Here are some of the the major AI trends shaping 2026 — based on current expert forecasts, industry reports, and recent developments in technology. The material is analyzed using AI tools and final version hand-edited to this blog text:
1. Generative AI Continues to Mature
Generative AI (text, image, video, code) will become more advanced and mainstream, with notable growth in:
* Generative video creation
* Gaming and entertainment content generation
* Advanced synthetic data for simulations and analytics
This trend will bring new creative possibilities — and intensify debates around authenticity and copyright.
2. AI Agents Move From Tools to Autonomous Workers
Rather than just answering questions or generating content, AI systems will increasingly act autonomously, performing complex, multi-step workflows and interacting with apps and processes on behalf of users — a shift sometimes called agentic AI. These agents will become part of enterprise operations, not just assistant features.
3. Smaller, Efficient & Domain-Specific Models
Instead of “bigger is always better,” specialized AI models tailored to specific industries (healthcare, finance, legal, telecom, manufacturing) will start to dominate in many enterprise applications. These models are more accurate, legally compliant, and cost-efficient than general models.
4. AI Embedded Everywhere
AI won’t be an add-on feature — it will be built into everyday software and devices:
* Office apps with intelligent drafting, summarization, and task insights
* Operating systems with native AI
* Edge devices processing AI tasks locally
This makes AI pervasive in both work and consumer contexts.
5. AI Infrastructure Evolves: Inference & Efficiency Focus
More investment is going into inference infrastructure — the real-time decision-making step where models run in production — thereby optimizing costs, latency, and scalability. Enterprises are also consolidating AI stacks for better governance and compliance.
6. AI in Healthcare, Research, and Sustainability
AI is spreading beyond diagnostics into treatment planning, global health access, environmental modeling, and scientific discovery. These applications could help address personnel shortages and speed up research breakthroughs.
7. Security, Ethics & Governance Become Critical
With AI handling more sensitive tasks, organizations will prioritize:
* Ethical use frameworks
* Governance policies
* AI risk management
This trend reflects broader concerns about trust, compliance, and responsible deployment.
8. Multimodal AI Goes Mainstream
AI systems that understand and generate across text, images, audio, and video will grow rapidly, enabling richer interactions and more powerful applications in search, creative work, and interfaces.
9. On-Device and Edge AI Growth
10. New Roles: AI Manager & Human-Agent Collaboration
Instead of replacing humans, AI will shift job roles:
* People will manage, supervise, and orchestrate AI agents
* Human expertise will focus on strategy, oversight, and creative judgment
This human-in-the-loop model becomes the norm.
Sources:
[1]: https://www.brilworks.com/blog/ai-trends-2026/?utm_source=chatgpt.com “7 AI Trends to Look for in 2026″
[2]: https://www.forbes.com/sites/bernardmarr/2025/10/13/10-generative-ai-trends-in-2026-that-will-transform-work-and-life/?utm_source=chatgpt.com “10 Generative AI Trends In 2026 That Will Transform Work And Life”
[3]: https://millipixels.com/blog/ai-trends-2026?utm_source=chatgpt.com “AI Trends 2026: The Key Enterprise Shifts You Must Know | Millipixels”
[4]: https://www.digitalregenesys.com/blog/top-10-ai-trends-for-2026?utm_source=chatgpt.com “Digital Regenesys | Top 10 AI Trends for 2026″
[5]: https://www.n-ix.com/ai-trends/?utm_source=chatgpt.com “7 AI trends to watch in 2026 – N-iX”
[6]: https://news.microsoft.com/source/asia/2025/12/11/microsoft-unveils-7-ai-trends-for-2026/?utm_source=chatgpt.com “Microsoft unveils 7 AI trends for 2026 – Source Asia”
[7]: https://www.risingtrends.co/blog/generative-ai-trends-2026?utm_source=chatgpt.com “7 Generative AI Trends to Watch In 2026″
[8]: https://www.fool.com/investing/2025/12/24/artificial-intelligence-ai-trends-to-watch-in-2026/?utm_source=chatgpt.com “3 Artificial Intelligence (AI) Trends to Watch in 2026 and How to Invest in Them | The Motley Fool”
[9]: https://www.reddit.com//r/AI_Agents/comments/1q3ka8o/i_read_google_clouds_ai_agent_trends_2026_report/?utm_source=chatgpt.com “I read Google Cloud’s “AI Agent Trends 2026” report, here are 10 takeaways that actually matter”
2,220 Comments
Tomi Engdahl says:
Dario Amodei:
An essay on policy responses to AI’s exponential progress across regulation and public safety, macroeconomics and taxes, science, civil liberties, geopolitics — In one of the side plots to The Lord of the Rings, two of the Hobbits attempt to rouse Treebeard—a wise but ponderous sentient tree …
Policy on the AI Exponential
https://darioamodei.com/post/policy-on-the-ai-exponential
In one of the side plots to The Lord of the Rings, two of the Hobbits attempt to rouse Treebeard—a wise but ponderous sentient tree—to defend his forest from an army that is cutting it down. The problem is that Treebeard operates at a very different speed than the Hobbits. It takes him a full day simply to say hello to another tree, so getting him and his peers to act fast enough is nearly impossible.
The intersection of AI and our political institutions feels a bit like the Hobbits and Treebeard. AI is advancing at a lightning pace—in only four years, AI models have gone from barely being able to write a coherent line of code to writing most of the code at major AI companies.
Similar gains have been made in biology, physics, math, finance, law, translation, and many other fields. AI’s scaling laws, which predict an exponential increase in general cognitive capabilities with increasing computing power, now have over a decade of empirical evidence behind them. If these scaling laws continue for only a year or two longer, we are likely to get what I’ve called Powerful AI, or “a country of geniuses in a datacenter”.
By contrast, policy—and especially legislation—moves very slowly. Often this is for good reasons: governments have grave powers, and it’s usually for the best that they aren’t used too hastily. But the mismatch in timescale is nevertheless very painful: in the several years that it can take Congress to act, AI can go from an amusing toy to the full country of geniuses.
Over the last few years since AI has become a major commercial technology, those of us who wanted to handle it responsibly have faced a dilemma. We could see clearly where the exponential was going: we strongly suspected that within a few years AI would be one of the rare technologies that fundamentally reshapes the entire policy landscape, in the same way that nuclear weapons reshaped geopolitics and the industrial revolution fundamentally reshaped every economic and social issue.
Given the limits imposed by this situation, many safety advocates (including Anthropic) have so far been focused on advocating for policy actions that preserve optionality, tee up a fast reaction in the future, or give the world better insight into what is coming down the pike – things like transparency legislation, export controls on chips, and data collection on AI’s labor effects. These are not enough, but they have felt like all that was possible.
In the last few months, however, the evidence of AI’s incredible power, as well as its risks, has become undeniable. Perhaps the most emblematic example is Claude Mythos Preview and the discovery that frontier models pose very real risks to cybersecurity, creating the potential for disruption of the financial sector, critical infrastructure, and national security. Mythos Preview scrambled the global cybersecurity landscape. But its broader significance is that it proves beyond doubt that AI models are now tools of global and national strategic consequence. The cyber risks that Mythos-class models present will not be the last that we must face. I believe that biological risks may soon follow, and that serious AI autonomy risks may not be far behind
.
