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The European Commission this week requested, but did not order Google to allow third party search engines in Europe access to its search data as a means to comply with the Digital Markets Act (DMA), legislation the Commission describes as a law designed to “make the markets in the digital sector fairer and more contestable.”

Google was sent a set of proposed measures on Wednesday that, according to a release, would grant third party search engines, including Qwant from France, Mojeek, based in the UK, swisscows from Switzerland, and Ecosia, Good, and metaGer, all headquartered in Germany, the ability to access search data, such as ranking, query, and click and view data “on fair, reasonable and non-discriminatory terms.”

In a statement, Teresa Ribera, executive vice-president for Clean, Just and Competitive Transition with the Commission, said that the decision “sets out the specifications we expect Google to follow to comply with its obligations under the [DMA]. Data is a key input for online search and for developing new services, including AI.”

The measures themselves cover several areas, including the scope of the search data Google must share, the means and frequency by which it must happen, and parameters for “setting fair, reasonable and non-discriminatory prices for search data.”

Move ‘far exceeds DMA’s original mandate’

In response to the Commission’s request, Clare Kelly, senior competition counsel for Google, said Thursday in a statement, “hundreds of millions of Europeans trust Google with their most sensitive searches, including private questions about their health, family, and finances, and the Commission’s proposal would force us to hand this data over to third parties, with dangerously ineffective privacy protections.”

The company, she said, “will continue to vigorously defend against this overreach, which far exceeds the DMA’s original mandate and jeopardizes people’s privacy and security.”

Phil Höfer, board member of SUMA-EV, which develops and runs MetaGer, said, “the planned measure might help with optimizing and developing European competitors to Google’s search service, but is not what’s needed most at this time. As long as the Commission isn’t planning on forcing Google to share their index data as well, this will not do much.”

Even better, he said, would be for the Commission “to decide to continue funding the European Open Web Index and allow European actors to build a competing infrastructure. We are convinced that without a European index, the EU will not be able to compete with American search engine giants.”

Forrester Senior Analyst Dario Maisto said the decision from the Commission is “not too timely but definitely in line with the measures Europe needs to free up businesses and citizens from risky dependencies on foreign organizations, vendors, and technologies. The final outcome is truly uncertain, though: one thing is to provide access to data to other players, one other thing is to modify users’ behaviors. We have to remember that the synonym for doing a search on the internet is actually: Google it.” 

Brian Jackson, principal research director at Info-Tech Research Group, said that opening Google’s search data to third parties could make search more specialized again, especially in high-value verticals where users want results tailored to a specific industry or service need.

Enterprise digital teams, he said, may need to optimize for multiple discovery environments rather than relying just on Google alone, and software buyers could see more choice as search and intelligence vendors build on shared data.

In addition, said Jackson, “it could revive domain-specific search models, but I think a more fragmented search ecosystem might raise manipulation risks, fraud, and poisoned results. That would make governance and monitoring much more important.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, noted that, in terms of the impact on enterprises if Google shares search data under DMA, “this is being framed as a competition move, but that is not where the real impact sits. What is actually shifting here is control over how enterprise information is interpreted by machines.”

Definition of optimization is changing

For a long time, he said, “enterprises have quietly relied on the stability of a dominant discovery layer led by Google. That stability shaped everything from how content was written to how digital performance was measured. What is changing now is not just who has access to data, but how many systems can interpret that data.”

Gogia pointed out, “as alternative engines improve and start to matter, enterprises will find themselves operating in an environment where the same content can be surfaced differently, depending on which engine or AI system is doing the interpreting. That creates inconsistency, and over time, inconsistency becomes risk.”

There is, he said, also a deeper shift underneath all this: “Search is no longer just about helping users find information. It is increasingly the layer that feeds AI systems, copilots, and automated decisions. Once that layer fragments, enterprises no longer have a single reference point for how they are represented externally. That loss of coherence is subtle at first, but it builds into something much more material.”

Addressing the question of whether or not enterprises will need to optimize for multiple algorithms, he said, “the short answer is yes, but the bigger point is that the definition of optimization itself is changing. Enterprises are moving away from a world where they could tune for one dominant system into one where relevance is decided differently across multiple engines that do not follow the same rules.”

Search engines such as Qwant, Ecosia, and Mojeek, “each approach indexing and ranking differently,” Gogia said. “Some rely on their own infrastructure, others blend multiple data sources. The result is that the same piece of content can behave very differently across environments, even when nothing about the content itself has changed.”

What complicates this further, he said, “is the rise of AI-generated answers. Enterprises are no longer competing for links, they are competing to be included in summaries that may not even reveal where the information came from. That shifts the focus away from keywords and toward clarity, context, and credibility. The organizations that do well will be the ones whose content holds up across systems, not just within one.”

