AI is developing all the time. Here are some picks from several articles what is expected to happen in AI and around it in 2025. Here are picks from various articles, the texts are picks from the article edited and in some cases translated for clarity.
AI in 2025: Five Defining Themes
https://news.sap.com/2025/01/ai-in-2025-defining-themes/
Artificial intelligence (AI) is accelerating at an astonishing pace, quickly moving from emerging technologies to impacting how businesses run. From building AI agents to interacting with technology in ways that feel more like a natural conversation, AI technologies are poised to transform how we work.
But what exactly lies ahead?
1. Agentic AI: Goodbye Agent Washing, Welcome Multi-Agent Systems
AI agents are currently in their infancy. While many software vendors are releasing and labeling the first “AI agents” based on simple conversational document search, advanced AI agents that will be able to plan, reason, use tools, collaborate with humans and other agents, and iteratively reflect on progress until they achieve their objective are on the horizon. The year 2025 will see them rapidly evolve and act more autonomously. More specifically, 2025 will see AI agents deployed more readily “under the hood,” driving complex agentic workflows.
In short, AI will handle mundane, high-volume tasks while the value of human judgement, creativity, and quality outcomes will increase.
2. Models: No Context, No Value
Large language models (LLMs) will continue to become a commodity for vanilla generative AI tasks, a trend that has already started. LLMs are drawing on an increasingly tapped pool of public data scraped from the internet. This will only worsen, and companies must learn to adapt their models to unique, content-rich data sources.
We will also see a greater variety of foundation models that fulfill different purposes. Take, for example, physics-informed neural networks (PINNs), which generate outcomes based on predictions grounded in physical reality or robotics. PINNs are set to gain more importance in the job market because they will enable autonomous robots to navigate and execute tasks in the real world.
Models will increasingly become more multimodal, meaning an AI system can process information from various input types.
3. Adoption: From Buzz to Business
While 2024 was all about introducing AI use cases and their value for organizations and individuals alike, 2025 will see the industry’s unprecedented adoption of AI specifically for businesses. More people will understand when and how to use AI, and the technology will mature to the point where it can deal with critical business issues such as managing multi-national complexities. Many companies will also gain practical experience working for the first time through issues like AI-specific legal and data privacy terms (compared to when companies started moving to the cloud 10 years ago), building the foundation for applying the technology to business processes.
4. User Experience: AI Is Becoming the New UI
AI’s next frontier is seamlessly unifying people, data, and processes to amplify business outcomes. In 2025, we will see increased adoption of AI across the workforce as people discover the benefits of humans plus AI.
This means disrupting the classical user experience from system-led interactions to intent-based, people-led conversations with AI acting in the background. AI copilots will become the new UI for engaging with a system, making software more accessible and easier for people. AI won’t be limited to one app; it might even replace them one day. With AI, frontend, backend, browser, and apps are blurring. This is like giving your AI “arms, legs, and eyes.”
5. Regulation: Innovate, Then Regulate
It’s fair to say that governments worldwide are struggling to keep pace with the rapid advancements in AI technology and to develop meaningful regulatory frameworks that set appropriate guardrails for AI without compromising innovation.
12 AI predictions for 2025
This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.
https://www.cio.com/article/3630070/12-ai-predictions-for-2025.html
This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.
1. Small language models and edge computing
Most of the attention this year and last has been on the big language models — specifically on ChatGPT in its various permutations, as well as competitors like Anthropic’s Claude and Meta’s Llama models. But for many business use cases, LLMs are overkill and are too expensive, and too slow, for practical use.
“Looking ahead to 2025, I expect small language models, specifically custom models, to become a more common solution for many businesses,”
2. AI will approach human reasoning ability
In mid-September, OpenAI released a new series of models that thinks through problems much like a person would, it claims. The company says it can achieve PhD-level performance in challenging benchmark tests in physics, chemistry, and biology. For example, the previous best model, GPT-4o, could only solve 13% of the problems on the International Mathematics Olympiad, while the new reasoning model solved 83%.
If AI can reason better, then it will make it possible for AI agents to understand our intent, translate that into a series of steps, and do things on our behalf, says Gartner analyst Arun Chandrasekaran. “Reasoning also helps us use AI as more of a decision support system,”
3. Massive growth in proven use cases
This year, we’ve seen some use cases proven to have ROI, says Monteiro. In 2025, those use cases will see massive adoption, especially if the AI technology is integrated into the software platforms that companies are already using, making it very simple to adopt.