Tomi Engdahl says:
Anthropic:
Anthropic releases two policy proposals on how governments should address catastrophic risks and manage labor market disruption from advanced AI systems — AI is advancing at exponential speed, and the policymaking process was built for a slower world. — We are sharing two policy proposals to prepare for AI progress.
Policy on the AI Exponential
https://www.anthropic.com/policy-on-the-ai-exponential
AI is advancing at exponential speed, and the policymaking process was built for a slower world.
We are sharing two policy proposals to prepare for AI progress. The first, our Advanced AI Framework, offers a roadmap for governing increasingly capable systems, from transparency and independent evaluation to government authority to block or deter dangerous deployments. The second, our Economic Policy Framework, turns to the question of how to prepare workers and the economy for AI’s impact and ensure the financial benefits of AI are broadly shared. Together they cover two sides of the same challenge: steering the technology responsibly as it advances, and preparing society for what it brings.
Tomi Engdahl says:
Dario Amodei / @darioamodei:
Dario Amodei says frontier models should face mandatory third-party testing for cyber, bio, and autonomy risks, in addition to overall transparency requirements
https://x.com/DarioAmodei/status/2064781778599268818
Tomi Engdahl says:
Maxwell Zeff / Wired:
Anthropic backtracks on a policy limiting Claude Fable 5′s ability to develop other AI models, after significant backlash from the AI research community — The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.
Anthropic Walks Back Policy That Could Have ‘Sabotaged’ AI Researchers Using Claude
The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.
https://www.wired.com/story/anthropic-responds-to-backlash-on-claudes-secret-sabotage-on-ai-research/
Tomi Engdahl says:
Lorenzo Franceschi-Bicchierai / TechCrunch:
Cybersecurity researchers complain that Claude Fable’s guardrails are too strict, rejecting “innocuous tasks” like reading blog posts or performing code reviews — Anthropic released its latest model Fable on Tuesday, billing it as a public and limited version of its powerful and much-hyped cybersecurity model Mythos.
Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable
https://techcrunch.com/2026/06/10/cybersecurity-researchers-arent-happy-about-the-guardrails-on-anthropics-fable/
Tomi Engdahl says:
Tom Warren / The Verge:
Sources: Microsoft is restricting employees from using Claude Fable 5 because of Anthropic’s new 30-day data retention requirements
Microsoft restricts Claude Fable for employees over data retention concerns
Microsoft’s legal teams are evaluating Anthropic’s new data retention changes.
https://techcrunch.com/2026/06/10/cybersecurity-researchers-arent-happy-about-the-guardrails-on-anthropics-fable/
Anthropic released Claude Fable, its first Mythos-class AI model, yesterday and it’s already causing concerns inside Microsoft. Sources tell me that Microsoft is limiting the use of Claude Fable 5 for employees because of Anthropic’s new data retention requirements.
While Microsoft quickly rolled out Claude Fable 5 to its GitHub Copilot and Foundry customers, I’m told the model isn’t available in the model picker that Microsoft employees use for internal versions of GitHub Copilot. All other Claude models are still available internally at Microsoft, because they operate under Zero Data Retention (ZDR) rules.
Tomi Engdahl says:
Dean W. Ball / @deanwball:
Anthropic secretly limiting Claude’s usefulness for LLM development strengthens the argument that Anthropic is using AI safety to justify monopolistic behavior
https://x.com/deanwball/status/2064665679307985244
My last observation re: Anthropic’s secret sabotage safety policy, is that it undermines actually good safety policy. How?
1. First, it is very plausible to describe this as anti-competitive behavior (even if you are maximally sympathetic to Anthropic here you must admit this), and it is behavior being justified in the name of AI safety. If you believe, as I and many Anthropic staff do, that it may end up being critically important to relax antitrust enforcement so that the frontier labs can cooperate and collaborate on some areas of AI safety, Anthropic just undermined the case for that in a large way.
2. Overall, this massively and profoundly raises the status of the argument that AI safety has been hype to justify monopolistic behavior by labs. I continue to believe AI safety is a real and serious issue that is growing in importance rather than diminishing. If you agree with me, this incident is a setback, maybe a serious one.
3. As I have observed elsewhere, Anthropic’s official corporate policy is structurally identical to the fact pattern alleged against them by the Department of War. I still think DoW acted both falsely and wrongly in that fight, but it is no longer possible to defend Anthropic with a full throat after this incident.
4. This raises the case for heavier handed regulations. Anthropic is making an awfully good case here that their products ought to be treated as utilities, and thus that their alignment practices should be a matter of public policy rather than private property. I am starkly opposed to this sort of state power grab, but Anthropic is doing more to justify it than anyone else.
5. Thus, significant damage has been done to a community and entire approach to AI governance. It was done unilaterally by Anthropic, likely motivated largely by self-interest and justified within the internal psychology of the firm through the lens of safety.
I suspect this is fixable in the economic and legal senses for Anthropic, but I fear the trust that has just been broken, and the goodwill extinguished, will take very much time to repair.
Tomi Engdahl says:
Raphael Satter / Reuters:
CISA shortens the deadline for US agencies to fix the most critical vulnerabilities in their networks to three days, citing hackers’ use of AI — The U.S. cyber defense agency said on Wednesday that government officials now have three days to deal with the most serious categories …
https://www.reuters.com/legal/litigation/us-shortens-cyber-fix-window-three-days-ai-threats-rise-2026-06-10/
Tomi Engdahl says:
Wall Street Journal:
Sources: OpenAI is considering drastically lowering its price for tokens in anticipation of similar cuts the company expects at Anthropic — The company might lower prices for tokens, the central unit for gauging AI costs, though the discussions are still in flux
https://www.wsj.com/tech/ai/openai-considers-drastic-price-cuts-anticipating-war-for-users-with-anthropic-9b8c178e?st=1Yyrco
Tomi Engdahl says:
Sam Sabin / Axios:
OpenAI says it has banned China-linked accounts that used ChatGPT to draft social media influence campaigns targeting US debates over tariffs and data centers — OpenAI has banned China-linked accounts that used ChatGPT to draft social media influence campaigns targeting U.S. debates …
China-linked operatives used ChatGPT to influence data centers debate: OpenAI
https://www.axios.com/2026/06/10/openai-china-ai-data-center-tariffs-chatgpt
Tomi Engdahl says:
Laurie Chen / Reuters:
Chinese companies are implementing “quiet” AI-driven layoffs to avoid labor laws that require government approval for job cuts exceeding 10% of a workforce — Liu, a Hangzhou-based contractor at a large Chinese internet firm, says her employer began quietly firing contractors …
https://www.reuters.com/business/world-at-work/china-inc-deploys-quiet-layoffs-beijing-promotes-ai-adoption-2026-06-10/
Tomi Engdahl says:
Samantha Subin / CNBC:
Alex Karp says Palantir’s enterprise customers are “unhappy” with how the frontier labs are operating, believing the labs only care about tokenmaxxing
Palantir’s Karp says businesses are ‘unhappy’ with the frontier AI labs
https://www.cnbc.com/2026/06/10/palantir-karp-enterprise-ai.html
Key Points
Palantir CEO Alex Karp said enterprises are “unhappy” with the frontier labs and believe they only care about tokenmaxxing.
