AI News Archive: August 4, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- Govt proposes easier compliance for foreign firms using Indian data centres
The Taxation and other laws (Amendment) Bill, 2026, introduced in the Lok Sabha on Tuesday, reduces reporting requirements for foreign companies which wish to claim the tax holiday announced in the FY27 Budget. It also ensures that genuine data centre businesses get started and operate with far less friction.
- LexisNexis opens customer innovation lab driven by AI to change the future of legal work
LexisNexis Legal & Professional, a division of RELX plc, today announced the opening of its Customer Innovation Lab in New York City, which will deliver a new model for how legal artificial intelligence gets built. The company said the shift is designed to bring AI directly into the grasp of legal professionals. Instead of building […] The post LexisNexis opens customer innovation lab driven by AI to change the future of legal work appeared first on SiliconANGLE .
- What Happens When AI Clones Your Face? Inside the Terrifying Rise of Digital Doppelgängers
What Happens When AI Clones Your Face? Inside the Terrifying Rise of Digital Doppelgängers PCMag
- Big Cloud™ touts swappable AI models, wants the harness
"You can't be subject to the refusal of one model. There's a lot more design space here…”
Score: 44🌐 MovesAug 4, 2026https://www.thestack.technology/big-cloudtm-touts-swappable-ai-models-wants-the-harness/ - Spotify's higher spending on marketing, AI features to hit profit
Spotify's higher spending on marketing, AI features to hit profit Reuters
Score: 44🌐 MovesAug 4, 2026https://www.reuters.com/business/spotify-forecasts-third-quarter-profit-below-estimates-2026-08-04/ - ‘I bought the tool to save time, but I did more manual work than before’: Pentesters are finding more bugs with AI than they can fix
‘I bought the tool to save time, but I did more manual work than before’: Pentesters are finding more bugs with AI than they can fix IT Pro
- Mixture-of-Kittens: our open-source MoE megakernel for NVL72s
Open-source mixture-of-experts megakernel for NVIDIA L40 GPUs.
- As AI Increases Demands on Memory, Storage Steps Up
Surging AI demands are driving the need for massive datasets and context windows that burst past the confines of system memory. But rising needs aren’t met by simply adding more storage capacity. What’s needed is useful, grounded insights from AI factories and efficient, secure storage architectures that enable those insights. At this week’s Future of […]
- Microsoft Framework to Cut AI Agent Training Costs
The vendor is aiming to capitalize on the growing demand for lower-cost AI services.
Score: 44🌐 MovesAug 4, 2026https://aibusiness.com/agentic-ai/microsoft-framework-cut-ai-agent-training-costs - Rubrik unveils Agent Identity to govern AI agents one tool call at a time
Rubrik Inc. today unveiled Rubrik Agent Identity, a service that governs what artificial intelligence agents are allowed to do by granting access one tool call at a time. The company announced the service at the Black Hat conference in Las Vegas. The service is an expansion of Rubrik Agent Cloud, the agent governance platform the […] The post Rubrik unveils Agent Identity to govern AI agents one tool call at a time appeared first on SiliconANGLE .
