As organizations move deeper into 2026, several powerful trends are reshaping the HR function. From AI-powered analytics to skills-based hiring, these developments are changing the way companies build and manage their workforces.

AI Is Transforming Talent Management

Artificial intelligence is becoming one of the most influential technologies in human resources. HR teams are increasingly using AI-powered platforms to streamline recruitment, screen candidates, analyze employee performance, and forecast workforce needs.

Modern HR systems can process thousands of applications in seconds, identify skills gaps across departments, and recommend targeted training programs for employees. AI-driven workforce analytics also allow companies to predict employee turnover risks and detect engagement challenges earlier than traditional HR methods.

According to Gartner, organizations are rapidly adopting AI-enabled HR tools to improve decision-making and workforce planning. The research firm notes that “AI is helping HR leaders move from descriptive reporting to predictive and prescriptive insights about their workforce.”

Companies are using these insights to make more informed hiring decisions, allocate training budgets more effectively, and improve employee retention strategies. However, experts caution that the growing use of automation in HR must be accompanied by responsible governance.

Gartner also warns that HR leaders must ensure transparency and fairness when deploying AI tools in recruitment and talent management to prevent unintended bias in automated decision-making.

Skills-Based Hiring Is Replacing Traditional Credentials

Another major shift in HR strategy is the growing emphasis on skills-based hiring. Instead of focusing primarily on academic degrees or job titles, many companies are prioritizing demonstrable skills and practical experience.

According to the LinkedIn Global Talent Trends report, employers are increasingly adopting skills-based hiring to expand the talent pool and identify candidates who might otherwise be overlooked through traditional recruitment processes.

LinkedIn notes that “skills are becoming the new currency of work,” with companies prioritizing capabilities such as digital literacy, data analysis, and AI-related expertise.

This shift is particularly visible in the technology sector, where the pace of innovation often outpaces traditional education systems. As a result, organizations are investing more heavily in internal training programs, certification pathways, and continuous learning initiatives.

The trend reflects a broader realization that the future workforce will need constant reskilling to keep pace with technological change.

Employee Experience Becomes a Strategic Priority

Employee expectations have changed dramatically in recent years. Workers increasingly seek flexibility, purpose-driven work, and stronger support for mental health and wellbeing.

As a result, HR leaders are placing greater emphasis on employee experience — a concept that encompasses workplace culture, leadership quality, career development opportunities, and digital workplace tools.

According to Deloitte’s Global Human Capital Trends report, organizations are increasingly recognizing that employee experience has a direct impact on business performance. The report states that “organizations that prioritize the human experience are more likely to achieve stronger engagement, productivity, and retention outcomes.”

Companies are therefore investing in tools that measure employee sentiment through pulse surveys, engagement analytics, and real-time feedback platforms.

These technologies allow HR teams to identify emerging workplace issues early and implement targeted improvements before dissatisfaction spreads across teams.

Hybrid Work Is Becoming the Long-Term Model

The shift toward hybrid work has become a defining feature of the modern workplace. Many organizations now combine remote work flexibility with in-office collaboration to balance productivity, employee satisfaction, and organizational culture.

According to research by McKinsey & Company, hybrid work arrangements are expected to remain a permanent component of the global labor market. The firm notes that flexible work models can significantly influence employee retention and talent attraction strategies.

McKinsey reports that employees consistently rank workplace flexibility among the most important factors when evaluating job opportunities.

For HR leaders, hybrid work requires new management frameworks. Performance evaluation is increasingly shifting from measuring hours spent in the office to focusing on outcomes, project results, and team collaboration.

Workforce Analytics Is Becoming Central to HR Strategy

Data-driven decision-making is becoming a core capability for modern HR teams. Workforce analytics platforms combine performance data, engagement metrics, and operational insights to help organizations understand how teams function and where improvements are needed.

According to Deloitte, the increasing availability of workforce data is transforming HR into a strategic business function. The firm notes that advanced people analytics enables organizations to identify productivity patterns, forecast staffing needs, and evaluate the effectiveness of leadership programs.

By integrating HR data with financial and operational metrics, companies can align workforce strategies more closely with business objectives.

This shift is also changing the skillset required of HR professionals. Data literacy, analytics capabilities, and technological expertise are becoming essential competencies for HR leaders.