“The fields of customer service, marketing, and customer development are going to see massive adoption,”
4. The evolution of agile development
The agile manifesto was released in 2001 and, since then, the development philosophy has steadily gained over the previous waterfall style of software development.
“For the last 15 years or so, it’s been the de-facto standard for how modern software development works,”
5. Increased regulation
At the end of September, California governor Gavin Newsom signed a law requiring gen AI developers to disclose the data they used to train their systems, which applies to developers who make gen AI systems publicly available to Californians. Developers must comply by the start of 2026.
There are also regulations about the use of deep fakes, facial recognition, and more. The most comprehensive law, the EU’s AI Act, which went into effect last summer, is also something that companies will have to comply with starting in mid-2026, so, again, 2025 is the year when they will need to get ready.
6. AI will become accessible and ubiquitous
With gen AI, people are still at the stage of trying to figure out what gen AI is, how it works, and how to use it.
“There’s going to be a lot less of that,” he says. But gen AI will become ubiquitous and seamlessly woven into workflows, the way the internet is today.
7. Agents will begin replacing services
Software has evolved from big, monolithic systems running on mainframes, to desktop apps, to distributed, service-based architectures, web applications, and mobile apps. Now, it will evolve again, says Malhotra. “Agents are the next phase,” he says. Agents can be more loosely coupled than services, making these architectures more flexible, resilient and smart. And that will bring with it a completely new stack of tools and development processes.
8. The rise of agentic assistants
In addition to agents replacing software components, we’ll also see the rise of agentic assistants, adds Malhotra. Take for example that task of keeping up with regulations.
Today, consultants get continuing education to stay abreast of new laws, or reach out to colleagues who are already experts in them. It takes time for the new knowledge to disseminate and be fully absorbed by employees.
“But an AI agent can be instantly updated to ensure that all our work is compliant with the new laws,” says Malhotra. “This isn’t science fiction.”
9. Multi-agent systems
Sure, AI agents are interesting. But things are going to get really interesting when agents start talking to each other, says Babak Hodjat, CTO of AI at Cognizant. It won’t happen overnight, of course, and companies will need to be careful that these agentic systems don’t go off the rails.
Companies such as Sailes and Salesforce are already developing multi-agent workflows.
10. Multi-modal AI
Humans and the companies we build are multi-modal. We read and write text, we speak and listen, we see and we draw. And we do all these things through time, so we understand that some things come before other things. Today’s AI models are, for the most part, fragmentary. One can create images, another can only handle text, and some recent ones can understand or produce video.
11. Multi-model routing
Not to be confused with multi-modal AI, multi-modal routing is when companies use more than one LLM to power their gen AI applications. Different AI models are better at different things, and some are cheaper than others, or have lower latency. And then there’s the matter of having all your eggs in one basket.
“A number of CIOs I’ve spoken with recently are thinking about the old ERP days of vendor lock,” says Brett Barton, global AI practice leader at Unisys. “And it’s top of mind for many as they look at their application portfolio, specifically as it relates to cloud and AI capabilities.”
Diversifying away from using just a single model for all use cases means a company is less dependent on any one provider and can be more flexible as circumstances change.
12. Mass customization of enterprise software
Today, only the largest companies, with the deepest pockets, get to have custom software developed specifically for them. It’s just not economically feasible to build large systems for small use cases.
“Right now, people are all using the same version of Teams or Slack or what have you,” says Ernst & Young’s Malhotra. “Microsoft can’t make a custom version just for me.” But once AI begins to accelerate the speed of software development while reducing costs, it starts to become much more feasible.
9 IT resolutions for 2025
https://www.cio.com/article/3629833/9-it-resolutions-for-2025.html
1. Innovate
“We’re embracing innovation,”
2. Double down on harnessing the power of AI
Not surprisingly, getting more out of AI is top of mind for many CIOs.
“I am excited about the potential of generative AI, particularly in the security space,”
3. And ensure effective and secure AI rollouts
“AI is everywhere, and while its benefits are extensive, implementing it effectively across a corporation presents challenges. Balancing the rollout with proper training, adoption, and careful measurement of costs and benefits is essential, particularly while securing company assets in tandem,”
4. Focus on responsible AI
The possibilities of AI grow by the day — but so do the risks.
