it gives enterprise buyers another path beyond Nvidia-heavy stacks—especially for workloads optimized for AWS’s ecosystem. Over time, it also strengthens AWS’s ability to bundle compute, storage, and AI services into higher-margin offerings, while reducing exposure to third-party chip supply cycles. For startups building AI products, the rise of custom chips broadens deployment options but also increases complexity: performance tuning becomes cloud-specific, and portability may come at a real cost.

Why It Matters: Cloud-driven silicon competition is becoming a core lever in the AI infrastructure economy.

Source: The Wall Street Journal.

ASML Made Record $11.5 Billion Profit in 2025, But Plans 1,700 Job Cuts

ASML reportedly made a record $11.5 billion profit fueled by AI-driven demand for advanced chipmaking equipment, even as it announced plans to cut roughly 1,700 roles. The juxtaposition is striking: AI capex is booming, yet major suppliers are still reorganizing to remain efficient and maintain high engineering throughput.

ASML sits at a choke point in the semiconductor supply chain, and its order flow is often read as a proxy for the durability of AI infrastructure spending. The company’s outlook suggests customers still expect sustained demand, especially for leading-edge tooling. At the same time, export controls and geopolitics remain a persistent constraint on where the most advanced manufacturing capacity can scale. For the broader ecosystem, the ASML story reinforces that “AI growth” is not a straight line; it is accompanied by cost discipline, operational reshaping, and political friction.

Why It Matters: AI demand is strong enough to drive record orders, but the chip supply chain is still tightening operations and navigating restrictions.

Source: Associated Press.

China Greenlights Nvidia H200 Purchases for Tech Giants in a Strategic AI Shift

China has reportedly approved purchases of Nvidia’s H200 AI chips by major tech companies, signaling a pragmatic effort to meet near-term AI needs while still pushing domestic alternatives. The move highlights the tension between national self-reliance goals and the immediate performance demands of frontier AI training and inference.

For global markets, this is another reminder that AI supply chains are increasingly governed by policy decisions, not just customer demand. It also raises stakes for companies building around U.S.-designed accelerators: approvals can be selective and subject to shifting constraints. For Chinese firms, access to top-tier chips can accelerate model development and data center planning, while also creating urgency to reduce long-term dependency. For U.S. and allied companies, the development adds complexity to compliance, forecasting, and competitive dynamics—especially as “who gets which chips” becomes a strategic variable.

Why It Matters: AI compute access is becoming a geopolitical instrument, reshaping who can scale frontier capability—and how fast.

Source: Reuters.

Fortinet “SSO Auth Bypass” Flaw Exploited in the Wild, Expanding Firewall Risk Surface

Fortinet disclosed and patched an authentication-bypass vulnerability affecting FortiOS and related products that has been exploited in the wild. The core concern is straightforward: devices that rely on FortiCloud SSO could be exposed to unauthorized access points that attackers can exploit for privileged access.

For enterprises, firewall and security appliance vulnerabilities are high-impact because they sit at the perimeter and often anchor segmentation, VPN access, and policy enforcement. A compromise can expose configuration data, enable account creation, or serve as a stepping stone for lateral movement. The practical takeaway is to prioritize urgent patching and validation of identity integrations (SSO pathways, device registrations, and admin access rules), and to monitor for abnormal authentication patterns. This is also a broader governance issue: as security stacks become more integrated with cloud identity systems, “alternate paths” in auth can become a systemic weakness if not rigorously audited.

Why It Matters: Security infrastructure is only as strong as its authentication edges—and attackers keep targeting the seams.

Source: Fortinet PSIRT / NIST NVD.

OpenSSL Ships Security Updates Fixing 12 Flaws, Including a High-Severity RCE Path

OpenSSL released updates addressing a dozen vulnerabilities, including issues that can cause crashes and, under certain conditions, remote code execution. Because OpenSSL is deeply embedded across servers, appliances, and software supply chains, even “niche” parsing vulnerabilities can matter if untrusted inputs reach affected components.