Regulation and Responsible AI Governance

As AI systems become more deeply integrated into hiring and workforce management, governments are introducing new regulations to ensure ethical use of employee data and automated decision-making systems.

The Society for Human Resource Management (SHRM) has highlighted growing regulatory attention on algorithmic hiring tools and employee monitoring technologies. According to SHRM research, organizations must establish clear governance frameworks to ensure transparency, fairness, and data protection.

Failure to address these issues could expose companies to legal risks as well as reputational damage.

HR leaders therefore face a growing responsibility to balance technological innovation with ethical and regulatory compliance.

HR Is Becoming a Strategic Business Function

Perhaps the most important shift in recent years is the transformation of HR itself. Rather than functioning solely as an administrative department, HR is becoming a strategic partner in shaping organizational success.

According to Deloitte, the role of HR is evolving from operational support to “architect of the workforce experience,” with responsibility for aligning talent strategies with long-term business goals.

Chief Human Resources Officers are increasingly involved in digital transformation initiatives, leadership development strategies, and workforce planning efforts designed to prepare organizations for the AI-driven economy.

In an era defined by rapid technological change and evolving employee expectations, the organizations that succeed will be those that treat talent strategy as a core component of business strategy. HR leaders who embrace data, technology, and employee-centric thinking will play a critical role in building resilient and future-ready workforces.

Business Insider reported that affected staff began receiving emails around 6 a.m. ET and were then quickly locked out of internal systems. In the email reviewed by the publication, Oracle told workers,

“After careful consideration of Oracle’s current business needs, we have made the decision to eliminate your role as part of a broader organizational change. As a result, today is your last working day.”

Posts from laid-off employees indicate the cuts touched Oracle Health, Sales, Cloud, Customer Success and NetSuite, underscoring that this was not limited to a single division. Oracle has not publicly detailed the total number of jobs eliminated, but the move comes as the company ramps up spending on AI data centers and broader restructuring.

The new model however attracted criticism for using Kimi 2.5 – an open source model by the Chinese company Moonshot AI with just additional reinforcement learning according to the X user Fynn. They said:

“[A]t least rename the model ID,”

At first, the company did not mention Moonshot AI or Kimi in its announcement, which quickly raised questions about the role of the AI “arms race” between the United States and China in the situation.

Lee Robinson, Cursor’s vice president of developer education, responded by saying, that Cursor’s use of Kimi was consistent with the terms of its license. He also clarified

“Only ~1/4 of the compute spent on the final model came from the base, the rest is from our training.”

As a result, he said Composer 2’s performance on various benchmarks is “very different” from Kimi’s.

The company Kimi also joined the conversations posting:

“We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor’s continued pretraining & high-compute RL training is the open model ecosystem we love to support.”

Cursor co-founder Aman Sanger said in a post,

“It was a miss to not mention the Kimi base in our blog from the start. We’ll fix that for the next model.”

The Ring Pro is central to Ultrahuman’s comeback strategy, featuring a redesigned unibody metal structure that helped the startup secure U.S. clearance. The new device boasts improvements such as longer battery life and enhanced on-device processing, and is available for U.S. pre-orders starting at $399. Kumar said

“We believe the Ring Air is a non-infringing model, and we are fighting that in federal court in the U.S.”

The new approval follows an October ruling by the U.S. International Trade Commission in favor of Oura, that significantly restricted imports of Ultrahuman’s earlier Ring Air model.This decision resulted in as much as $50 million in lost sales, according to CEO Mohit Kumar. The U.S. continues to be the most vital market for smart rings, representing about 60% of the 4.4 million units sold globally in 2025.

The period of import restrictions has also caused rapid market consolidation in favor of Oura. The company capitalized on Ultrahuman’s absence, increasing its U.S. market share from 63.3% to 85%, while Ultrahuman’s share plummeted to low single digits from its peak of 24.6% in Q2 2025. The U.S. market previously accounted for up to 50% of Ultrahuman’s revenue. The company plans an immediate and aggressive rollout of the Ring Pro. According to Kumar it will take five to six months to reach full scale as it rebuilds its supply chain and distribution.

Image: Ultrahuman

He stated he’s pursuing this project because semiconductor manufacturers are not producing chips fast enough to meet his companies’ AI and robotics demands. Musk said:

“We either build the Terafab or we don’t have the chips, and we need the chips, so we build the Terafab.”