“My resolution is to mature in our execution of responsible AI,”
“AI is the new gold and in order to truly maximize it’s potential, we must first have the proper guardrails in place. Taking a human-first approach to AI will help ensure our state can maintain ethics while taking advantage of the new AI innovations.”
5. Deliver value from generative AI
As organizations move from experimenting and testing generative AI use cases, they’re looking for gen AI to deliver real business value.
“As we go into 2025, we’ll continue to see the evolution of gen AI. But it’s no longer about just standing it up. It’s more about optimizing and maximizing the value we’re getting out of gen AI,”
6. Empower global talent
Although harnessing AI is a top objective for Morgan Stanley’s Wetmur, she says she’s equally committed to harnessing the power of people.
7. Create a wholistic learning culture
Wetmur has another talent-related objective: to create a learning culture — not just in her own department but across all divisions.
8. Deliver better digital experiences
Deltek’s Cilsick has her sights set on improving her company’s digital employee experience, believing that a better DEX will yield benefits in multiple ways.
Cilsick says she first wants to bring in new technologies and automation to “make things as easy as possible,” mirroring the digital experiences most workers have when using consumer technologies.
“It’s really about leveraging tech to make sure [employees] are more efficient and productive,”
“In 2025 my primary focus as CIO will be on transforming operational efficiency, maximizing business productivity, and enhancing employee experiences,”
9. Position the company for long-term success
Lieberman wants to look beyond 2025, saying another resolution for the year is “to develop a longer-term view of our technology roadmap so that we can strategically decide where to invest our resources.”
“My resolutions for 2025 reflect the evolving needs of our organization, the opportunities presented by AI and emerging technologies, and the necessity to balance innovation with operational efficiency,”
Lieberman aims to develop AI capabilities to automate routine tasks.
“Bots will handle common inquiries ranging from sales account summaries to HR benefits, reducing response times and freeing up resources for strategic initiatives,”
Not just hype — here are real-world use cases for AI agents
https://venturebeat.com/ai/not-just-hype-here-are-real-world-use-cases-for-ai-agents/
Just seven or eight months ago, when a customer called in to or emailed Baca Systems with a service question, a human agent handling the query would begin searching for similar cases in the system and analyzing technical documents.
This process would take roughly five to seven minutes; then the agent could offer the “first meaningful response” and finally begin troubleshooting.
But now, with AI agents powered by Salesforce, that time has been shortened to as few as five to 10 seconds.
Now, instead of having to sift through databases for previous customer calls and similar cases, human reps can ask the AI agent to find the relevant information. The AI runs in the background and allows humans to respond right away, Russo noted.
AI can serve as a sales development representative (SDR) to send out general inquires and emails, have a back-and-forth dialogue, then pass the prospect to a member of the sales team, Russo explained.
But once the company implements Salesforce’s Agentforce, a customer needing to modify an order will be able to communicate their needs with AI in natural language, and the AI agent will automatically make adjustments. When more complex issues come up — such as a reconfiguration of an order or an all-out venue change — the AI agent will quickly push the matter up to a human rep.
Open Source in 2025: Strap In, Disruption Straight Ahead
Look for new tensions to arise in the New Year over licensing, the open source AI definition, security and compliance, and how to pay volunteer maintainers.
https://thenewstack.io/open-source-in-2025-strap-in-disruption-straight-ahead/
The trend of widely used open source software moving to more restrictive licensing isn’t new.
In addition to the demands of late-stage capitalism and impatient investors in companies built on open source tools, other outside factors are pressuring the open source world. There’s the promise/threat of generative AI, for instance. Or the shifting geopolitical landscape, which brings new security concerns and governance regulations.
What’s ahead for open source in 2025?