The bigger story is operational: security teams now face recurring “high leverage” patch cycles where the vulnerable component is ubiquitous, dependencies are hard to inventory, and remediation requires coordination across application owners and vendors. This update is also a signal about modern vulnerability discovery: researchers are increasingly using automation to uncover bugs in mature codebases that have been reviewed for decades. For defenders, that means the vulnerability pipeline may get faster—raising the value of SBOMs, asset discovery, and disciplined patch SLAs.

Why It Matters: When a core crypto library updates, the blast radius can span the entire internet-facing software stack.

Source: SecurityWeek.

Italy Preps an Olympic Cyber Command as AI-Driven Threats Target Ticketing, Streaming, and Infrastructure

Italy is ramping up cybersecurity planning for the Milano Cortina Winter Olympics, with officials warning that AI can amplify phishing, automation, and disruption attempts aimed at highly visible systems. The likely targets are predictable: ticketing, event websites, streaming availability, and the digital services that keep transportation and venues running smoothly.

Major global events are stress tests for cyber resilience because they combine attention, time pressure, and complex vendor ecosystems. Even brief outages can cause reputational damage and operational chaos when millions of users simultaneously attempt to access services. The Olympics also attract partners, sponsors, and contractors—expanding the supply chain footprint that attackers can probe. For enterprises outside sports, the lesson is portable: every large, time-bound digital event behaves like a “mini critical infrastructure” scenario, and AI-assisted attackers reduce the cost of broad, persistent targeting.

Why It Matters: AI lowers the barrier to disruptive attacks, making high-profile, time-sensitive systems a magnet for cyber campaigns.

Source: The Independent.

Panera Bread Added to ShinyHunters Leak Claims, Renewing Retail Data Breach Pressure

TechRepublic reports that ShinyHunters has claimed access to customer-linked data tied to Panera Bread, adding another major brand to the growing list of organizations pulled into extortion-style leak ecosystems. While breach claims require careful verification, the pattern is consistent: attackers increasingly monetize brand pressure, reputational risk, and fear of regulatory exposure — sometimes even before full technical attribution is publicly confirmed.

For retailers and consumer platforms, the risk surface is widening. Large customer databases, loyalty programs, and third-party vendor connections create multiple entry points. Even when payment systems are not impacted, exposure of personal data can drive identity fraud and long-tail consumer distrust. For startups selling into retail and hospitality, this also increases demand for practical controls: data minimization, stronger vendor assessments, incident response readiness, and measurable monitoring to detect exfiltration early. The market continues to reward security products that help companies act faster and communicate more credibly under pressure.

Why It Matters: Breach claims and leak-site pressure are becoming a recurring operational reality for consumer brands, not an edge-case crisis.

Source: TechRepublic.

Snout Raises $110M to Tackle Rising Vet Bills With a Membership Model

Snout disclosed a $110 million financing package, combining a $10 million Series A with $100 million in debt, as it builds a membership-style approach to pet care costs. The timing is notable: consumers are facing cost pressure across essentials, and pet healthcare has become a significant recurring expense for many households.

While this is not a pure “AI” play, it reflects a broader funding pattern: startups that can pair recurring demand with predictable unit economics can still raise meaningful capital. The inclusion of large debt financing also signals that the model is being positioned as much as a financial product as a consumer subscription, where underwriting, utilization, and risk controls become central. For tech and startup watchers, Snout is a reminder that “durable demand + financial structure” can be as investable as frontier tech, especially when the category is large, and pain is obvious.

Why It Matters: Investors still fund non-hype businesses when the market is big and the economics can be structured to scale.

Source: Fortune.

Limy Raises $10M to Build “AI Storefront” Infrastructure for Agentic Commerce

Limy raised $10 million to build tooling to help brands show up in LLM-driven shopping and recommendation experiences. The premise is that as AI agents become an interface for discovery and purchasing, brands will need new ways to publish product information, pricing, and conversion flows that machines can reliably interpret and act on.