According to Bloomberg Musk shared his plans during an event in downtown Austin, Texas, with a photo indicating that the “Terafab” facility will be “gigafactory,” located near Tesla’s Austin headquarters.

He also added that the aim is to produce chips capable of supporting 100–200 gigawatts of computing power annually on Earth, as well as one terawatt in space. He did not provide a timeline for the plan.

Image: Presentation of the Terafab project

The findings show that while the number of zero-day vulnerabilities exploited in the wild fluctuates year to year, the strategic value of these vulnerabilities for cyber espionage, surveillance, and financially motivated attacks continues to grow. The report also reveals a shift in targeting priorities—from consumer devices toward enterprise infrastructure—reflecting how attackers increasingly aim for systems that can provide broader access to corporate networks and sensitive data.

Zero-Day Exploits Remain a Core Tool for Advanced Threat Actors

According to the analysis published by Google’s Threat Intelligence Group, researchers tracked 75 zero-day vulnerabilities actively exploited in the wild during 2024, a decline from 98 recorded in 2023 but still significantly higher than earlier years. 

Zero-day vulnerabilities—software flaws that are unknown to vendors at the time of exploitation—are among the most powerful tools in cyber operations because they allow attackers to bypass security controls before patches are available.

Despite the slight decline in the number of exploited vulnerabilities, the report emphasizes that zero-day activity remains at historically elevated levels compared with the pre-2021 period, suggesting that exploitation has become a standard technique in advanced cyber campaigns. 

What makes this trend particularly concerning is that the majority of these attacks are not random. Instead, they are typically deployed in targeted operations conducted by sophisticated threat actors, including nation-state groups and commercial surveillance vendors.

A Strategic Shift Toward Enterprise Technologies

One of the most notable conclusions of the report is the growing shift away from consumer targets and toward enterprise technologies.

Google’s researchers found that 33 of the zero-day vulnerabilities exploited in 2024 affected enterprise software, including networking appliances, security tools, and enterprise infrastructure platforms.

This shift reflects the evolving priorities of threat actors. Compromising enterprise technologies often provides attackers with a gateway into entire organizational environments. Once inside, adversaries can move laterally across systems, escalate privileges, and access sensitive data or intellectual property.

Enterprise infrastructure is particularly attractive because it often acts as the backbone of corporate networks. A vulnerability in a network security appliance, for example, can allow attackers to bypass perimeter defenses and gain persistent access to internal systems.

The report also notes that organizations increasingly rely on complex technology stacks, which expands the potential attack surface and increases the likelihood that exploitable vulnerabilities will exist somewhere within the infrastructure.

Espionage Operations Still Drive Zero-Day Development

While cybercrime continues to grow globally, the research indicates that cyber espionage operations remain one of the primary drivers of zero-day exploitation.

Government-backed threat actors often rely on zero-day vulnerabilities to gain covert access to targeted networks. These actors typically prioritize stealth and persistence over scale, deploying exploits selectively against high-value targets such as government agencies, defense contractors, telecommunications providers, and research institutions.

The report also highlights the continued role of the commercial spyware industry, which develops and sells advanced exploit chains to governments and law enforcement agencies. Some surveillance vendors have been linked to multiple zero-day vulnerabilities over the past several years, demonstrating how the commercialization of cyber capabilities is reshaping the threat ecosystem.

In these cases, vulnerabilities are not simply discovered and used by hackers but are developed as part of an organized market for offensive cyber tools.

2025 zero-days in end-user vs enterprise products

Browsers and Mobile Platforms Remain Critical Attack Surfaces

Although enterprise technologies are becoming increasingly attractive targets, browsers and mobile platforms remain central to many zero-day campaigns.

Web browsers represent a particularly valuable attack vector because they serve as the primary interface between users and the internet. Vulnerabilities in browser engines can allow attackers to execute malicious code simply by tricking users into visiting a specially crafted webpage.

Several real-world cases illustrate this risk. Security researchers have documented browser vulnerabilities that enable attackers to escape sandbox protections or execute arbitrary code, potentially allowing full system compromise.