More Consolidation, More Licensing Changes
The Open Source AI Debate: Just Getting Started
Security and Compliance Concerns Will Rise
Paying Maintainers: More Cash, Creativity Needed
Kyberturvallisuuden ja tekoälyn tärkeimmät trendit 2025
https://www.uusiteknologia.fi/2024/11/20/kyberturvallisuuden-ja-tekoalyn-tarkeimmat-trendit-2025/
1. Cyber infrastructure will be centered on a single, unified security platform
2. Big data will give an edge against new entrants
3. AI’s integrated role in 2025 means building trust, governance engagement, and a new kind of leadership
4. Businesses will adopt secure enterprise browsers more widely
5. AI’s energy implications will be more widely recognized in 2025
6. Quantum realities will become clearer in 2025
7. Security and marketing leaders will work more closely together
Presentation: For 2025, ‘AI eats the world’.
https://www.ben-evans.com/presentations
Just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity.
https://www.securityweek.com/ai-implementing-the-right-technology-for-the-right-use-case/
If 2023 and 2024 were the years of exploration, hype and excitement around AI, 2025 (and 2026) will be the year(s) that organizations start to focus on specific use cases for the most productive implementations of AI and, more importantly, to understand how to implement guardrails and governance so that it is viewed as less of a risk by security teams and more of a benefit to the organization.
Businesses are developing applications that add Large Language Model (LLM) capabilities to provide superior functionality and advanced personalization
Employees are using third party GenAI tools for research and productivity purposes
Developers are leveraging AI-powered code assistants to code faster and meet challenging production deadlines
Companies are building their own LLMs for internal use cases and commercial purposes.
AI is still maturing
However, just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity. Right now, we very much see AI in this “peak of inflated expectations” phase and predict that it will dip into the “trough of disillusionment”, where organizations realize that it is not the silver bullet they thought it would be. In fact, there are already signs of cynicism as decision-makers are bombarded with marketing messages from vendors and struggle to discern what is a genuine use case and what is not relevant for their organization.
There is also regulation that will come into force, such as the EU AI Act, which is a comprehensive legal framework that sets out rules for the development and use of AI.
AI certainly won’t solve every problem, and it should be used like automation, as part of a collaborative mix of people, process and technology. You simply can’t replace human intuition with AI, and many new AI regulations stipulate that human oversight is maintained.
7 Splunk Predictions for 2025
https://www.splunk.com/en_us/form/future-predictions.html
AI: Projects must prove their worth to anxious boards or risk defunding, and LLMs will go small to reduce operating costs and environmental impact.
OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI
Three of the leading artificial intelligence companies are seeing diminishing returns from their costly efforts to develop newer models.
https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai
Sources: OpenAI, Google, and Anthropic are all seeing diminishing returns from costly efforts to build new AI models; a new Gemini model misses internal targets
It Costs So Much to Run ChatGPT That OpenAI Is Losing Money on $200 ChatGPT Pro Subscriptions
https://futurism.com/the-byte/openai-chatgpt-pro-subscription-losing-money?fbclid=IwY2xjawH8epVleHRuA2FlbQIxMQABHeggEpKe8ZQfjtPRC0f2pOI7A3z9LFtFon8lVG2VAbj178dkxSQbX_2CJQ_aem_N_ll3ETcuQ4OTRrShHqNGg
In a post on X-formerly-Twitter, CEO Sam Altman admitted an “insane” fact: that the company is “currently losing money” on ChatGPT Pro subscriptions, which run $200 per month and give users access to its suite of products including its o1 “reasoning” model.
“People use it much more than we expected,” the cofounder wrote, later adding in response to another user that he “personally chose the price and thought we would make some money.”
Though Altman didn’t explicitly say why OpenAI is losing money on these premium subscriptions, the issue almost certainly comes down to the enormous expense of running AI infrastructure: the massive and increasing amounts of electricity needed to power the facilities that power AI, not to mention the cost of building and maintaining those data centers. Nowadays, a single query on the company’s most advanced models can cost a staggering $1,000.
Tekoäly edellyttää yhä nopeampia verkkoja
https://etn.fi/index.php/opinion/16974-tekoaely-edellyttaeae-yhae-nopeampia-verkkoja
A resilient digital infrastructure is critical to effectively harnessing telecommunications networks for AI innovations and cloud-based services. The increasing demand for data-rich applications related to AI requires a telecommunications network that can handle large amounts of data with low latency, writes Carl Hansson, Partner Solutions Manager at Orange Business.