Score: 43🌐 MovesAug 4, 2026https://siliconangle.com/2026/08/04/rubrik-unveils-agent-identity-govern-ai-agents-one-tool-call-time/ - What the hell happened with AGI timelines in 2026? – Rob Wiblin
Last October, famed coder Andrej Karpathy called AI agents “slop.” Two months later he completely reversed his view , describing them as “alien tools” that are “rocking the profession.” He was far from alone in his whiplash. Six months ago, host Rob Wiblin recorded a video explaining why so many AI experts had longer timelines to AGI than a year earlier. By the time he clicked publish, another huge vibe shift was well underway. Evidence of AI acceleration has piled up since: Models now complete software engineering tasks that would take human professionals a full day — improving faster than our measurements can even keep up. Anthropic’s revenue is growing at an annualised 8,400%, a trend so steep it would hit the whole world's GDP in 2028 if it continued. AI models are making breakthroughs in famous mathematics puzzles. And according to Anthropic, Claude now writes 80% of their code and is itself a key contributor to making itself smarter. While legitimately impressive, Rob isn’t entirely sold. Going through each point carefully he finds this evidence is less decisive than it looks at first glance. And key gaps remain, such as models struggling with complex, real-world tasks. He tours the odd experiments that remain our best attempts to measure that gap: vending machine simulators, an “AI Village” that organises live events, and a real cafe and shop where AI managers are left to do their best handling staff, suppliers, and government paperwork on their own. Rob argues that the nature of the gap between clean and messy work is one of the four biggest unresolved questions in AGI forecasting. In today's piece he explains that, the three other key disagreements between AGI bulls and bears, the seven big pieces of evidence we've gotten about AGI timelines in 2026, and his updated timelines to AGI. Links to learn more, video, and full transcript: https://80k.info/2026-timelines This episode was written and recorded before OpenAI’s AI agents hacked Hugging Face. You can read about the incident on our Substack . This episode was recorded on July 3, 2026. Chapters: What the hell happened? (00:00) Vibe shift (01:17) Exhibit 1: AI revenue explodes (04:33) Exhibit 2: That METR graph (09:54) Exhibit 3: AI capabilities jump, then flatten out (14:57) Exhibit 4: AI starts to build itself… maybe (17:35) Exhibit 5: AI still struggles to run a business (23:02) Exhibit 6: OpenAI makes a maths breakthrough (33:48) Exhibit 7: inference scaling wasn't as big as believed (38:19) How does that all change timelines? (41:41) Four reasons long timelines are still possible (44:26) It's time to limit dangerous research practices (48:01) Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour Producers: Elizabeth Cox and Nick Stockton Coordination and support: Katy Moore and Lou Moran Camera operator: Dominic Armstrong Music: CORBIT
- U of T prof Sanja Fidler leaves as Nvidia’s VP of AI research
Fidler did not reveal her next move, but said world models are the “next breakthrough” in AI. The post U of T prof Sanja Fidler leaves as Nvidia’s VP of AI research first appeared on BetaKit .
Score: 43🌐 MovesAug 4, 2026https://betakit.com/u-of-t-prof-sanja-fidler-leaves-as-nvidias-vp-of-ai-research/ - When AI Escapes the Sandbox: The New Governance Imperative
Over the past several years, organizations have embraced generative AI to improve productivity, accelerate software development, summarize information, and enhance decision-making. Increasingly, however, AI is evolving beyond generating information to taking action. Agentic AI systems can execute code, invoke tools, interact with enterprise applications, retrieve information, and pursue objectives with limited human intervention. These capabilities […] The post When AI Escapes the Sandbox: The New Governance Imperative appeared first on IDC .
Score: 42🌐 MovesAug 4, 2026https://www.idc.com/resource-center/blog/when-ai-escapes-the-sandbox-the-new-governance-imperative/ - Secure Inference Data Centers
This report describes the deployment of secure inference data centers (SIDCs)—purpose-built facilities designed to protect artificial intelligence models from advanced nation-state adversaries. SIDCs can be built today with proven technologies.
- CHROs say they are skeptical about AI readiness
The executives with the most insight into workforce preparedness are the ones most likely to be cautious about timelines, according to a Protiviti survey.
Score: 42🌐 MovesAug 4, 2026https://www.hrdive.com/news/chros-skeptical-regarding-ai-readiness/826929/ - As the skies get busier, will AI help air traffic controllers keep planes flying safely?
Global air travel is predicted to more than double by 2050, threatening to exceed the capacity of air traffic control officers (atcos) to oversee our skies safely.
- 9to5Mac Daily: August 4, 2026 – The latest Apple vs OpenAI drama
Listen to a recap of the top stories of the day from 9to5Mac . 9to5Mac Daily is available on iTunes and Apple’s Podcasts app , Stitcher , TuneIn , Google Play , or through our dedicated RSS feed for Overcast and other podcast players. Sponsored by Backblaze : Backup you can rely on. Save 20% with code 9to5daily .