Interested parties have until May 1 to submit views on the proposed measures prior to a final decision, which will be binding on Google and must be adopted by July 27.

V programovacím jazyce Go naprogramovaná webová aplikace pro spolupráci na zdrojových kódech pomocí gitu Forgejo byla vydána ve verzi 15.0 (Mastodon). Forgejo je fork Gitei.

Současně se SUSECON 2026 proběhne příští čtvrtek v Praze také komunitní Open Developer Summit (ODS) zaměřený na open source a openSUSE. Akce se koná ve čtvrtek 23. 4. (poslední den SUSECONu) v Hilton Prague (místnost Berlin 3) a je zcela zdarma, bez nutnosti registrace na SUSECON. Na programu jsou témata jako automatizace (AutoYaST), DevOps, AI v terminálu, bezpečnost, RISC-V nebo image-based systémy. Všichni jste srdečně zváni.

Bug or feature?

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Admins who use Cisco Webex Services configured to use trust anchors within the SSO integration with Control Hub must install a new identity provider certificate to close a critical vulnerability, or risk losing access control.

Cisco said in an advisory this week that admins must upload a new identity provider (IdP) SAML certificate to Webex Control Hub, the web-based management portal where IT administrators can control all Cisco Webex services, including certificate management, meetings, messaging and calling. Failure to close this hole will allow an unauthenticated, remote attacker to impersonate any user within the service.

The vulnerability, CVE-2026-20184, carries a CVSS score of 9.8.

Because Webex is a cloud service, Cisco can, and has, patched its side of the application. But admins using single-sign on (SSO) still need to install the new certificate. There are no workarounds.

A Webex support article on managing SSO integration says that information about certificates is found in the Webex Control Hub Alerts center, where customers can view which ones are installed, and their status. The Control Hub also contains an SSO wizard to aid in updating certificates. The article contains step-by-step details on the process.

Asked for comment, and for more details about the vulnerability, a Cisco spokesperson didn’t go beyond the advisory. “Cisco published a security advisory disclosing a vulnerability in the integration of single sign-on with Control Hub in Cisco Webex Services,” the spokesperson said. “At the time of publication (April 15) Cisco had addressed the vulnerability, and was not aware of any malicious use of this vulnerability. Affected customers must update their SAML certificate to ensure uninterrupted services.”

Gartner analyst Peter Firstbrook noted in an email that, since Cisco has applied the patch to the cloud service, this is more of a configuration change. But that doesn’t minimize the possible damage. “While we are not aware of exploits using this vulnerability, users can lose SSO access to Webex without this change,” he said. 

“This does illustrate a bigger trend that identity and access management is the corporate perimeter,” he added, “and the majority of attacks include an identity and access management component. CISOs must increase their focus on IAM hygiene, particularly as agentic computing is accelerating.” 

Identity and access management is, of course, the keystone of cybersecurity. As Crowdstrike observed in its 2026 Global Threat Report, abuse of valid accounts accounted for 35% of cloud incidents it investigated last year, “reinforcing that identity has become central to intrusion.” Single sign-on allows a user to authenticate to multiple applications through one set of credentials. It’s efficient, and, of more importance to a CSO, strengthens security.

Additional critical fixes

The Webex flaw is one of three critical vulnerabilities Cisco identified and issued patches for this week. In addition, multiple vulnerabilities have to be patched in Cisco Identity Services Engine (ISE) and Cisco ISE Passive Identity Connector (ISE-PIC).

These holes (CVE-2026-20147 and CVE-2026-20148, which carry CVSS scores of 9.9), could allow an authenticated, remote attacker to perform remote code execution or conduct path traversal attacks on an affected device. To exploit these vulnerabilities, the attacker must have valid administrative credentials, and send a crafted HTTP request to an affected device. There are no workarounds.

Separately, two more vulnerabilities were found in ISE that could lead to remote code execution on the underlying operating system of an affected device. To exploit these vulnerabilities (CVE-2026-20180 and CVE-2026-20186), the attacker would only need Read Only Admin credentials.

This article originally appeared on CSOonline.

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The soap refill dispenser at a cafe in TRACE Expert City — a technology hub in Colombo, Sri Lanka — boldly declares that it was delivered by ”USAID, from the American people.”

The device is a relic from the past, reflecting goodwill that once existed between the US and Sri Lanka. Now, as external aid through USAID and other entities dries up, the island nation is looking inward for its future.

You’ll find some of that focus at TRACE’s campus, where energetic AI entrepreneurs with international ambitions brim with fresh ideas. Startup Jendo Innovations is already deploying AI-driven medical technology internationally. “We have the best affordable talent in the region to develop applications on AI,” said Heminda Jayaweera, executive director of TRACE.