AI’s Slowdown Is Everyone Else’s Opportunity
Businesses will benefit from some much-needed breathing space to figure out how to deliver that all-important return on investment.
https://www.bloomberg.com/opinion/articles/2024-11-20/ai-slowdown-is-everyone-else-s-opportunity
Näin sirumarkkinoilla käy ensi vuonna
https://etn.fi/index.php/13-news/16984-naein-sirumarkkinoilla-kaey-ensi-vuonna
The growing demand for high-performance computing (HPC) for artificial intelligence and HPC computing continues to be strong, with the market set to grow by more than 15 percent in 2025, IDC estimates in its recent Worldwide Semiconductor Technology Supply Chain Intelligence report.
IDC predicts eight significant trends for the chip market by 2025.
1. AI growth accelerates
2. Asia-Pacific IC Design Heats Up
3. TSMC’s leadership position is strengthening
4. The expansion of advanced processes is accelerating.
5. Mature process market recovers
6. 2nm Technology Breakthrough
7. Restructuring the Packaging and Testing Market
8. Advanced packaging technologies on the rise
2024: The year when MCUs became AI-enabled
https://www-edn-com.translate.goog/2024-the-year-when-mcus-became-ai-enabled/?fbclid=IwZXh0bgNhZW0CMTEAAR1_fEakArfPtgGZfjd-NiPd_MLBiuHyp9qfiszczOENPGPg38wzl9KOLrQ_aem_rLmf2vF2kjDIFGWzRVZWKw&_x_tr_sl=en&_x_tr_tl=fi&_x_tr_hl=fi&_x_tr_pto=wapp
The AI party in the MCU space started in 2024, and in 2025, it is very likely that there will be more advancements in MCUs using lightweight AI models.
Adoption of AI acceleration features is a big step in the development of microcontrollers. The inclusion of AI features in microcontrollers started in 2024, and it is very likely that in 2025, their features and tools will develop further.
Just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity.
https://www.securityweek.com/ai-implementing-the-right-technology-for-the-right-use-case/
If 2023 and 2024 were the years of exploration, hype and excitement around AI, 2025 (and 2026) will be the year(s) that organizations start to focus on specific use cases for the most productive implementations of AI and, more importantly, to understand how to implement guardrails and governance so that it is viewed as less of a risk by security teams and more of a benefit to the organization.
Businesses are developing applications that add Large Language Model (LLM) capabilities to provide superior functionality and advanced personalization
Employees are using third party GenAI tools for research and productivity purposes
Developers are leveraging AI-powered code assistants to code faster and meet challenging production deadlines
Companies are building their own LLMs for internal use cases and commercial purposes.
AI is still maturing
AI Regulation Gets Serious in 2025 – Is Your Organization Ready?
While the challenges are significant, organizations have an opportunity to build scalable AI governance frameworks that ensure compliance while enabling responsible AI innovation.
https://www.securityweek.com/ai-regulation-gets-serious-in-2025-is-your-organization-ready/
Similar to the GDPR, the EU AI Act will take a phased approach to implementation. The first milestone arrives on February 2, 2025, when organizations operating in the EU must ensure that employees involved in AI use, deployment, or oversight possess adequate AI literacy. Thereafter from August 1 any new AI models based on GPAI standards must be fully compliant with the act. Also similar to GDPR is the threat of huge fines for non-compliance – EUR 35 million or 7 percent of worldwide annual turnover, whichever is higher.
While this requirement may appear manageable on the surface, many organizations are still in the early stages of defining and formalizing their AI usage policies.
Later phases of the EU AI Act, expected in late 2025 and into 2026, will introduce stricter requirements around prohibited and high-risk AI applications. For organizations, this will surface a significant governance challenge: maintaining visibility and control over AI assets.
Tracking the usage of standalone generative AI tools, such as ChatGPT or Claude, is relatively straightforward. However, the challenge intensifies when dealing with SaaS platforms that integrate AI functionalities on the backend. Analysts, including Gartner, refer to this as “embedded AI,” and its proliferation makes maintaining accurate AI asset inventories increasingly complex.
Where frameworks like the EU AI Act grow more complex is their focus on ‘high-risk’ use cases. Compliance will require organizations to move beyond merely identifying AI tools in use; they must also assess how these tools are used, what data is being shared, and what tasks the AI is performing. For instance, an employee using a generative AI tool to summarize sensitive internal documents introduces very different risks than someone using the same tool to draft marketing content.
For security and compliance leaders, the EU AI Act represents just one piece of a broader AI governance puzzle that will dominate 2025.