- Airlock Digital Unveils Agentic AI Control & Governance to Extend Preventative Endpoint Security
Airlock Digital Unveils Agentic AI Control & Governance to Extend Preventative Endpoint Security DevOps.com
- Forget Caterpillar. There Are Cheaper Ways to Buy the AI Rebound
Forget Caterpillar. There Are Cheaper Ways to Buy the AI Rebound Barron's
Score: 42🌐 MovesAug 4, 2026https://www.barrons.com/articles/forget-caterpillar-there-are-cheaper-ways-to-buy-the-ai-rebound-1aca06ea - As the holidays near, two-thirds of shoppers are using AI
Consumers are looking to AI this holiday season to sift through products and search for bargains, according to Attentive.
Score: 42🌐 MovesAug 4, 2026https://www.retaildive.com/news/holiday-shoppers-using-ai-gift-purchases/826939/ - Bolt brings ChatGPT ride requests to SA
Bolt adds conversational AI to ride-hailing, with a ChatGPT integration now available to South African customers.
Score: 42🌐 MovesAug 4, 2026https://www.itweb.co.za/article/bolt-brings-chatgpt-ride-requests-to-sa/4r1ly7R9a2Nvpmda - How AI is reshaping insurance: Inside ICICI Lombard’s technology-first transformation
Artificial intelligence is rapidly moving from pilot projects to the core of insurance operations. From underwriting and customer engagement to claims servicing and product personalization, insurers are leveraging AI and […] The post How AI is reshaping insurance: Inside ICICI Lombard’s technology-first transformation appeared first on Express Computer .
- Britain has an AI minister – now it needs an AI answer
The appointment of Kanishka Narayan, Britain’s first Cabinet-level minister for AI is an acknowledgement that this technology is the central economic question of the age, but the minister’s real test is whether he continues to build durable homegrown capability or instead subsidises dependency on non-European platforms, says Stefano Pasquali Kanishka Narayan’s appointment as Britain’s first [...]
Score: 42🌐 MovesAug 4, 2026https://www.cityam.com/britain-has-an-ai-minister-now-it-needs-an-ai-answer/ - The real reasons to be terrified about AI and WMD
The real reasons to be terrified about AI and WMD The Japan Times
Score: 42🌐 MovesAug 4, 2026https://www.japantimes.co.jp/commentary/2026/08/04/world/be-terrified-about-ai-wmd/ - SnapLogic introduces agentic assistant for data integration
SnapLogic introduces agentic assistant for data integration InfoWorld
Score: 42🌐 MovesAug 4, 2026https://www.infoworld.com/article/4205287/snaplogic-introduces-agentic-assistant-for-data-integration.html - Alibaba's Qwen beats GPT, Claude
Alibaba's new Qwen model outperforms GPT and Claude in benchmarks.
- Genspark Open Sources GenOffice: A Free, Ad-Free AI Office Suite for macOS and Windows with Docs, Sheets, Slides, PDF
Genspark Open Sources GenOffice: A Free, Ad-Free AI Office Suite for macOS and Windows with Docs, Sheets, Slides, PDF MarkTechPost
- What is V2V, India's connected vehicle system to make roads safer from 2028
MoRTH has released draft rules proposing mandatory V2V communication systems in new vehicles from October 2028. Here's how the technology works and where it fits into India's broader V2X roadmap
- Why Hugging Face thinks China is winning the open AI race
Why Hugging Face thinks China is winning the open AI race YourStory.com
Score: 42🌐 MovesAug 4, 2026https://yourstory.com/ai-story/china-open-ai-strategy-hugging-face-warning - The AI race isn’t about models, it’s about infrastructure—and the U.S. is still far ahead
The AI race isn’t about models, it’s about infrastructure—and the U.S. is still far ahead Fortune
Score: 42🌐 MovesAug 4, 2026https://fortune.com/2026/08/04/ai-race-is-about-infrastructure-not-models-us-far-ahead/ - AI agents get better at IT ops, but only with humans in the loop
AI agents are performing roughly 1 in 3 actions in enterprise IT workflows (but that share is rising quickly), while human analysts are rejecting about one-quarter of AI-proposed actions (but that rate is falling), according to a new study of tens of thousands of human-AI interactions. Operational data, rather than underlying AI infrastructures, is often the culprit when things go wrong. Human analysts are approving the most consequential actions, managing exceptions, and supervising and shaping agentic systems, while AI agents are carrying out routine tasks and executions, automation platform provider Fixify found in the study . “That may sound less dramatic than replacing the help desk,” Matt Peters , Fixify’s co-founder and CEO, wrote in a blog post . “It’s also a much more credible path to changing how IT work gets done.” Building scaffolding Fixify identified four steps of agentic work: Planning, proposing, approving or declining, then acting on approved steps. It analyzed nearly 18,000 plans and over 147,000 actions executed by agents across 40 companies over a three-month period, finding that agents are taking over one-third of IT actions, most notably in software, applications, security, and collaboration work where requests tend to be “repeatable and easy to reverse.” Tasks that