Mobile operating systems are similarly targeted due to their widespread adoption and the sensitive data stored on modern smartphones. Attackers frequently chain multiple vulnerabilities together—combining browser flaws with privilege-escalation exploits—to achieve complete device takeover.

These exploit chains are particularly valuable in surveillance operations where the goal is long-term access to communications, location data, or encrypted messaging platforms.

The Growing Role of Rapid Vulnerability Patching

One of the more positive findings of the report is that vendor patching processes have improved significantly in recent years.

Technology companies now deploy patches faster and coordinate more closely with security researchers through responsible disclosure programs. Initiatives such as Google’s Project Zero have helped standardize vulnerability reporting timelines and encouraged faster remediation cycles.

These improvements have contributed to the decline in some categories of exploit activity. However, the report cautions that attackers have adapted by focusing on less-scrutinized technologies, particularly specialized enterprise products and network appliances.

In many cases, these systems are deployed in environments where patching is slower or operationally difficult, creating a window of opportunity for attackers to exploit vulnerabilities before they are addressed.

Exploit Development Is Becoming More Sophisticated

Another major theme in the report is the increasing sophistication of exploit development.

Modern zero-day attacks frequently involve multi-stage exploit chains that combine several vulnerabilities across different components of a system. This approach allows attackers to bypass multiple layers of defense and maintain persistence even after partial detection.

For example, an attacker may begin with a browser vulnerability to execute code on a target machine. From there, a second exploit could elevate privileges, while a third vulnerability allows the attacker to escape security sandboxes or virtualization environments.

These complex exploit chains require advanced research capabilities and are often developed by well-resourced threat actors.

This graph only reflects clusters for which we can assess motivation. In one case, we identify two groups that are separately exploiting the same vulnerability.

The Strategic Implications for Enterprises

For enterprise security teams, the report underscores a fundamental reality: preventing zero-day exploitation entirely is nearly impossible.

Instead, organizations must focus on defense-in-depth strategies that limit the damage when vulnerabilities are exploited.

This includes measures such as:

• strict network segmentation
• zero-trust architectures
• continuous monitoring of privileged accounts
• rapid patch management
• proactive threat hunting

By assuming that some vulnerabilities will inevitably be exploited, security teams can design systems that prevent attackers from achieving their ultimate objectives.

AI and Automation Are Changing Both Sides of Cybersecurity

Looking ahead, the report suggests that artificial intelligence and automation will increasingly influence the zero-day ecosystem.

AI-driven tools are already being used by defenders to identify vulnerabilities and analyze exploit patterns more efficiently. At the same time, threat actors are beginning to experiment with AI-assisted malware development and automated reconnaissance.

This dynamic creates an arms race in which both attackers and defenders rely on increasingly sophisticated technologies to gain an advantage.

The Future of Zero-Day Exploitation

Google’s analysis ultimately reinforces a broader conclusion about the future of cybersecurity: zero-day vulnerabilities will remain a central component of advanced cyber operations.

Even as vendors improve patching practices and strengthen security architectures, attackers continue to invest heavily in discovering new vulnerabilities and developing sophisticated exploit techniques.

For governments, enterprises, and technology providers, this means that cybersecurity strategies must evolve beyond reactive patching. Proactive vulnerability research, threat intelligence sharing, and resilient system design will be essential in defending against the next generation of zero-day attacks.

As the digital economy becomes increasingly dependent on interconnected infrastructure, the stakes surrounding zero-day vulnerabilities—and the race to exploit or defend them—are only likely to grow.

Graphics: Google

At a time when many teams report that AI has accelerated code production faster than it has sped up the rest of the software lifecycle, Cursor is making the case that the bottleneck has moved: not writing code, but reviewing it, keeping it healthy, and responding when it breaks. Automations, the company argues, are meant to help scale those “other parts of the development lifecycle” that haven’t kept pace with agent-driven coding.

From “agent in your editor” to “agent in your pipeline”

Cursor describes Automations as cloud agents that spin up on demand in a cloud sandbox, follow instructions defined by the user, and “verify” their own output. The same automations can be configured with the models and MCPs (Model Context Protocol servers) a team already uses, and Cursor says agents can also use a memory tool to learn from past runs and improve over time.