The next 12-18 months will require sustained focus and collaboration across security, compliance, and technology teams to stay ahead of these developments.
The Global Partnership on Artificial Intelligence (GPAI) is a multi-stakeholder initiative which aims to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities.
https://gpai.ai/about/#:~:text=The%20Global%20Partnership%20on%20Artificial,activities%20on%20AI%2Drelated%20priorities.
1,951 Comments
Tomi Engdahl says:
Victoria Song / The Verge:
Hands-on with Cluely’s “cheat on everything” tool: the AI can’t intuit what the user needs despite being given prior context and takes long to give a response
I used the ‘cheat on everything’ AI tool and it didn’t help me cheat on anything
Cluely’s creator thinks it will revolutionize job interviews, meetings, and… catfishing?
https://www.theverge.com/ai-artificial-intelligence/654223/cheat-on-everything-ai
Tomi Engdahl says:
Leah Nylen / Bloomberg:
US v. Google: a Perplexity executive says Google’s contract with Motorola blocked Perplexity from being the default AI assistant on new Motorola devices — – AI startup’s app will be preloaded on new Motorola phones — Company in negotiations with another smartphone maker
https://www.bloomberg.com/news/articles/2025-04-23/perplexity-executive-says-google-blocked-motorola-s-use-of-ai-assistant
Erin Woo / The Information:
Court data: Google estimates its Gemini AI chatbot had 35M DAUs and 350M MAUs worldwide as of last month while ChatGPT had 160M DAUs and 600M MAUs
https://www.theinformation.com/briefings/googles-gemini-user-numbers-revealed-court
Tomi Engdahl says:
Stephanie Lai / Bloomberg:
Trump signs an EO to boost AI education and workforce training, and establishes an AI education task force led by White House OSTP Director Michael Kratsios — President Donald Trump signed an executive action to boost artificial intelligence education and workforce training …
https://www.bloomberg.com/news/articles/2025-04-23/trump-to-sign-executive-order-to-bolster-ai-education-workforce
Tomi Engdahl says:
Larry Dignan / Constellation Research:
Microsoft’s 2025 Work Trend Index report, based on a survey of 31,000 people, argues that “Frontier Firms” utilizing agentic AI as digital workers are emerging — As enterprises adopt AI agents and digital workers, companies’ success may depend on the human-agent ratio and how workflows are managed.
Microsoft: Human, AI agent ratios will be critical to success as new roles emerge
https://www.constellationr.com/blog-news/insights/microsoft-human-ai-agent-ratios-will-be-critical-success-new-roles-emerge
As enterprises adopt AI agents and digital workers, companies’ success may depend on the human-agent ratio and how workflows are managed.
Microsoft released its annual Work Trend Index report, which surveyed 31,000 people across 31 countries and including LinkedIn labor and hiring trends. The report argues that Frontier Firms are emerging that are utilizing digital workers via agentic AI.
Tomi Engdahl says:
Andrew Liszewski / The Verge:
Meta rolls out live translations and other AI features to all Ray-Ban smart glasses, unveils new color lens combos, and plans an India, Mexico, and UAE debut — The Ray-Ban Meta Skyler frames are also now available in shiny chalky gray with sapphire Transitions lenses.
Meta rolls out live translations to all Ray-Ban smart glasses users
The Ray-Ban Meta Skyler frames are also now available in shiny chalky gray with sapphire Transitions lenses.
https://www.theverge.com/news/654387/meta-smart-glasses-ray-ban-live-translation-ai
Tomi Engdahl says:
Kyle Wiggers / TechCrunch:
Sources: OpenAI aims to debut its next “open” model, which will be “text in, text out” and possibly let developers turn “reasoning” on and off, in early summer — Toward the end of March, OpenAI announced its intention to release its first “open” language model since GPT‑2 sometime this year.