are well understood and that present low risk are best suited for the current generation of agents, Peters wrote. Human analysts remain closely involved in higher-stakes areas like identity verification, setting up and removing IT access (onboarding and offboarding), and hardware environments. However, AI’s share of the work is increasing as feedback loops improve: Over the three-month period, human approval of AI-proposed actions rose from 23% to 41%, and rejection fell from 27% to 16%, Fixify found. The company identified six types of actions in AI automation. Running a skill — actually doing something — accounted for 39.4% of all actions). Most of the rest were coordination: sending a message to the human requester (27.7% of actions), leaving an initial comment (13.2%), giving instructions to a human analyst (9.8%), or waiting (8.8%). Running entire workflows accounted for just 1.1% of actions. AI is building “scaffolding” that wraps around meaningful changes, often planning far more scenarios than the agent will execute. Typically, agents map out 15 possible actions but run only two, Fixify said. “The agent maps the paths a request could take, then walks down the path that makes the most sense as it meets reality,” the study said. Peters pointed to one example where an AI agent identified which team needed access to process a high-volume type of ticket. Rather than fully automating the process, the agent did the initial triage, asked questions, then routed tickets to the team that had the information to act immediately. “We didn’t need a world-ending hive mind,” he said. “We just needed to point a little conversational intelligence in the right direction.” When AI breaks down IT automation typically involves analyzing tickets and moving them along; in other words, low-risk tasks. But agents do participate in areas like security (albeit only about 6%), most notably adding and removing people from groups or channels, unlocking accounts, resetting passwords, analyzing multi-factor authentication (MFA), provisioning (or deprovisioning) accounts, and assigning software licenses. However, this identity-lifecycle work is where agents failed the most, particularly in onboarding and offboarding and identity-access management (IAM), the study found. “Hardware and connectivity changes rarely fail; identity-lifecycle changes fail three-to-nine times as often.” Why AI breaks down Thanks to human-in-the-loop controls, Fixify was able to analyze scenarios where agent recommendation diverged from human judgment. This occurred about 23% of the time. The largest failure category (nearly 50%) was ‘target not found,’ meaning the agent couldn’t uncover what it needed. This typically comes down to poor data: A user, group, account, or resource was not where the system expected it to be. When people change teams, groups are restructured, accounts are renamed, or work has already been done but not reflected in the system, this is more of an identity hygiene problem than an AI problem. The system needs cleaner and more current data. Invalid inputs accounted for around 29% of failures, followed by unhandled errors, denied permissions, or invalid operations or configurations. The latter signal “real breakage” in integrations, according to Fixify. AI becomes more sophisticated over time The good news is that AI automation improves over time, even if it might take a while. In hybrid systems , humans keep the most consequential changes under their own control, and iterative rejection and approval helps AI learn. Over time, agents’ plans get leaner and they start to re-plan when conditions change, rather than pre-planning all kinds of scenarios that may never occur. “That’s a sign of sophistication,” the study said. “Adapting in the moment is a more advanced behavior than trying to pre-script every contingency.” In turn, humans second guess the system less often and feel comfortable handing off more work. Instead, they control how agents behave, make high-impact decisions, and handle exceptions. “The hardest requests remain human-heavy, especially those that require repeated replanning or contextual judgment,” the study said. How teams can adapt to AI agents As agentic AI becomes embedded in more workflows — and at deeper levels — enterprises must evolve to accommodate, Fixify emphasized. This means investing in clean identity data and building strong playbooks, review workflows, and reliable integrations. Teams should judge agentic tools by their supervision loop and view rejections as a training process, Fixify advised. Analyst time, queues, and metrics should be built around reviewing proposals. Agent replanning can be seen as a routing signal: A single replan might indicate healthy adaptation, while repeated replanning means ambiguity, irrelevance, or unclear policies. “Make the review surface easy to understand so analysts can assess proposed actions and make quick decisions about how to proceed,” the study advised. “This is where the analyst’s attention belongs.” This article first appeared on Computerworld .