This is early, but strategically important. If agent-driven shopping expands, it could reroute traffic away from traditional search and marketplaces—and change how attribution, advertising, and conversion measurement work. For retailers, the risk is becoming “invisible” to the agent layer if data is incomplete or poorly structured. For platforms, it creates a new monetization surface: pay to be recommended, pay to be the default option, or pay to influence the agent’s ranking logic. For startups, it’s an emerging infrastructure category that sits between marketing tech, product data, and e-commerce operations.

Why It Matters: If AI agents become buyers, the commerce stack will need a machine-readable layer for trust, ranking, and checkout.

Source: TMCnet.

Wall Street’s New AI Question: Where Are the Payoffs From Data Center Spending?

Investor scrutiny is intensifying as Big Tech companies commit extraordinary budgets to AI infrastructure while growth varies across cloud and platform businesses. The market is increasingly asking not whether AI is important, but how quickly spending converts into margins, revenue expansion, and defensible product advantages.

This matters for the entire ecosystem because Big Tech capex influences everything downstream: GPU supply, cloud pricing, startup compute availability, and even power procurement. If investors demand tighter discipline, it could change the pace of data center expansion and shift emphasis toward efficiency—better utilization, model distillation, and lower-cost inference. On the flip side, if the payoffs are clear, the cycle can accelerate and pull more capital into adjacent infrastructure like networking, cooling, and energy. For founders, the near-term reality remains: building on top of hyperscalers means your costs and constraints can be shaped by forces far outside your control.

Why It Matters: The next phase of AI isn’t about belief—it’s about proving returns on infrastructure at scale.

Source: Fortune.

Big Tech Faces a $680B Depreciation Wave From AI Capex

A key financial consequence of the AI data center boom is coming into view: depreciation. As companies pour hundreds of billions into hardware and facilities, accounting charges will rise over the next several years, potentially pressuring margins and changing how investors evaluate “profitability” during an infrastructure buildout.

This is not just an accounting footnote. Depreciation schedules, useful life assumptions, and hardware refresh cycles will matter more as AI accelerators evolve quickly and data centers become more specialized. Companies that extend asset lifetimes can smooth earnings in the short term, but may face sharper write-downs later if hardware becomes obsolete faster than expected. For the startup ecosystem, these economics can influence cloud pricing and capacity decisions; if hyperscalers need faster payback, they may optimize for utilization and contract structure in ways that ripple into enterprise procurement.

Why It Matters: The AI boom is turning Big Tech into infrastructure giants—and the financial gravity of that shift is increasing.

Source: Financial Times.

Amazon Cuts 16,000 Corporate Roles as It Restructures Around AI and Efficiency

Amazon is eliminating roughly 16,000 corporate jobs, extending a broad restructuring push to reduce layers and bureaucracy, while increasingly leaning on automation and AI-driven efficiency. The cuts follow earlier workforce reductions and signal that operational streamlining is now a structural priority, not a temporary response to macro pressure.

For the tech ecosystem, the key signal is how companies frame these decisions: not simply as cost-cutting, but as organizational redesign. That has a downstream impact on cloud services, internal tooling, and the vendor landscape that supports large enterprises. It also raises the debate about AI and knowledge work displacement: even when companies cite “efficiency,” the underlying reality is that software automation is steadily replacing certain classes of corporate process work, while increasing demand for highly technical roles that build and govern these systems.

Why It Matters: Big Tech is normalizing AI-driven org redesign, which will reshape hiring, tooling demand, and startup opportunities in enterprise workflows.

Source: TechStartups via CNBC and Reuters.

Wrap Up

That’s the signal from today’s cycle: AI is no longer a side feature. It’s becoming the infrastructure layer that rewires browsers, clouds, chips, and corporate budgets, while security teams fight to keep the seams from splitting under pressure. As Big Tech accelerates spending, restructures org charts, and absorbs the financial gravity of depreciation, the next winners will be the ones who can pair scale with trust, resilience, and clear returns.

That’s your quick tech briefing for today. Follow us on X @TheTechStartups for more real-time updates.