OpenAI seeks to make its upcoming ‘open’ AI model best-in-class
https://techcrunch.com/2025/04/23/openai-seeks-to-make-its-upcoming-open-ai-model-best-in-class/
Tomi Engdahl says:
Sharon Goldman / Fortune:
Listen Labs, which uses AI to conduct thousands of voice interviews simultaneously for customer research, raised $27M across a seed and Series A led by Sequoia
https://fortune.com/article/ai-startup-listen-labs-sequoia-27-million-funding/
Tomi Engdahl says:
Umar Shakir / The Verge:
Perplexity’s iOS app enables support for the company’s conversational AI voice assistant, letting users ask the chatbot to write emails, set reminders, and more
Perplexity’s AI voice assistant is now available on iOS
The Perplexity bot is now available on both iPhones and Android devices, allowing you to ask it to set reminders, send messages, and more.
https://www.theverge.com/news/654946/perplexity-ai-mobile-assistant-ios-iphone
Tomi Engdahl says:
SemiAnalysis:
A deep dive on AMD 2.0: a new sense of urgency, rapid AI software stack progress, a critical talent retention challenge, ROCm lags Nvidia’s CUDA, and more
AMD 2.0 – New Sense of Urgency | MI450X Chance to Beat Nvidia | Nvidia’s New Moat Rapid Improvements, Developers First Approach, Low AMD AI Software Engineer Pay, Python DSL, UALink Disaster, MI325x, MI355x, MI430X UL4, MI450X Architecture, IF64/IF128, Flexible IO, UALink, IF
https://semianalysis.com/2025/04/23/amd-2-0-new-sense-of-urgency-mi450x-chance-to-beat-nvidia-nvidias-new-moat/
Tomi Engdahl says:
Kyt Dotson / SiliconANGLE:
Nvidia announces the general availability of its NeMo platform to build AI agents, supporting Meta’s Llama, Microsoft’s Phi, Google’s Gemma, and Mistral
UPDATED 09:00 EDT / APRIL 23 2025
AI
Nvidia announces general availability of NeMo tools for building AI agents
https://siliconangle.com/2025/04/23/nvidia-announces-general-availability-nemo-tools-building-ai-agents/
Tomi Engdahl says:
Michael Nuñez / VentureBeat:
Microsoft updates Microsoft 365 Copilot with OpenAI-powered AI agents Researcher and Analyst, available via its new Agent Store, AI-powered Search, and more
Microsoft just launched powerful AI ‘agents’ that could completely transform your workday — and challenge Google’s workplace dominance
https://venturebeat.com/ai/microsoft-just-launched-powerful-ai-agents-that-could-completely-transform-your-workday-and-challenge-googles-workplace-dominance/
Tomi Engdahl says:
Amanda Silberling / TechCrunch:
Character.AI unveils AvatarFX, a new AI video generation model in closed beta that animates characters in diverse styles and voices, including human-like and 2D
https://techcrunch.com/2025/04/22/character-ai-unveils-avatarfx-an-ai-video-model-to-create-lifelike-chatbots/
Tomi Engdahl says:
Tom Warren / The Verge:
Nvidia updates its G-Assist AI chatbot, offering plugin and API support to let developers connect to external services and tools, such as Spotify and Gemini
Nvidia’s AI assistant on Windows now has plugins for Spotify, Twitch, and more
G-Assist is moving beyond just improving PC gaming, to controlling more apps and services.
https://www.theverge.com/news/654221/nvidia-g-assist-plugins-spotify-twitch-apps
Tomi Engdahl says:
Sri Muppidi / The Information:
Documents: OpenAI told investors it expects revenues of $125B in 2029 and $174B in 2030, with sales from agents and other products exceeding those from ChatGPT
https://www.theinformation.com/articles/openai-forecasts-revenue-topping-125-billion-2029-agents-new-products-gain
Tomi Engdahl says:
Eleanor Olcott / Financial Times:
Baidu CEO Robin Li says demand for text-based models like DeepSeek’s is “shrinking” and claims its model had a higher propensity for “hallucinations”
Baidu founder highlights ‘shrinking’ demand for DeepSeek’s text-based AI
Search group’s chief makes rare criticism of China’s generative artificial intelligence darling
https://www.ft.com/content/c462fbd1-1672-4d8f-bd91-c3aa185d2418
Baidu’s founder has said demand for the type of text-based models developed by generative AI sensation DeepSeek is “shrinking”, as his search group seeks to reestablish itself as an artificial intelligence leader in China.
In a striking criticism of the limitations of China’s AI darling, Robin Li told Baidu’s developer conference on Friday that there were constraints to DeepSeek’s leading model. Its popular R1, widely praised by the global developer community, is geared towards text-based tasks.