Score: 42🌐 MovesAug 4, 2026https://www.cio.com/article/4205069/ai-agents-get-better-at-it-ops-but-only-with-humans-in-the-loop-2.html - Students Create National Framework for AI in Schools
Nearly 100 high school students from all 50 states met in Washington, D.C., last month to draft what they think a national policy for AI in K-12 education should look like.
Score: 42🌐 MovesAug 4, 2026https://www.govtech.com/education/k-12/students-create-national-framework-for-ai-in-schools - Emergency management’s AI challenge isn’t technology — it’s implementation, new reports say
New reports from the nonprofit Markle Foundation's AI for Disasters and Emergencies Initiative found that AI holds great benefit for emergency management agencies, if they can clear hurdles of governance, procurement and skills shortages.
- Ethyca launches Astralis to govern enterprise AI agents in real time
Data privacy engineering company Ethyca Inc. today launched Astralis, a platform that governs how enterprise artificial intelligence models and agents use company data in real time. The company is pitching the product as an answer to a widening gap between how fast enterprises are deploying AI agents and how well their compliance teams can keep […] The post Ethyca launches Astralis to govern enterprise AI agents in real time appeared first on SiliconANGLE .
Score: 41🌐 MovesAug 4, 2026https://siliconangle.com/2026/08/04/ethyca-launches-astralis-govern-enterprise-ai-agents-real-time/ - AI and the Future of Emergency Management
In this report, the authors present the results of a landscape assessment of artificial intelligence–enabled products that are available to support the work of emergency management across the United States.
- Aurora Innovation partners with Arrow McLaren IndyCar Team in first major marketing push
The Strip District-headquartered company recently began rolling out its second-generation autonomous trucks in Texas. The multi-year deal is the company's first major marketing push.
Score: 40🌐 MovesAug 4, 2026https://www.bizjournals.com/pittsburgh/news/2026/08/04/aurora-indycar-partnership.html?ana=brss_6150 - People prefer stories written by AI—especially when told they're written by a human
People gave the highest ratings to AI-generated stories they were told had been written by humans and were unable to tell the difference between human- and AI-created stories, according to new research published in Judgement and Decision Making. The study, led by researchers at Villanova University, U.S., adds to the growing body of evidence that many people's ability to spot AI-generated content is limited.
- Safe Pro Group Wins New U.S. Government Subcontract for Patented AI Threat Mapping and Drone Package
Safe Pro Group Wins New U.S. Government Subcontract for Patented AI Threat Mapping and Drone Package USA Today
- Bring It On: AI Strategy Sways Underwriter Choices of Employers
Property/casualty insurance underwriters are worried about AI tools taking over their jobs. Now, according to a recent survey, nearly three-quarters say they’re opting for companies with clear AI strategies as employers. The result shows up in the report “The Future …
- ‘Not healthy’ LLM use is more common than you think
Hank Green, a popular YouTuber and science communicator, said he is stepping back from production amid intense criticism over his use of AI. Green described his AI usage as "not healthy," but stressed that he used it for finding research sources and not to write scripts. Much of the ensuing firestorm in this corner of […]
Score: 40🌐 MovesAug 4, 2026https://www.theverge.com/ai-artificial-intelligence/975180/llm-ai-chatbot-use-not-healthy - Y Combinator Open-Sources QM: An MIT-Licensed Multiplayer Agent Harness That Runs In Slack And The Web
Y Combinator Open-Sources QM: An MIT-Licensed Multiplayer Agent Harness That Runs In Slack And The Web MarkTechPost
Score: 40🌐 MovesAug 4, 2026https://www.marktechpost.com/2026/08/03/y-combinator-open-sources-qm-multiplayer-ai-agent-harness/ - Why Governing World Models Is AI's Next Big Policy Challenge
As artificial intelligence moves beyond language into the physical world through "world models," Stanford researchers warn that policymakers face an even steeper governance challenge than with large l
Score: 40🌐 MovesAug 4, 2026https://hai.stanford.edu/news/why-governing-world-models-is-ais-next-big-policy-challenge - Kerala unveils AI-led governance overhaul to streamline administration, speed up public services
Kerala's government is launching administrative reforms to modernize bureaucracy. AI-assisted governance and digital public service delivery are key components. Faster land acquisition and deregulation are also being implemented. New digital platforms will manage public assets and infrastructure planning. These changes aim to improve ease of doing business and living.