He told CNBC’s Sara Eisen that most of Anthropic’s publicly discussed projects are “running on Palantir.”
Increasing costs are raising alarm as businesses use more AI in their workloads.
Palantir
CEO Alex Karp said the artificial intelligence software company’s enterprise customers are “unhappy” with how the frontier labs are operating.
“It’s not just the man and woman on the street that is unhappy with the frontier labs, it’s in private, every single enterprise we deal with,” he told CNBC’s Sara Eisen on Wednesday.
Many customers, he said, believe these companies don’t understand their businesses and only care about “tokenmaxxing,” or burning through AI tokens to signal productivity.
Tomi Engdahl says:
Joe Brennan / The Irish Times:
CameraMatics, which uses AI to help fleet operators improve safety, reduce operational risk, and lower carbon emissions, raised €49M
https://www.irishtimes.com/business/2026/06/10/fleet-safety-tech-firm-cameramatics-raises-49m-with-isif-and-aib-backing/
Tomi Engdahl says:
Financial Times:
Sources: Germany’s Neura Robotics, which builds AI-powered humanoid robots, raised $1.4B from Tether, Qualcomm, Amazon, Nvidia, and others at a ~$7B valuation
https://www.ft.com/content/237f10c2-b2b2-490b-bec1-8864e0a22772?syn-25a6b1a6=1
Tomi Engdahl says:
Sarah Guo:
As AI commoditizes benchmarkable work, an organization’s lasting moats lie in tasks that are verifiable through its private data and judgment
The Untrainable
https://saranormous.substack.com/p/the-untrainable
The mid-2026 investor’s version of AI psychosis is a despair that nothing is investable, that we should put all our money into Anthropic and Nvidia and go home. I have never felt it. I have been sure the models are smarter than me for several sub-versions now, I’d be a happy buyer of Anthropic and Nvidia at the market price, and all my smartest friends are quite convinced that self-improvement is soon to work – and I still don’t feel it. The despair isn’t stupid. The logic runs: if the model keeps getting better at everything, then every company built on top of one is a thin wrapper waiting to be absorbed, and the only value that survives is the compute and the frontier weights.
Take software, the case the despair leans on hardest. Devin shipped in 2024 solving thirteen percent of the tasks on the standard software benchmark, and was largely dismissed. A year and a half later the best agents hit the high eighties, and they’re doing real work inside Goldman Sachs and the U.S. Army. Nearly everyone drew the same wrong lesson: the model ate software engineering. But as the model swallowed the part of software engineering you can best measure, we’re relearning what many teams knew – engineering has always resisted measurement, and the most measurable parts may not be the only important ones.
Mert Demirer and coauthors at MIT finally put numbers on it: across more than 100,000 developers, the latest coding agents lifted how much code got written by roughly 180%, and how much actually shipped by about 30%. Writing got cheap. The rest still runs through a person, and it matters. The net impact is, of course, still amazing.
A benchmark is a thing you can measure, and a thing you can measure is a thing you can train against. Thus, coding agents matured first: a compiler is a free verifier, a test suite is a free verifier, and when the answer checks itself for nothing you can grind against the check until you beat it. But passing the test never told you the change was the right one for a decade-old codebase with three undocumented reasons that module exists and a deploy pipeline held together by a cron job no one will admit to writing.
That kind of correctness can’t be read off a leaderboard, and it can’t really be read off anything. You find out whether a system that complex works by running it in the world long enough to learn, and a smarter model doesn’t make the world run faster. Nobody unit-tests something the size of Google and trusts the green check; you trust it because it survived years of real load. Correctness like that isn’t only private, it’s the slow kind of moat capital can’t collapse. Even the optimists grant the clock can’t be skipped: Noam Brown, who has pioneered OpenAI’s reasoning models, wrote recently that the only sure way to evaluate an agent over a one-year horizon may be to run it…for a year.
As Gabe Pereyra says, real automation isn’t only the model getting better. It’s the product, the model, the workflow, and the firm moving together, and three of those four move at the speed of an organization. Moving people is the part no benchmark touches: getting a skeptical partner to change how she runs her matters, holding a team together through a rebuild. It’s why, when we hire a CEO, the ability to deal with people weighs at least as much as the analytical horsepower, and a smarter model doesn’t change that weighting. The feedback is ambiguous, the horizon is years, and the trust belongs to a person. Every company I know has every engineer on frontier coding models, and not one has changed its eng org at anything close to that speed. Adoption took a quarter, and what a magical quarter of token growth it was! But the rebuild is taking years.
Tomi Engdahl says:
Matthias Bastian / The Decoder:
A German court rules that Google is directly liable for what AI Overviews say after AI Overviews falsely tied two publishers to shady business practices
Landmark German ruling declares Google’s AI Overviews are Google’s own words and makes it liable for false answers
https://the-decoder.com/landmark-german-ruling-declares-googles-ai-overviews-are-googles-own-words-and-makes-it-liable-for-false-answers/
Key Points
A German regional court has ruled that Google is directly liable for false claims in its AI-generated search overviews.
In this case, Google’s AI had wrongly linked two publishers to scams and shady business practices.
The court treated the AI overviews as Google’s own content and rejected Google’s argument that users were responsible for fact-checking the results themselves.
Tomi Engdahl says:
Bloomberg:
Dario Amodei says he doesn’t know what role Claude played in a missile strike on an Iranian school, and its use in this instance didn’t violate Anthropic’s ToS
https://www.bloomberg.com/news/articles/2026-06-10/anthropic-ceo-doesn-t-know-if-claude-used-in-iran-school-strike
Tomi Engdahl says:
Moneycontrol:
Opendoor says it is shutting down its India operations and laying off nearly 250 employees, replacing them with smaller, AI-enabled teams in the US
https://www.moneycontrol.com/europe/?url=https://www.moneycontrol.com/technology/opendoor-shuts-india-operations-lays-off-250-employees-and-moves-roles-to-the-us-article-13946645.html
Tomi Engdahl says:
Paige Smith / Bloomberg:
OpenAI and Visa partner to let AI agents make purchases online after users give their permission and to explore enterprise applications for AI-driven payments
https://www.bloomberg.com/news/articles/2026-06-10/openai-visa-team-up-to-let-ai-agents-make-purchases-online
Tomi Engdahl says:
https://bit.ly/4uZgt9X
“The more slop there is, the more valuable, more experimental, or more unique art is.”
At Fortune #BrainstormTech, Grimes argued that while AI music slop is flooding streaming platforms, it may ultimately benefit artists who are creating original work.
“The worse we make the corporate music system, the better it is for art,” she said.