Meanwhile, in Kathmandu, Nepal, venture capitalist Preeti Adhikary and medical doctor Kisu Rawal beam with joy as women entrepreneurs wrap up a series of AI training sessions. Adhikary sees in the women newfound confidence and fresh ideas about how to expand and streamline their businesses.

While much of the chatter about AI in the US carries with it an element of danger — hallucinations, rogue AI agents, leaked or stolen data — in Nepal the conversation centers around empowerment and upward mobility.

“While AI has been an equalizer…, it’s on us to find underserved communities and demographics to educate and enable,” said Adhikary, who is a general partner at Momo VC. She works to connect the Nepal tech ecosystem through initiatives like The Great Nepali Diaspora, a global network of Nepali talent, and The Empowered Women Network, which organized the recent AI training session.

Preeti Adhikary, venture capitalist and a general partner at Momo VC.

The Empowered Women Network

To be sure, AI is still in the Wild West phase in Nepal, Sri Lanka and many developing economies — just as it is elsewhere in the world. Locally, some startups have focused  AI tools on their unique cultures, demographics and business environments.

Some are focused on AI for good, especially in rural areas, where 33% of the population resides, according to the World Bank. 

Mobile devices are ubiquitous in those areas, which opens the door for AI-driven healthcare that can quickly diagnose diseases and reduce long lines in remote hospitals.

But culture also presents unique data challenges. Literacy remains an issue, and locals prefer interacting with AI using voice-driven methods rather than text, which changes customer feedback and service flows. 

Product recommendations are also made by word-of-mouth, cutting off one layer of product discovery and reshaping how AI models are built.

Because workforces in Nepal and Sri Lanka are still emerging, the concern over AI-driven job losses is not as dire as in the US, where job cuts have been attributed to automation via AI. And unlike in the US, legacy systems and technical debt are not a burden here. 

Enterprises with lean IT budgets have little appetite for digitizing legacy data, which could slow AI adoption. So, bootstrapped startups, facing limited hardware, capital and talent, hope to make their workflows and processes AI-native from the start.

Even so, the lack of resources makes innovation and skills development difficult. Nvidia GPUs, for instance, are particularly scarce in Nepal and Sri Lanka and prohibitively expensive. 

To work around the challenges, startups have turned to small language models (SLMs) and popular AI tools. “AI tools — which are powerful even at the free version — and open-source models have meant AI is truly democratized now,” Adhikary said.

AI adoption is poised to grow in both Nepal and Sri Lanka, both of which have Gen-Z-inspired governments that grew up on social media and are AI-ready. Nepal last year published a national AI strategy outlining plans to boost infrastructure, skills readiness and countrywide awareness.

It has room to grow. Nepal ranks 106 out of 190 countries in Oxford Insights’ 2025 Government AI Readiness Index. The nation’s goal is to reach the top 50.

Meanwhile, Sri Lanka is ranked 80th and is pushing AI adoption. The country in 2024 established the Ministry of Digital Economy and has an AI strategy to boost the local ecosystem.

And Sri Lanka’s new digital nomad program could bring diaspora and foreign tech workers back onshore to help bolster local AI development and adoption.

As in other parts of the world, there is an ongoing need for mentorship, domain expertise and access to networks to grow and scale, according to Adhikary. But AI  entrepreneurs in Nepal recognize the benefits of collaboration. 

“I’m pleasantly surprised … that a nation with a newish tech ecosystem can compete with New York and Silicon Valley in terms of ideas and technical acumen,” Adhikary said.

New technologies rarely leave work untouched. They also rarely eliminate the need for human contribution altogether.

Forecasts of the impact of artificial intelligence range from the apocalyptic to the utopian. An October 2025 report from Senate Democrats, for example, predicted AI will destroy millions of US jobs. A couple of years earlier, consultant company McKinsey forecast AI will add trillions to the global economy, while emphasizing job losses can be mitigated by training workers to do new things.

The problem is that many of these claims are based on projections, overly simplified surveys, or thought experiments rather than observed changes in the economy. That makes it hard for the public, and often policymakers, to know what to trust.

As a labor economist who studies how technology and organizational change affect productivity and well-being, I believe a better place to start is with actual data on output, employment, and wages—which are all looking relatively more hopeful.

AI and Jobs

In one of my new research papers with economist Andrew Johnston, we studied how exposure to generative AI affected industries across America between 2017 and 2024, using administrative data that covers nearly all employers. Our analysis covered a crucial period when generative AI use exploded, allowing us to analyze the effect within businesses and industries.

We measured AI exposure using occupation-level task data matched to each industry and state’s occupational workforce mix prior to the pandemic. A state and industry with more workers in roles requiring language processing, coding, or data tasks scored higher on exposure, for example, compared with one with more plumbers and electricians.

We then took that exposure ranking by occupation and looked at changes in the standard deviation in occupational exposure, comparing that with labor market and GDP across states and industries from 2017 to 2024.