- Assessing Sovereign AI: A Two-Pronged Framework
This piece offers a two-pronged framework for assessing sovereign AI based on why states pursue it and how they do so. Five country case studies trace the distinct pathways the United States, China, France, India, and Singapore have each taken toward sovereign AI. The post Assessing Sovereign AI: A Two-Pronged Framework appeared first on Center for Security and Emerging Technology .
Score: 40🌐 MovesAug 4, 2026https://cset.georgetown.edu/article/assessing-sovereign-ai-a-two-pronged-framework/ - Bengaluru, INDIA: NANO 2026 focuses on AI, semiconductors and the commercialisation of nanotechnology
The 14th edition of Bengaluru INDIA NANO, India’s flagship international nanotechnology conference and exhibition, was inaugurated in Bengaluru, bringing together scientists, researchers, industry leaders, startups, policymakers and students to accelerate […] The post Bengaluru, INDIA: NANO 2026 focuses on AI, semiconductors and the commercialisation of nanotechnology appeared first on Express Computer .
- Superblocks, AWS sign multi-year deal for enterprise AI on Bedrock
Superblocks and AWS have begun a multi-year collaboration aimed at broadening secure generative AI application development options.
Score: 40🌐 MovesAug 4, 2026https://www.techmonitor.ai/news/superblocks-aws-sign-multi-year-deal-for-enterprise-ai-on-bedrock - Automotive Cybersecurity: AI Attack Surfaces Grow
AI and software-defined cars turn APIs, servers, and chargers into hacker playgrounds; see why automakers must harden fleets now. The post Automotive Cybersecurity: AI Attack Surfaces Grow appeared first on EE Times .
Score: 40🌐 MovesAug 4, 2026https://www.eetimes.com/automotive-cybersecurity-ai-attack-surfaces-grow/ - For creators like Hank Green, AI is now a reputational risk
Hank Green's AI apology has sparked a wider conversation, as creators discover that generative AI can be a risk to their reputation.
- AI startup Kily secures Rs 30 crore from Sorin Investments, Razorpay, Wyser
The AI startup said that it plans to use the fresh capital to deepen Kily's product capabilities, strengthen its go-to market initiatives, and accelerate adoption across India's largest consumer brands. The company said its AI agents help brands manage pricing, advertising, inventory and marketplace operations by analysing real-time marketplace data and autonomously executing decisions.
- Surviving AI: Navigating workload creep, AI slop, and the new tech career playbook
After more than 30 years the cybersecurity field, Keith Jones recently realized that his role had changed, from being a single contributor to manager of a fairly large team. And this team was getting a lot accomplished — tasks that used to take up a huge chunk of his workday. No, his company hadn’t hired a group of new employees to work under him. He simply accelerated his use of artificial intelligence tools. Now, instead of grinding through a lot of basic tasks, that work is done for him while he focuses on bigger-picture work. “It really feels like I have a team behind the scenes, but what I have is Claude [Anthropic’s AI tool],” says Jones, who currently works as a cybersecurity researcher. “I’ve been thinking for the last several months about how much differently I work now than I did a year ago, when I would sit and write all the low-level stuff before I could get to the 10% of the good stuff I really wanted to focus on. Now I can sit back and say, ‘Give me three different ways to solve this problem.’” width="1024" height="678" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Keith Jones, cybersecurity researcher Keith Jones Most people working in the technology field, like Jones, have had to figure out how best to work with AI. The technology has come on strong, with many companies making its use mandatory and actively evaluating whether employees are faster and more efficient because of it. And while it is boosting productivity and taking over the burden of repetitive, manual tasks, it’s also creating a new level of stress and a dizzying kind of mental exhaustion. So what can tech workers do about the heavier mental load that comes with using AI, on top of escalating worries about the safety of their own jobs? AI users and industry analysts say there are specific ways to ease some of those burdens and prepare for a changing job market. Combating the slop factor When it comes to working with LLM tools, a well-known issue is dealing with AI workslop and hallucinations . The slop is AI-generated output that is low-quality, buzzword-heavy, and generic. It also can refer to bloated, boilerplate code. Hallucinations are inaccurate or completely made-up results. AI routinely offers this messy or incorrect information with total confidence, giving users a false sense of security. Using this bad data can lead to anything from minor software bugs to severe liabilities. “Don’t believe the machine is infallible,” says Craig Shue , computer science professor and