Tomi Engdahl says:
Grimes says AI can make music, but humans must still tell the story
https://fortune.com/2026/06/09/grimes-says-ai-can-make-music-but-humans-must-still-tell-the-story/?fbclid=IwdGRjcASXZO1jbGNrBJdk6GV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHmizFL5xkO6nlspVjeBRG0V1_8ueH2OnnkfaN9ApoYAk45ifNvBDYNjMUFEU_aem_xO2N0u68uLgfW2GRshpqxA
The singer-songwriter Grimes has embraced AI-generated music, but she insists that even as music formats change, humans must still play a central role.
Tomi Engdahl says:
https://etn.fi/index.php/13-news/19047-seuraava-askel-ennakoivassa-kunnossapidossa-on-tekoaelyinsinoeoeri
Koneen laakerivika havaittu. Varaosa tilattu. Huoltoikkuna varattu ensi viikon seisokkiin. Työmääräys luotu SAPiin. Aivan näin automatisoitua kunnossapito ei vielä ole, mutta Rotomaten toimitusjohtaja Mikko Kuusiston mukaan tähän suuntaan ollaan tekoälyn avulla menossa kovaa vauhtia.
Suomalainen Rotomate kehittää teollisuudelle tekoälyinsinööriä, jonka tehtävänä on analysoida tehdasdataa, tunnistaa alkavia vikoja ja ehdottaa korjaavia toimenpiteitä. Vuonna 2024 perustettu yhtiö on kerännyt 2,1 miljoonan euron siemenvaiheen rahoituksen tuotekehityksen ja kansainvälisen kasvun vauhdittamiseen.
Rotomaten mukaan teollisuuden suurin haaste ei enää ole datan puute. Tehtailla on jo runsaasti sensoreita ja vuosien aikana kertynyttä kunnossapitotietoa. Ongelmaksi muodostuu datan tulkinta.
– Teollisuuden käyttöasteessa ja luotettavuuden parantamisessa on valtavasti kohennettavaa lähes jokaisessa teollisuuslaitoksessa ja tehtaassa. Hidasteena ei ole data vaan ihmisen rajallinen huomiokyky, sanoo toimitusjohtaja Mikko Kuusisto.
Kuusiston mukaan markkinoiden yleisimmät ennakoivan kunnossapidon ratkaisut perustuvat edelleen hälytysten tuottamiseen. Kun jokin mitattu suure ylittää asetetun raja-arvon, järjestelmä ilmoittaa asiasta ja jättää varsinaisen analyysin asiantuntijalle.
Tomi Engdahl says:
Application Security
After AI Reaches Production: 12 Ways Security Teams Can Take Control
Security teams need more than visibility into AI applications, they need a repeatable framework for monitoring, investigating, and defending them in production.
https://www.securityweek.com/after-ai-reaches-production-12-ways-security-teams-can-take-control/
Tomi Engdahl says:
Artificial Intelligence
Claude Mythos Turns N-Days Into N-Hours With Rapid Exploit Creation
Public LLM models with safeguards turned off can also build working exploits, increasing patch gap risks.
https://www.securityweek.com/claude-mythos-turns-n-days-into-n-hours-with-rapid-exploit-creation/
Anthropic says its Claude Mythos Preview model can build working exploits targeting known vulnerabilities within hours, or even minutes.
Announced in early April and promoted as the most capable AI frontier model, Mythos right from the start raised fears regarding its ability to supercharge attacks.
In April and May, Anthropic touted its ability to find vulnerabilities, including 271 Firefox flaws and thousands of severe security defects across over 1,000 open source software (OSS) projects.
Now, the company says its most advanced model can also weaponize these discoveries, demonstrating that the surge in AI use in cyberattacks increases the threats faced by organizations in the patch gap.
Put to the test, Claude Mythos Preview delivered 16 working exploits targeting Firefox and Windows within hours.
Anthropic’s public models were also tested, with safeguards off. While they did not rise to Mythos’s level, they too delivered working exploits, proving that LLMs significantly increase the threat posed by N-days that have not been exploited in attacks before.
Tomi Engdahl says:
Artificial Intelligence
Raising the Cybersecurity Stakes: Ante up for the Agentic Era
CISOs are now facing machine-speed attacks and asking, “How do I agent?” The industry must provide remediation at scale.
https://www.securityweek.com/raising-the-cybersecurity-stakes-ante-up-for-the-agentic-era/
Organizations are making a big bet on AI, but if their plans don’t include a cybersecurity strategy, then they are gambling with their future.
Over the past few years, GenAI platforms have matured from pattern-matching large language models (LLMs) to tool-calling agents. Many enterprises now report that the majority of their code is written by AI. However, threat actors have also upped the ante – agentic attacks shape offense faster than human defenses can respond.
In the last decade, the fundamental questions of cybersecurity have evolved. When CISOs asked, “What do I have?”, the industry provided context on assets. When they asked, “What is important?”, the industry provided prioritization. When they asked, “How do I fix it?”, the industry provided remediation.
Now, virtually every cybersecurity solution has implemented conversational AI that can make recommendations, but manual remediation cannot keep pace with AI-powered cyberattacks.
The agentic era is forcing manual remediation processes to evolve rapidly. CISOs are now facing machine-speed attacks and asking, “How do I agent?” The industry must provide remediation at scale.
AI Is the New Perimeter
AI has changed the game in both the scope of the attack surface and the scale of agentic attacks. This attack surface (and the control plane) spans assets, identity, and decision context. Enterprise AI agents and AI-generated code are both sources of risk.
In February 2026, OpenClaw, an agentic assistant, became so popular that its creator was recruited to join OpenAI. Although early adopters of OpenClaw may pose a shadow AI risk in enterprise environments, they also serve as a proof of concept for the agentic enterprise.
But the agentic enterprise is a security nightmare. Connecting AI to everything creates a flat network that runs counter to the principles of network segmentation and isolation that the security industry has advocated for decades.
One risk is that AI agents have the ability to execute tasks and make decisions autonomously, but they lack the discernment to avoid harming themselves or their enterprise.
Many parents have scolded their children by asking, “If everyone jumped off a bridge, would you?” There are numerous examples of AI-induced outages and data leaks that demonstrate AI would jump off a bridge. Therefore, organizations must implement guardrails.
Another risk is that threat actors are targeting AI. Model poisoning can manipulate training data to corrupt the foundational logic of AI models. Evasion of logic attacks bypasses defensive decision-making algorithms. Autonomous systems create blind spots that humans might miss. AI-powered cyberattacks continuously learn from their failed attempts to improve future attacks.
It has been estimated that within the next few years, the ratio of humans to agents will increase to 1:100 (or more). That means the typical large enterprise with 10,000 employees will be contending with a million or more agents – the size of a major metropolitan city.
Organizations should think of managing the agentic enterprise like a major metropolitan city, implementing infrastructure, establishing proactive policies, and governing it with controls.
The Agentic Detection Gap
As bad actors reshape the threat landscape with agentic cyberattacks, the defensive paradigm has yet to adapt. In Armis’ 2026 State of Cyberwarfare Report (PDF), 43% of respondents reported that their organization still detects and responds to significant cyberattacks as they happen or after they have already occurred.