The product framing is straightforward: define a trigger, write the prompt, choose the tools the agent can access—and then let it run continuously in the background. In the company’s forum announcement, Cursor highlights the breadth of actions these agents can take, including opening pull requests, commenting on code, sending Slack messages, calling MCP servers, and using “Memories” across runs.

For teams that have already experimented with Cursor’s agentic workflows, Automations is essentially a shift in posture: agents aren’t just helpers you invoke; they become workers you schedule.

The practical use cases: review, monitoring, and “chores”

Cursor’s launch post groups early automations into two buckets: review/monitoring and chores (recurring engineering tasks and cross-tool knowledge work).

Review and monitoring: agents as continuous codeowners

Cursor’s first claim is that automations can review changes at scale—from “style nits” to “security vulnerabilities and performance regressions.”

The company points to Bugbot as the conceptual predecessor: a review agent that runs when PRs are opened or updated, “triggered thousands of times a day,” and which Cursor says has “caught millions of bugs” since its launch. Automations, Cursor argues, generalizes that idea so teams can create many specialized reviewers.

Cursor shares three internal examples:

• Security review automation triggered on every push to main, built to run longer (without blocking PR flow), audit diffs for vulnerabilities, avoid re-litigating issues already discussed in the PR, and post high-risk findings to Slack. Cursor says this has already caught “multiple vulnerabilities and critical bugs.”
• “Agentic codeowners” automation that classifies PR risk based on blast radius, complexity, and infrastructure impact; auto-approves low-risk PRs; assigns up to two reviewers for higher-risk changes based on contribution history; then summarizes decisions in Slack and logs them to Notion via MCP for audit and tuning.
• Incident response automation triggered by PagerDuty, using Datadog via MCP to investigate logs, checking the repo for recent changes, and notifying on-call engineers in Slack—along with a PR proposing a fix. Cursor says this “significantly reduced” incident response time.

The pattern is notable: Cursor is not positioning these agents as simply generating suggestions, but as executing a loop—classify → investigate → act → write artifacts (Slack message/Notion log/PR)—that maps closely to how engineering teams actually operate.

Chores: daily and weekly agent work that stitches tools together

Cursor’s second bucket covers recurring tasks, including:

• Weekly Slack digest summarizing meaningful repo changes over seven days, highlighting major merged PRs, bug fixes, technical debt, and security/dependency updates.
• Test coverage automation that runs every morning to identify coverage gaps, add tests following existing conventions, run relevant test targets, and open a PR.
• Bug report triage, referenced as another chores category where agents can help consolidate and process incoming issues.

This category is where “always-on” becomes more than a slogan: the work isn’t prompted by a developer thinking to ask, but by time, process, or operational signals.

Early signals: Rippling and the rise of personal “ops agents”

Cursor’s post also includes a real-world example from Rippling, where a staff engineer describes building automations as a personal assistant layer on top of daily work streams. In Cursor’s telling, the workflow looks like this: drop meeting notes, action items, Loom links, and TODOs into a Slack channel; then a cron-based automation runs every two hours, reads those items alongside GitHub PRs, Jira issues, and Slack mentions, deduplicates them, and posts a dashboard.

Rippling also uses Slack-triggered automations to turn threads into Jira issues and summarize discussions into Confluence, extending into tasks like incident triage, weekly status reports, and on-call handoff.

That kind of “personal operations layer” is a strong indicator of where teams may take this next: not just making CI smarter, but making the organization more legible by having agents continuously translate conversations and events into structured artifacts.

“Anything can be an automation”—but the real bet is governance

Cursor’s launch leans on practitioner enthusiasm. A Decagon engineer highlights flexibility:

I love that automations work for both quick wins and more complex workflows… I still have full flexibility to catch any webhook or plug into custom MCPs when I need to.

And Rippling’s Tim Fall frames the value as focus and offloading:

Automations have made the repetitive aspects of my work easy to offload… Anything can be an automation!

But the deeper product bet is visible in Cursor’s internal examples: audit trails, Slack summaries, Notion logging, risk classification, guardrails, and “verify its own output.” This isn’t just “agents run code”; it’s “agents run processes,” which raises governance questions: What can the agent merge? What can it access? How do you review its actions? Who owns failures?

Cursor’s “agentic codeowners” example is telling precisely because it emphasizes risk scoring and auditability—not just speed.