department head at Worcester Polytechnic Institute (WPI). “That’s when bugs will start working in. There’s a lot of misinformation on the internet, and that could be what the LLM is grabbing and using.” width="1024" height="674" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Craig Shue, computer science professor and department head at Worcester Polytechnic Institute WPI Here are ways to combat the problem: Make AI show its work: Ask it to cite its sources or explain its reasoning. Example prompt: Explain the logic and show the steps before writing the code. Give the AI a source of truth: Instead of letting the AI search the internet for information, give it the exact source material (reports, transcripts, data sheets) to base its output on, telling it to use only the information provided. Validate. Validate. Validate: Never let AI publish code directly to the main project without first reviewing and running it locally. Similarly, never take AI output and simply move it on to the next person in the project. Every single AI output needs review. Don’t be fooled by confidence: As with managing a human, question the output. When you’re busy or tired, it’s easy to just go with the results it gives you. Don’t. Always analyze and question it. Taking on AI-driven workload creep Let’s face it: The great promise of AI is that it will take over repetitive, manual tasks, which will save you an incredible amount of time. What isn’t talked about as much is that it also can create a new workload — one that can be exhausting in a whole new way. “Is AI saving people time? The short answer is yes,” says J.P. Gownder , vice president and principal analyst with Forrester Research. “But people also are being overwhelmed with overproduced things. Everyone wants to look busy and they’re producing more, but not necessarily better. Managers have to push back on that or it’s not really saving you time.” In a multi-year study by Upwork , the largest online freelance marketplace, 77% of employees reported that AI had increased their workload. The report noted that a boost in productivity comes with a “significant emotional and relational cost,” with 88% of workers who saw the highest productivity gains also feeling burned out. And IDC’s Future of Work 2026 survey reported that 24% of IT workers report increased workload as a top AI concern. Here are ways to combat the problem: Keep it short: Part of the prompt — always — should be to tell any AI tool to be concise. Build an anti-slop culture: Don’t simply accept and pass on workslop. It’s insulting for a co-worker to have to deal with pages of largely useless information. Filter the noise: When everyone on a team starts using AI, the volume of Slack messages, long-form memos, and data reports skyrockets. Don’t treat it all with equal importance. Practice radical prioritization. Manage the transcript deluge: Stop wading through 40-page meeting transcripts that bury action items. Instead, prompt the system to produce a concise summary focused strictly on deliverables or status updates. Reduce the AI blast radius: When asked to fix a bug, AI tools often rewrite hundreds of lines of unrelated code, multiplying your code review time. Prevent this by instructing the tool to isolate its changes only to the specific function or file in question. Managing the AI mental tax Using AI often necessitates a different kind of mental processing, changing what had been the natural pacing of your day and dramatically increasing context switching. Instead of simply building and testing, someone might be jumping back and forth between auditing, fact-checking, prompting, and re-prompting. To manage strain and protect your focus, new strategies are needed. Published this past March in the Harvard Business Review, a study by Boston Consulting Group and the University of California, Riverside, surveyed 1,500 workers and coined the term “AI brain fry.” The researchers found that juggling multiple AI tools causes decision fatigue and increases errors. How to combat the problem: Work in batches: Continuously reviewing AI output as it comes in can quickly lead to mental burnout. Dedicate blocks of time throughout your day to interact with your AI tools. Create analog islands: Your brain needs time to decompress, and that means taking a break from digital processing… and from screen time in general. Make time in your day to step away from screens, such as taking a 15-minute walk or reading a book instead of watching a video online. Know when to step in: Instead of endlessly tweaking prompts to get a perfect result, it is often faster and less mentally taxing to manually write or refactor the final 20% yourself. Create base prompts: Writing custom prompts that include everything from guardrails to tone instructions for every new project quickly drains your mental energy. Instead, build two or three reusable system prompts, such as one for refactoring legacy code and another for drafting API docs, and use them as