Tomi Engdahl says:
Having A Cry
Palantir, World’s Weepiest Eye of Sauron, Sues Mayor of London After Losing a Contract
“The Met only fully engaged with one potential supplier: Palantir.”
https://futurism.com/future-society/palantir-eye-sauron-sues-mayor-london?fbclid=IwdGRjcASXzC5jbGNrBJfL-WV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHkkj6et26e2KOYpnIyEW1CzTdg-A8CC4AQU1YR1gi4QroQd8JzcYaKr9kz8R_aem_86pjoBR94YB5IB1JqsU-5A
Palantir, the multi-billion dollar AI surveillance company, has been dealt a major blow after London mayor Sadiq Khan blocked a contract with the city’s Metropolitan police force.
Named after the seeing stone used by the villain Sauron, the physical embodiment of a cosmic evil in JRR Tolkien’s “Lord of the Rings” books, Palantir the company is not backing down. According to the Guardian, the surveillance-tech giant has now signaled its intent to sue Khan over his decision to block the contract.
Tomi Engdahl says:
https://www.uniladtech.com/news/ai/anthropic-releases-claude-mythos-public-risks-056101-20260610?utm_content=tech&utm_medium=Social&utm_source=facebook#Echobox=1781105123
Tomi Engdahl says:
New York Times:
How Chinese manufacturers are dominating the humanoid robot supply chain, even as the industry struggles to find a purpose for such robots
Why It’s Nearly Impossible to Build a Robot Without China
https://www.nytimes.com/2026/06/11/business/china-robots-humanoid.html?unlocked_article_code=1.pVA.02VR.HGFQn7T5p-wq&smid=nytcore-ios-share
Building on the country’s electric vehicle industry, Chinese companies are making robot parts at a scale and price point others can’t match.
Japan led the world in robotics for decades.
More than 50 years ago, Japanese researchers captured imaginations with the first robot capable of grasping objects and walking on two legs. In 1984, a team in Japan built one that could read sheet music and play the piano. When Honda unveiled its first humanoid in 2000, it seemed to cement the country’s lead.
But now, just as tech investors, start-up founders and government officials around the world are betting that artificial intelligence will spur growth for robots, that lead no longer belongs to Japan.
It belongs to China.
Last month at the Humanoids Summit, a robotics conference in Tokyo, what could have been a victory lap for an industry built on decades of development and investment instead centered on a different topic: how Japanese companies can break through in a market increasingly dominated by Chinese rivals.
Tomi Engdahl says:
Ivan Mehta / TechCrunch:
Hyderabad, India-based Equal AI, which makes an eponymous AI-powered call screening app, raised a $30M Series B led by Prosus Ventures and Tomales Bay Capital
Equal AI raises $30M to screen calls so Indians don’t have to
https://techcrunch.com/2026/06/11/equal-ai-raises-30m-to-screen-calls-so-indians-dont-have-to/
In India, consumers receive a lot of calls every day, ranging from spam and scams to delivery people and financial service companies trying to contact them. There are apps like Truecaller and the government’s Calling Name Presentation (CNAP) system to identify who is calling, but knowing the name of the caller is often not enough. That is why Equal AI is creating an assistant that can receive calls on your behalf, gather information, and tell you why someone is calling.
The app is currently available on Android, and since its launch last year, it has grown to more than a million monthly active users and over 300,000 daily active users, it says. The app screens the call and displays the reason someone is calling you.
The dialer shows quick reply options like “Leave the delivery near the door” or “Give it to the neighbor,” and the AI reads them back to the caller. You can also type a custom message for the AI to read out. The app records the call, and users can see the recording and transcription history with a summary in the app.
Equal AI said today it has raised $30 million in Series B funding led by Prosus Ventures and Tomales Bay Capital with participation from Think Investments and Valiant Fund. Individual investors include Indian fintech PhonePe’s founder Sameer Nigam, Zubin Bharti Mittal from Airtel Family Office, Skyflow AI co-founder Anshu Sharma, Meta India and Southeast Asia’s VP Sandhya Devanathan, and CtrlS Datacenters’ Chairman Sridhar Pinnapureddy. With the new funding, the company has raised over $42 million to date.
Tomi Engdahl says:
The Information:
How Anthropic is blindsiding business partners by launching potentially competitive products with little warning, changing pricing, and more
https://www.theinformation.com/articles/anthropic-blindsides-business-partners
Tomi Engdahl says:
Julia Fioretti / Bloomberg:
Sources: Shenzhen-based humanoid robot manufacturer EngineAI has filed confidentially for a Hong Kong IPO; the company was last valued at $1.5B in April 2026
https://www.bloomberg.com/news/articles/2026-06-12/humanoid-robot-manufacturer-engineai-is-said-to-file-for-hong-kong-ipo
Tomi Engdahl says:
Bill Toulas / BleepingComputer:
Europol says it has dismantled the AudiA6 crypto mixing service, which allegedly laundered $380M+ for ransomware actors and others between 2022 and 2025 — Law enforcement has dismantled the “AudiA6” cryptocurrency service allegedly used by ransomware actors and other cybercriminals to launder more than $380 million.
Authorities dismantle ‘AudiA6′ ransomware crypto-laundering service
https://www.bleepingcomputer.com/news/legal/authorities-dismantle-audia6-ransomware-crypto-laundering-service/
Tomi Engdahl says:
Ivan Mehta / TechCrunch:
Coinbase launches an AI agent that can execute trades and pay for premium research; users can give it access to their main account or have it operate separately — As AI agent traffic surpasses human traffic on the internet, companies working in commerce and finance are building tools …
Coinbase’s new tool can help agents trade and pay for premium research
https://techcrunch.com/2026/06/11/coinbase-debuts-mcp-for-agent-trading/
Tomi Engdahl says:
Dan Primack / Axios:
Jeff Bezos’ Prometheus, which is building AI models for physical tasks, raised a $12B Series B at a $41B valuation, following a $6.2B Series A — Prometheus, the industrial AI startup led by Jeff Bezos and former Google exec Vik Bajaj, today will announce that it’s raised $12 billion in Series B funding at a $41 billion valuation.
Prometheus, Jeff Bezos’ AI startup, is now worth $41 billion
https://www.axios.com/2026/06/11/prometheus-bezos-industrial-ai
Tomi Engdahl says:
Seth Fiegerman / Bloomberg:
OpenAI acquires Ona, which offers cloud services to support AI agents, and plans to bring Ona’s team into its Codex effort; terms of the deal were not disclosed — OpenAI has agreed to acquire Ona, a startup that offers cloud services to support artificial intelligence agents …
https://www.bloomberg.com/news/articles/2026-06-11/openai-to-acquire-cloud-platform-ona-to-support-ai-agents
Tomi Engdahl says:
@semianalysis_:
Analysis: assuming API pricing, the $200/month Claude Max and ChatGPT Pro plans offer up to ~$8,000/month and ~$14,000/month worth of tokens, respectively — Recently, we purchased one of each Anthropic/OpenAI subscription plan and randomly ran long horizon coding tasks until we exhausted the weekly limit. It’s widely believed that a $200/month plan maxes out at ~$2000/month worth of tokens (assuming API pricing). However, we found [image]
https://x.com/SemiAnalysis_/status/2064815044085318040
Tomi Engdahl says:
Kai Nicol-Schwarz / CNBC:
Anthropic, OpenAI, and other AI startups have announced major London expansions over the past year, as the city emerges as a deep AI talent pool outside the US — A slew of U.S. Big Tech and AI companies are racing to expand in London as they look to take advantage of the city’s deep talent pools amid …
Why U.S. AI giants like Anthropic, OpenAI are launching major expansions in London
https://www.cnbc.com/2026/06/11/anthropic-openai-london-expansions-big-tech.html
Key Points
A number of key AI companies have announced major expansions in London over the the past year, as the sector races to secure key talent and build commercial revenue.