Toward a “factory that creates your software”

Cursor’s headline metaphor is explicit: automations are “powered by cloud agents that use their own computers to build, test, and demo their work,” and teams can now “build the factory that creates your software” by configuring agents to continuously monitor and improve a codebase.

A Runlayer co-founder, quoted by Cursor, sums up the aspiration as leverage—small teams moving like large ones:

We move faster than teams five times our size because our agents have the right tools, the right context, and the right guardrails.

In the near term, the most credible impact is in the unglamorous corners of engineering: security checks that don’t block PRs, tests written after merges, incident response that starts before humans join the channel, and triage that turns chaos into queues. If Cursor Automations works as described, the “agent era” story shifts from better autocomplete to a more radical claim: software teams can operationalize AI as an always-on layer of labor embedded directly into the engineering pipeline.

Image: Cursor Blog

AI First, But Not Chatbot First

While many tech companies rushed to integrate chatbots into their apps, Airbnb took a different route.

We started by solving the hardest problem, customer support,

Chesky said during the call.

The company built a custom AI agent trained on millions of Airbnb’s historical support interactions. According to Chesky, the system is already resolving one-third of customer support cases without human intervention — significantly reducing resolution times. The AI support agent is currently live across North America, with a global rollout planned.

Toward an AI-Native Airbnb App

Customer support is only the beginning. Chesky described a broader vision of an “AI-native” Airbnb experience.

We’re building an AI-native experience where the app doesn’t just search for you, it knows you,

he said.

In practical terms, that means AI will eventually help guests plan entire trips — not just book accommodations. It will also assist hosts in running their businesses more effectively and help Airbnb operate more efficiently at scale.

To lead this transformation, Airbnb hired Ahmad Al-Dahle as CTO. Al-Dahle previously spent 16 years at Apple and most recently led the generative AI team at Meta that built the Llama model. Chesky called him “one of the world’s leading AI experts,” highlighting his ability to combine large-scale technical systems with design-focused user experiences.

AI as a Competitive Moat

Airbnb’s AI ambitions also serve as a defensive strategy in a world increasingly shaped by AI search and aggregators.

This approach is also our strongest defense against disintermediation,

Chesky said.

While chatbots might surface listings from across the web, Airbnb argues that its proprietary ecosystem creates a barrier to replication.

A chatbot can give you a list of homes, but it can’t give you the unique points you find on Airbnb,

Chesky said.

He pointed to Airbnb’s 200 million verified identities, 500 million proprietary reviews, integrated messaging between guests and hosts (used by 90% of guests), global payment infrastructure, customer support, and insurance.

By layering AI over the entire Airbnb experience, we believe we’re building something that’s impossible to replicate,

he added.

AI Inside the Company

The AI push extends internally. Airbnb said that 80% of its engineers are already using AI tools in their workflows — with a goal of reaching 100%.

Spotify Co-CEO: “Our Best Developers Have Not Written a Single Line of Code Since December”

That level of adoption signals that AI is not just a product initiative but a productivity multiplier across the organization, potentially accelerating feature development, services expansion, and experimentation.

Beyond Homes: Services, Hotels, and the ‘Airbnb Trip’

AI will also play a role in Airbnb’s broader product strategy, which Chesky described as the “Airbnb trip.” The company has been expanding beyond accommodations into services and experiences, launching them globally in May but scaling city by city.

We are one app and one brand, where every part of the trip makes the other parts stronger,

Chesky said.

Airbnb has started in cities like Paris for experiences and Los Angeles for services, while also testing new offerings such as grocery delivery and airport pickups. The company is also bringing boutique and independent hotels onto the platform, calling the opportunity “massive.”

AI will serve as connective tissue across these verticals — helping users move fluidly from booking a home to reserving an experience, ordering services, or selecting a hotel — all within a personalized interface.

The Long Game

Airbnb emphasized that it is still early in its AI journey but signaled strong expectations around product and services growth, platform enhancements, and the impact of AI investments.

Unlike companies that treat AI as a bolt-on feature, Airbnb is attempting to re-architect its entire platform around intelligence, identity, and integrated services.

Material by Iva Abadjievа

Image: Wikipedia, Portrait of Brian Chesky, 2025

According to multiple media reports citing sources familiar with the matter, NVIDIA is in discussions to participate in OpenAI’s next funding round, potentially committing around $20 billion. The investment would form part of a much larger capital-raising effort by OpenAI as it seeks to finance the massive compute and data-centre capacity required to train and deploy next-generation AI models.