Think of a standard deviation as roughly the gap between a paramedic—whose work centers on physical assessment, emergency response, and hands-on care that AI cannot easily replicate—and a public relations manager, whose work involves drafting communications, analyzing sentiment, and synthesizing information that AI tools handle well. That gap in AI exposure is roughly what we’re measuring when we ask: Does being on the higher-exposure side of that divide change your industry’s trajectory?

This data allowed us to answer two questions: When AI tools became widely available following the public release of ChatGPT in late 2022, did states and industries that were more exposed to generative AI become more productive, and what happened to workers?

Our answers are more encouraging, and more nuanced, than much of the public debate suggests.

We found that industries in states that were more exposed to AI experienced faster productivity growth beginning in 2021—before ChatGPT reached the public—driven by enterprise tools already embedded in professional workflows, including GitHub Copilot for software development, Jasper for marketing and content writing, and Microsoft’s GPT-3-powered business applications. In 2024, for example, industries whose AI exposure was one standard deviation higher saw gains of 10% in productivity, 3.9% in jobs, and 4.8% in wages than comparable industries in the same state.

Those patterns suggest that, at least so far, AI has acted as a productivity-enhancing tool that boosts employment and wages rather than a simple substitute for labor.

Augmentation Versus Displacement

A crucial distinction in the data is between tasks where AI works with people and tasks where AI can act more independently. In sectors where AI mainly complements workers—think marketing, writing, or financial analysis—our data show that employment rose by about 3.6% per standard deviation increase in exposure.

In sectors where AI can execute tasks more autonomously—including basic data processing, generating boilerplate code, or handling standardized customer interactions—we found no significant employment change, though workers in those roles saw slower wage growth.

What these findings suggest is that when AI lowers the cost of completing a task and raises worker productivity, companies expand output enough to increase their demand for labor overall—the same logic that explains why power tools didn’t eliminate construction workers.

The economic question is not whether any given task disappears. It is whether businesses and workers can reorganize fast enough to create new productive combinations. And so far, in most sectors, our evidence suggests they can.

But state policies also matter: These benefits were concentrated in the states with more efficient labor markets, meaning that the impact of generative AI on workers and the economy also depends on the types of policies and institutions of the local economy.

Importantly, these findings hold beyond occupational exposure. In additional work with co-authors at the Bureau of Economic Analysis, we found a similar effect on GDP and employment when looking at actual AI utilization—that is, how often workers use AI. Drawing on the Gallup Workforce Panel, we measured workers actively using AI daily or multiple times a week. We found that each percentage-point increase in the share of frequent AI users in a state and industry is associated with roughly 0.1% to 0.2% higher real output and 0.2% to 0.4% higher employment.

To put that in context: The share of frequent AI users across all occupations rose from about 12% in mid-2024 to 26% by late 2025, a shift our estimates suggest corresponds to roughly 1.4% to 2.8% higher real output—or about 1 to 2 percentage points of annualized growth over that period.

New technologies rarely leave work untouched. But they also rarely eliminate the need for human contribution altogether. Instead, they change the composition of work, as our research shows. Some tasks shrink. Others expand. New ones emerge that were previously too costly or too hard to perform at scale. Put simply, some occupations might go away, but most of them just change.

If anything, the trends documented here are likely to strengthen rather than fade. Not only are generative AI tools rapidly improving, but also the experimentation and research and development that many workers and companies are engaging in are likely to pay large dividends. These investments—often referred to as intangible capital—tend to get unlocked a few years after a technology comes onto the scene, once complementary investments have been made.

The Role of Companies and Managers

Whether AI leads to anxiety or adaptation for workers depends in part on what happens inside organizations. Using additional data collected over many years in the Gallup Workforce Panel covering more than 30,000 US employees from 2023 to 2026, I found in a 2026 paper that workplace adoption of generative AI rose quickly over the period, with the share of workers using AI often increasing from 9% to 26%.

But the more important finding is that adoption was far more common where workers believed their organization had communicated a clear AI strategy and where employees said they trust leadership. This suggests that growing adoption and effective use of AI depends not only on the availability of the technology but on whether managers make its use clear, credible, and safe.

Where that clarity exists, frequent AI use is associated with higher engagement and job satisfaction, and it even reverses the burnout penalties that appear elsewhere.

In other words, the broader economic effects of AI depend not only on how sophisticated the tools are but on whether companies and managers create environments where workers can experiment, reorganize tasks, and integrate new tools into productive routines. That is, if employees do not feel the psychological safety to experiment, they are less likely to use AI, and they are especially less likely to use it for higher-value work.

That is precisely the kind of adaptation that I believe makes labor markets more resilient than the most alarmist forecasts suggest.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post Industries Most Exposed to AI Are Not Only Seeing Productivity Gains but Jobs and Wage Growth Too appeared first on SingularityHub.