In recent months, both OpenAI and Anthropic said they’d be upping headcount in the U.K. capital.
Access to top technical and commercial talent is a key driver, analysts told CNBC.
Tomi Engdahl says:
Carl Franzen / VentureBeat:
Xiaomi releases MiMo Code V0.1.0, an open-source AI coding assistant that it says outperforms Claude Code on agentic coding and software engineering benchmarks — Xiaomi’s MiMo AI team has open-sourced MiMo Code V0.1., a terminal-native AI coding assistant that the Chinese electronics giant …
https://venturebeat.com/technology/xiaomis-new-open-source-agentic-ai-coding-harness-mimo-code-beats-claude-code-at-ultra-long-200-step-tasks
Tomi Engdahl says:
Anissa Gardizy / The Information:
Sources: Anthropic signed 12+ initial agreements for direct data center leases, a first for the startup, with Google potentially providing a financial guarantee
Anthropic Pursues First Data Center Leases, Seeks Financial Backing From Google
https://www.theinformation.com/articles/anthropic-pursues-first-data-center-leases-seeks-financial-backing-google
Tomi Engdahl says:
Anvee Bhutani / Wall Street Journal:
Amazon says its data centers used ~2.5B gallons of water in 2025, or 0.12 liters of water per kWh, and water use at sites it owns and operates fell 2% from 2024
Amazon Says Its Data Centers Used 2.5 Billion Gallons of Water in 2025
The company said water use at sites it owns and operates directly fell 2% from 2024 levels, even while it expanded its data-center footprint
https://www.wsj.com/tech/amazon-says-its-data-centers-used-2-5-billion-gallons-of-water-in-2025-019e76f9?st=zjz6PE&reflink=desktopwebshare_permalink
Tomi Engdahl says:
Samantha Subin / CNBC:
DoorDash launches Ask DoorDash, an in-app AI chatbot that lets users in select markets order food and groceries and make reservations with photos and prompts
DoorDash lets customers use photos, prompts to order food and book reservations in latest AI push
https://www.cnbc.com/2026/06/11/doordash-ai-ordering-automation.html
Key Points
DoorDash launched a new chatbot that lets users order food and groceries and make reservations with photos and prompts.
It’s a market that’s becoming a major testing ground for agentic AI tools.
DoorDash is in the middle of a massive investment cycle that includes revamping its tech platform after a string of acquisitions.
Tomi Engdahl says:
Alert Fatigue Is Becoming a Security Threat of Its Own
https://www.securityweek.com/alert-fatigue-is-becoming-a-security-threat-of-its-own/
As alert volumes outpace human capacity, organizations are turning to AI, automation, and deeper context to separate real threats from the noise.
Alert fatigue and its related effects on SOC efficiency are self-evident problems. Less obvious and more complex are the cause, effect and possible solutions to these problems.
SOC analysts are inundated with a huge and continuous volume of alerts generated by security tools. Each alert is often meaningless absent correlation with other alerts. But finding relationships is time-consuming, and even if found, might be irrelevant to business security. Much of the alert volume is simply noise, but attempting correlation to find true positive alerts (signals) from the huge number of false positives (noise) is difficult, boring, and often pointless.
The reasons are numerous:
Absence of automated prioritization. Security tools are great at detecting alert signals but poor at prioritizing them. Alerts sometimes arrive with a score. “A tool might say, ‘I found a threat. The score is 32 out of 100’,” comments Obbe Knoop, founder and CEO at Lanxit. “What does that mean? What does a score of 100 out of 100 actually mean? Why give it a score of 32? Without context it is meaningless.”
Absence of alert context. Alerts suffer from a paucity if not complete lack of context. An alert might suggest the presence of a vulnerability and appear to be urgent; but full context might indicate that this device in that location has no outgoing connectivity and zero relevance to business continuity. It can be noted and queued behind more genuinely urgent alerts. It all depends on having accurate and full context to understand relevance.
Jeff Reed, CTO at SentinelOne, summarizes: “Alert fatigue isn’t necessarily the volume of alerts, but rather the relevance of the alerts.”
Criminal use of AI is increasing the pace, sophistication, and stealth of attacks. “Attackers are increasingly using AI to scale their operations – analyzing stolen data faster, generating more convincing phishing campaigns and automating parts of the intrusion process,” adds Reed. The result is continuous growth in the volume of alerts.
Defensive use of AI simultaneously increases the attack surface that bad actors can target. “AI systems themselves are also becoming part of the attack surface, introducing new risks around model manipulation, data exposure and misuse – and yet more alerts,” explains Reed.
“In short,” he adds, “human analysts simply cannot triage and investigate every signal at the pace modern environments produce them.”
Effects
Burnout is not an illness. It is not something that can be cured; it can only be prevented or alleviated. One solution is indeed to change jobs – but then the company loses a highly specialized skill. It is easier to prevent burnout than to alleviate it. This would involve the simultaneous benefit of reducing or preventing alert fatigue.
Alert fatigue isn’t caused by occasional long hours and stress – it is caused by continuous long hours and continuous stress with no escape. If it isn’t prevented, the effect on the analyst could begin with a few missed false negatives and grow into a full business compromise.
For the analyst, it could start with subconscious, but overly aggressive filtering merely designed to keep up with the volume of fresh alerts. Within this filtering, too many alerts may be assumed to be false positives. Many will be but some may not, and true positive signals may be filtered out as noise.
Solutions
There are two obvious approaches to prevent alert fatigue: reduce the number of alerts by formal filtering to improve the signal to noise ratio, or improve the speed and efficiency of triaging through AI-assisted automation. The problem with the former is the potential to throw out true positives with the noise bathwater; while the problem with the latter is that AI is not yet foolproof.
Ariel Parnes, former colonel at IDF 8200 Cyber Unit, and current co-founder and COO at Mitiga, believes the solution to alert fatigue is to increase rather than decrease the alerts, but to more clearly surface and correlate associated alerts for the analysts.
The goal is to reconstruct every action, log, and signal into a unified attack sequence, so analysts aren’t triaging individual events but reading a complete, decoded story of attacker behavior.
“AI-native automation,” he suggests, “can turn alert floods into clear priorities: automating triage and accelerating investigations so the SOC leads every response rather than chasing it.”
“Organizations are moving toward more operationalized models that combine automation, correlation, and continuous monitoring to reduce noise, improve prioritization, and give analysts the space to work both sides of that equation.”