The talks come after earlier headlines in 2025 suggested a far more ambitious $100 billion strategic collaboration between the companies, centred on building large-scale AI infrastructure and co-optimising hardware and software. However, subsequent reporting indicated that the original framework was non-binding and had not progressed into a definitive agreement, prompting questions about the true scale and structure of the relationship.

Strategic alignment, practical limits

At a strategic level, the logic of a deal remains clear. NVIDIA dominates the market for AI accelerators, while OpenAI is among the world’s largest and most demanding consumers of AI compute. A closer financial and technical partnership could secure long-term access to hardware for OpenAI and anchor NVIDIA’s position at the centre of the AI boom.

Yet the negotiations also reflect the growing complexity of AI economics. OpenAI’s compute needs are expanding faster than any single supplier can easily accommodate, and reports suggest the company has been evaluating alternative chip providers for certain workloads. That dynamic introduces leverage on both sides—and makes exclusivity less likely.

NVIDIA chief executive Jensen Huang has publicly played down concerns about tension, stating that NVIDIA intends to support OpenAI over the long term and could also participate in a future public offering. His comments suggest commitment, but stop short of confirming any finalised deal.

For now, the answer to whether there will be a deal between NVIDIA and OpenAI remains unresolved. What is clear is that both companies remain deeply interdependent, and any agreement—formal or informal—will have significant implications for the pace, cost and direction of AI development worldwide.

Material by Irina Kalaydjieva

IMAGE: NVIDIA

The latest selloff follows a period of strong earnings from major technology companies, highlighting a growing disconnect between operational performance and market sentiment. While revenues and profits across Big Tech remain robust, investors are increasingly focused on costs, capital intensity, and the sustainability of AI-driven growth.

AI optimism meets market caution

Much of the recent volatility has been concentrated in AI-linked stocks, which have led market gains over the past two years. As companies accelerate spending on data centres, chips, and infrastructure to support generative AI, markets are beginning to question how quickly those investments will translate into higher margins.

Recent earnings updates from companies such as Alphabet and Microsoft underscore this tension. Both companies reported strong top-line growth and expanding AI adoption, yet also signalled sharply higher capital expenditure, prompting investors to reassess risk and near-term cash flow.

Valuations under scrutiny

Technology stocks entered 2026 trading at elevated valuation multiples, reflecting expectations that AI would unlock a new phase of productivity and revenue expansion. The current pullback suggests markets are now recalibrating those expectations.

Analysts point to several overlapping concerns:

• Slower-than-expected monetisation of AI products
• Rising costs for compute, energy, and specialised talent
• Greater sensitivity to interest rates and global macro uncertainty

As a result, even companies delivering double-digit growth have seen their share prices weaken, particularly those most exposed to AI infrastructure spending.

Global ripple effects

The selloff has not been limited to US markets. Asian and European technology shares have followed Wall Street lower, reflecting the global nature of the tech supply chain and investor exposure. Semiconductor stocks, cloud service providers, and software firms with heavy AI positioning have all faced renewed volatility.

This synchronised decline highlights how tightly global tech markets are now linked — and how quickly sentiment can shift when expectations change.

Not a crisis, but a reset

Despite the pressure, market observers caution against interpreting the downturn as a sign of structural weakness in the technology sector. Demand for cloud computing, AI services, digital advertising, and enterprise software remains strong. Instead, the current environment is increasingly viewed as a valuation reset rather than a collapse.

In this context, companies with diversified revenue streams, disciplined spending, and clear paths to AI monetisation are expected to outperform as markets stabilise.

What to watch next

Investors will be closely monitoring:

• Capital expenditure guidance from major tech firms
• Evidence of AI-driven revenue acceleration
• Signals from central banks on interest rate policy
• Enterprise demand for cloud and AI services

In the near term, volatility is likely to persist. Over the longer term, however, the fundamentals underpinning the technology sector — data, software, and automation — remain intact.

For now, global tech markets appear to be entering a more selective phase, where execution matters as much as ambition, and where scale alone may no longer be enough to sustain premium valuations.

Material by Iva Abadjievа

Image: Freepik