templates. Proving your human value in a new job market With companies regularly using AI-based applicant tracking systems to filter resumes, and AI actively shifting job responsibilities and skills requirements, the strategy for how you apply for roles and handle interviews is changing. Leo Freitas , an analyst and research manager at IDC Research, says it’s critical for job applicants to show potential employers what they can do that machines cannot. “You need demonstrable achievements,” he adds. “It’s good to show highly human skills.” width="1024" height="683" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Leo Freitas, analyst and research manager at IDC IDC How to combat the problem: For your resume: Mirror their language: Use the exact language from the job description. Traditional applicant tracking systems rely heavily on structured information and keyword matching, says Teresa Hill , founder and leader of Anchor GTM, a growth marketing consultancy. That means if a posting says “product marketing manager,” don’t just write “PMM.” Write both. Keep it simple: Use standard section headers, like “Experience” and “Education.” Creative alternatives and formatting can confuse parsers. Avoid AI writing: Use AI to help structure your thinking, then edit until the copy sounds natural and authentically human. Swap responsibilities for metrics: To stand out, especially with AI scanners, tie as many bullet points as possible from your work to business outcomes. For the interview: So, how do you use AI? Every interviewer eventually asks some version of this question. The best answers focus on judgment vs. simply name-dropping tools. Explain what you automate, what you never automate, and why. Show that you’re a gatekeeper: AI tools can generate code or copy instantly, but they also introduce errors. To stand out, show that you know how to audit, verify, and safeguard quality. Focus on learning: Don’t emphasize your expertise with a specific AI tool. Focus on being someone who is adaptable and learns quickly, Hill says. What sets you apart from a machine: Show, don’t tell. Give work examples that demonstrate that you know how to be creative, collaborative, and problem solve. Show metrics whenever possible. Demonstrate that you know the limits: Make it clear that you know when to use AI and what not to let it touch without heavy review. width="1024" height="576" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Teresa Hill, founder and leader of Anchor GTM Teresa Hill Future-proofing your career in a shifting tech market The anxiety echoing through the tech industry is tangible as companies reallocate corporate capital toward automation. While both Gownder and Freitas emphasize that there is far more fear than actual AI-driven layoffs, the shift in corporate spending is undeniably stoking job insecurity. “There’s this apocalyptical view that AI will take everyone’s job in a few years,” says Freitas. “I don’t see that happening, but many things will change in the nature of how we work. I don’t think the world is going to end tomorrow. But it’s always good to do a self-assessment and look at whether AI can do what you’re doing now.” How to combat the problem: Use this technology to your advantage: Approach AI as a new tool, not a replacement. Use it to make yourself better and faster at your job. “I look at AI like it’s another new tool, and I’m going to learn it like I’ve learned any other tool,” says Jones. Don’t bury your head in the sand: Take a look at what you do and consider if it could be done by AI.If your role is highly automatable, think about switching to a more advanced position or to a role, like security, that more clearly needs a human in the loop. Take ownership of your career: Expand your knowledge and skills. Find courses and certifications (many are free online) and take advantage of employers’ training programs. Work with the business side: Make sure you understand the business — its long-term goals, competitive market, and jargon. Be the bridge between the technical and business sides of the company, giving presentations and solving business problems with technology. Highlight your in-house expertise: When you have institutional knowledge, make sure you are openly using it to benefit the business. Your knowledge can be your key differentiator. Push the business forward: Think about the next app or customer-facing system that will propel the business forward. Be the one who is advancing the company with tech. Continue to adapt: Don’t get stuck in your anxiety. Keep being curious and working your learning muscles. More on AI in the workplace: Burned out by bots: The rise of prompt fatigue in the workplace The AI tech job slaughter gets real Increased AI expectations without guidance leads to employee burnout ‘Botsitting’: The AI time-savings killer only governance can stop Here are the top AI certifications that will get you hired and promoted How to curb hallucinations in Copilot (and other genAI tools)