Reed agrees. “Repetitive tasks such as log analysis, enrichment and early-stage investigation can be handled automatically, allowing analysts to focus on understanding attacker behavior and making strategic decisions. When machines handle the heavy data processing,” he adds, “security teams gain the clarity and time they need to respond effectively.”
His solution is to use artificial intelligence to provide automation. “AI is becoming essential for analyzing large volumes of telemetry, correlating signals across multiple environments and identifying the small number of events that actually represent real risk. Rather than presenting analysts with thousands of disconnected alerts, AI can group related activity, add context and prioritize incidents based on likely impact.”
Michael Brown, Field CISO at Presidio, adds, “Analysts should not be working on any raw alerts, only correlated incidents. This enables much faster investigations and remediations while reducing staff burnout and attrition.”
The question is, ‘How should this be done?’ Not all AI systems are created equal. AI only knows what it knows. It doesn’t know what it hasn’t learned – but it may still fabricate a wrong response.
Merlin Gillespie, CTO of Cybanetix, offers one approach. He suggests that using known IoCs as the primary indication of compromise is no longer sufficient. “Over the past few years, attacks have become more subtle. Threat actors now obtain access via stolen credentials and maintain persistence using ‘living off the land’ techniques, which makes detection far more difficult.”
So, agreeing with Parnes, he suggests, “This means we need to collect more alerts, not less, to catch and connect those small signs. Capturing more alerts and adopting a paranoid posture means those attacks can be spotted earlier, but it does of course increase the likelihood of alert fatigue and analyst burnout. It’s for this reason we need to let technology do the heavy lifting.”
The technology he recommends is a combination of machine learning (ML) and large language models (LLMs). “Together, they can be used to carry out 90% of alert triage and investigation. ML can analyze vast sets of data and identify patterns, anomalies and potential breaches. Over time, ML can even make inferences to anticipate attacks and improve detection,” he says.
“LLMs, on the other hand, can explain alerts, investigation findings, and provide case summaries, speeding up investigations and producing intelligible outputs.”
But he also warns there are still problems with AI. “The subjective nature means it is also prone to variance. During a recent experiment, we found an agent not only misinterpreted the threat but produced a fictitious killchain. This illustrates,” he says, “that AI doesn’t yet have the maturity needed.”
The key seems to be context. Everybody accepts that alert context is necessary for accurate correlation and prioritization, but there is little definition over what constitutes and what provides the necessary context.
Valenzuela links it to divergence from normal. “Effective noise reduction requires… understanding which assets are truly at risk and establishing what normal and abnormal look like in their specific environment,” he explains.
“Simply adding more tools without that context tends to increase complexity and volume rather than improve outcomes, creating what many describe as an ‘all noise, no signal’ problem.”
The priority, he adds, “Is to improve signal quality by enriching alerts with context and continuously adapting detection logic to reflect a changing environment, rather than relying on static rules.”
Rob Demain, CEO of e2e-assure, suggests that context can be understood by the analyst after AI has removed the humdrum layer of analysis. “AI removes the repetitive layer of work that consumes so much of an analyst’s day. The result is faster, more consistent first-response times, and a team whose energy is directed where it matters most: understanding context, refining threat intelligence, and making nuanced judgement calls that no automated system can replicate.”
Gillespie believes that context can be surfaced by the LLM part of a dual ML and gen-AI solution. Reed agrees. “AI can group related activity, add context and prioritize incidents based on likely impact.”
Toby Lewis, global head of threat analysis at Darktrace, also concurs. He accepts that extracting context from the noise is humanly difficult. “Building a tech stack that can combine these feeds without a huge amount of human legwork seems like a near impossible task but it’s one that AI makes vastly more plausible. Its ability to combine, correlate and analyze data in real-time creates that single picture.”
Brown provides a more complete description. “Mature SOCs auto-enrich their raw alert data so that analysts start their investigations with the context already assembled. This enrichment might include asset inventory data, asset criticality level, identity privileges, device ownership and physical location, historical behavior analytics, network traffic context, and much more.”
He explains, “Correlation and contextualization is what allows analysts to look at attack chains and not just alerts. Signals from different sources (endpoints, cloud logs, IAM system, network device telemetry, etc.) are linked to create an incident narrative and help analysts understand the bigger picture much faster.”
Full context can help locate the true positive alert within the noise. It can highlight what must be actioned immediately, and what may be queued for later action.
Knoop explains the importance of this context. “You could get an alert indicating a vulnerability on a machine. The vulnerability is scored at 100 out of 100 and is very urgent, so it needs immediate attention. The analyst panics.”
But, adds Knoop, “If you look at the full context, you might find the machine is in a lab somewhere, and isn’t connected to any business information. So, if something does happen to it, the revenue impact – the operational impact – on the business might be zero. But current tool sets don’t reason across context and everything else that’s happening.”
While artificial intelligence is a powerful new tool, it can also be a dangerous tool. AI only knows what it knows. If it doesn’t know the correct answer, it might hallucinate an inaccurate answer to fill the gap. Users of AI, which in our case are overworked and stressed SOC analysts, may not recognize the hallucination.
“AI is used to sift alerts,” warns Knoop, “and is separately used to automate responses. But it does so without full context, and without full context, wrong decisions leading to wrong actions can be made.”
His opinion is that context is vital to understanding and correctly responding to alerts, but that the current approach to context is generally too limited. To get full insight into whether the alert is important or just noise, context needs to be built through knowing everything about the business
This reasoning layer must understand the business in its entirety. So, for equipment, it uses the company’s CMDB. It doesn’t simply know each device, it knows what information is handled by that device, which other devices are connected to it and the potential blast radius of an incident affecting that device.
This new reasoning layer also understands the company’s business sector; it understands what an attacker might be seeking; it understands through threat intelligence what current threats are targeting that sector. It has the potential to understand everything about the company – for example, which departments might be understaffed, and even potential attack areas that are not visible to the current security system.
“It’s a system that can reason in context between all the signals that are currently available – a new layer in security that sits on top of all the current security solutions. It takes input from those security solutions, the signals, and reasons between them,” explains Knoop.
“So, very simply, an alert is generated by a security tool. The reasoning layer picks up that alert and says, ‘Okay, this is an alert about this machine.’ It pulls the information about that machine from the CMDB, from the customer’s asset database. It compares it with the device information, then compares it with the business context. What industry is the customer in? Is it in the financial industry? Is it a manufacturer of cars? Is it a chemical manufacturer? So, what kind of threats have I seen in the world?”
Armed with all the information about the alert and full device and business context, the reasoning layer reasons across everything and provides a natural language response to the analyst. It doesn’t simply give a score; it suggests what action needs to be taken.
“It might respond, ‘this thing in your environment is a threat,” continues Knoop. “’The device has no access to anything else. Monitor it and patch it in the next cycle.’ Or it might respond, ‘This is a threat. You should act now, because it will have financial impact to your business.’”
Knoop’s reasoning layer for finding the signal in the noise and what action should be taken is a work in progress. It is currently a beta in test at various sites.
Tomi Engdahl says:
https://etn.fi/index.php/13-news/19044-tekoaely-auttaa-loeytaemaeaen-rf-mittauksen-virheet
Tomi Engdahl says:
https://etn.fi/index.php/13-news/19053-seuraavaksi-generatiivinen-tekoaely-mullistaa-asiakaspuhelut
– Ennen generatiivista tekoälyä AI-chatit olivat pitkälti päätöspuita, ja puhelut parhaimmillaan päätöspuita tai äänitettyjä viestejä. Nyt keskustelua pystyy käymään luontevasti mihin tahansa määriteltyyn asiaan liittyen, sanoo Sonon toimitusjohtaja Aleksi Löytynoja.
Generatiivinen tekoäly on mullistanut chatbotit muutamassa vuodessa. Nyt sama muutos on siirtymässä puheluihin. Helsingissä viime vuonna perustettu Sono on kerännyt 1,1 miljoonan euron rahoituksen kehittääkseen tekoälyagentteja, jotka vastaavat asiakaspalvelupuheluihin ilman jonotusta.
Tomi Engdahl says:
After AI Reaches Production: 12 Ways Security Teams Can Take Control
Security teams need more than visibility into AI applications, they need a repeatable framework for monitoring, investigating, and defending them in production.
https://www.securityweek.com/after-ai-reaches-production-12-ways-security-teams-can-take-control/
Tomi Engdahl says:
Caught Off Guard: Securing AI After It Hits Production
As enterprises rush AI projects into production, security teams are increasingly being forced into reactive mode.
https://www.securityweek.com/caught-off-guard-securing-ai-after-it-hits-production/
Tomi Engdahl says:
Tekoälyn käyttö töissä voi aiheuttaa ”aivokärähdyksen”
Tekoälyn aiheuttama kuormitus on työsuojelukysymys, sanoo Työterveyslaitoksen tutkimuspäällikkö Virpi Kalakoski.
https://yle.fi/a/74-20226132
Yhä useampi käyttää työssään tekoälyä. Suosituinta se on korkeakoulutettujen keskuudessa, käy ilmi Tilastokeskuksen ja Euroopan komission tuoreesta kyselystä.
Helmikuussa tehdyn kyselyn mukaan 47 prosenttia korkeakoulutetuista käyttää tekoälyä työtehtäviinsä.
Tekoälyn tarkoitus on tehdä työstä helpompaa ja tehokkaampaa, mutta joskus vaikutus voi olla päinvastainen.
Työterveyslaitoksen tutkimuspäällikkö Virpi Kalakosken mukaan tekoälyn käyttö töissä voi kuormittaa mieltä ja heikentää työhyvinvointia, jos tietyt asiat eivät työpaikalla toteudu.
– Työnantajien pitäisi herätä siihen, että tekoäly muuttaa työtä. Tuottavuuskaan ei lisäänny, jos työtä ei saada aidosti sujumaan, vaan ihmiset alkavat voida huonosti.
Ylikuormituksesta voi seurata aivokärähdys
Maaliskuussa yhdysvaltalainen aikakauslehti Harvard Business Review uutisoi lähes 1500 osallistujan kyselytutkimuksesta, jossa ihmiset kertoivat tekoälyn käytön lisäävän kuormitusta töissä. Tätä kutsutaan artikkelissa nimellä AI brain fry.
Kalakoski käyttää termistä suomennosta aivokärähdys. Se on peräisin suomentaja Antero Tiittulalta. Kalakoski tapasi Tiittulan hiljattain sattumalta ja kysyi, mikä voisi toimia AI brain fryn suomenkielisenä vastineena.
Aivokärähdyksessä on kyse psyykkisestä ja kognitiivisesta ylikuormitustilasta, Kalakoski sanoo. Siitä, että tekee liikaa, ylittää oman kantokyvynsä.
Ajatus ei kulje, pää on puuroa, uuvuttaa. Psyykkinen ylikuormitustila oireilee eri tavoin ja pakottaa ottamaan aikalisän.
Toisin kuin burnout, aivokärähdys ei synny ajan myötä, vaan se on ylikuormittumisen välitön seuraus vähän samaan tapaan kuin lihakset väsyvät suuren fyysisen ponnistelun aikana, Kalakoski selittää.
– Emme voi tehdä lihastyötäkään loputtoman pitkiä sarjoja isoilla painoilla.
Tekoälyn valvominen on raskasta ja kuormittaa mieltä
Harvard Business Reviewin kyselytutkimuksessa kuormitus liittyi etenkin tilanteisiin, joissa ihmisen piti seurata samaan aikaan montaa asiaa, kuten valvoa usean eri tekoälyagentin tehtäviä ja reagoida niihin.
– Aiemmasta tutkimuksesta tiedämme, että monitekeminen on ihmiselle hyvin kuormittavaa eikä kovin tehokasta.
Kalakoski puhuu ”päätöskuormasta”. Kun kone laskee, kokoaa ja järjestelee, jää ihmisen tehtäväksi arviointi ja päätösten tekeminen. Onko tekoäly onnistunut tehtävässään vai ei?
Koska tekoäly ei ole aukoton ja tekee välillä virheitä, on ihmisen oltava tarkkana.
Tomi Engdahl says:
“I don’t want to tell you to hang on if you don’t believe it can ever get better.” https://trib.al/krmt5ew
Tomi Engdahl says:
If you can’t survive the AI of today, you’re not ready for the AI of tomorrow
Tomi Engdahl says:
AI Price War
OpenAI Execs Are Panicking
They’re getting desperate.
https://futurism.com/artificial-intelligence/openai-execs-panicking-price-anthropic?fbclid=IwdGRjcASY1UVjbGNrBJjVNWV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHhApS6anDV2F64ZmcduRu4mXGt9q9kk-ptkb7oT-F4yF4iiNaouW2RboCEqf_aem_FkpPTnUprSwIq4ZcDzxEOQ
An AI price war is brewing.
Corporations are reeling after finding that the cost to access powerful AI tools is soaring — despite showing no clear payoff. In one particularly unfortunate incident, according to Axios, the CFO of a company accidentally racked up half a billion dollars in Claude usage fees in a single month.
Put simply, the horrible economics of AI are finally starting to rear their ugly head. Astronomical capital expenditures by AI companies are starting to trickle down to users — and they’re not liking what they’re seeing.
Meanwhile, as the Wall Street Journal reports, executives at OpenAI are pondering whether to kick off a price war with the company’s biggest competitor, Anthropic. By dramatically lowering prices, the company’s reportedly hoping to steal users, while also anticipating similar price cuts by its competitor.
Put simply, pricing is turning into a major headache for AI leaders.
“That went from, at the beginning of this year, an issue that never came up — people were totally happy with the amount they were spending — to all of a sudden, a huge issue,” OpenAI CEO Altman admitted during an event last week.
“I think we’ll have a lot of ways we can help people get more value for less spend,” he added.
Anthropic and OpenAI have been caught in a heated race, with the former making major gains through its enterprise-focused coding tools as of late. Its recent advancements clearly rattled the latter, considering the latest news.
Both companies have confidentially filed for an IPO within the last ten days, raising enormous stakes. However, the fact that both are scaring away new users thanks to soaring prices isn’t exactly a vote of confidence to investors, which could soon force AI executives to rethink their business models.