“The market for text models is shrinking,” Li said, as he released two new multimodal models — Ernie 4.5 Turbo and X1 Turbo — with not just text but also audio, image and video capabilities. He added that DeepSeek’s model had a higher propensity for misleading “hallucinations” and was slower and more expensive than other domestic offerings.
DeepSeek did not immediately respond to a request for comment.
Li said the competitive landscape for new models was constantly changing, with a stream of “powerful new models that provide more choice”.
His comments come as Baidu tries to reposition itself as an AI leader after being forced to pivot by dropping its subscription service to its chatbot and making its models freely available as “open source”. Baidu faces stiff domestic competition from its peer Alibaba, which has released competitive open-source multimodal models.
DeepSeek is still focused on further developing models
After the release of ChatGPT in November 2022, Baidu was the first Chinese company to respond to OpenAI’s popular chatbot. In March 2023, it announced Erniebot, with the mobile version later rebranded to Wenxinyan.
On Friday, Baidu announced the release of a new AI agent application called Xinxiang, entering an increasingly crowded market that includes Alibaba’s Quark app and offerings from start-ups such as Manus AI.
Baidu also announced it had built a computing cluster composed of 30,000 Kunlun P800 AI chips from its semiconductor design subsidiary, which it said could support training of several DeepSeek-like models. Li added that developers did not have to worry about a shortage of computing power.
The FT last month reported that Samsung had sold Kunlun three years’ supply of logic chips, a critical component in manufacturing AI products.
Tomi Engdahl says:
Gabriel Daros / Rest of World:
Brazil’s AI-based social security app, launched in 2018, has cut bureaucracy in some cases but wrongly rejected hundreds of vulnerable people over minor errors
Brazil’s AI-powered social security app is wrongly rejecting claims
An algorithmic tool meant to reduce bureaucracy is misfiring on complex cases, and vulnerable Brazilians are paying the price.
https://restofworld.org/2025/brazil-ai-social-security-app-rejected/
Tomi Engdahl says:
Bloomberg:
Baidu rolls out Ernie 4.5 Turbo and X1 Turbo, new versions of its flagship foundation and reasoning models, as it looks to take on Alibaba’s Qwen and DeepSeek
https://www.bloomberg.com/news/articles/2025-04-25/china-s-baidu-upgrades-ernie-ai-models-and-slashes-prices
Tomi Engdahl says:
Benjamin Mullin / New York Times:
Ziff Davis sues OpenAI, alleging it used Ziff Davis’ content to train AI models; sources say Ziff Davis is seeking at least hundreds of millions of dollars — Ziff Davis, which owns more than 45 media properties, is accusing the tech company of infringing on the publisher’s copyrights and diluting its trademarks.
https://www.nytimes.com/2025/04/24/business/media/ziff-davis-openai-lawsuit.html
Tomi Engdahl says:
Stephen Nellis / Reuters:
Adobe says Firefly users can now generate images using OpenAI’s GPT, Google’s Imagen 3 and Veo 2, and Flux 1.1 Pro, in addition to its own Firefly image models — Adobe (ADBE.O) said on Thursday it is adding image-generation artificial intelligence models from OpenAI and Alphabet’s Google …
Adobe adds AI models from OpenAI, Google to its Firefly app
https://www.reuters.com/business/adobe-adds-ai-models-openai-google-its-firefly-app-2025-04-24/
Tomi Engdahl says:
Kyle Wiggers / TechCrunch:
OpenAI expands deep research usage for Plus, Pro, and Team users with an o4-mini-powered lightweight version, which also rolls out to Free users today — OpenAI is bringing a new “lightweight” version of its ChatGPT deep research tool, which scours the web to compile research reports on a topic …
OpenAI rolls out a ‘lightweight’ version of its ChatGPT deep research tool
https://techcrunch.com/2025/04/24/openai-rolls-out-a-lightweight-version-of-its-chatgpt-deep-research-tool/
Tomi Engdahl says:
Hayden Field / CNBC:
Motorola’s new Razr phones will include Perplexity’s AI search engine, as part of a distribution partnership that lets Perplexity gain users rather than revenue — Perplexity AI is getting in on the smartphone game. — The startup on Thursday announced a partnership to bring …
Perplexity AI enters the smartphone market with Motorola partnership
https://www.cnbc.com/2025/04/24/perplexity-ai-enters-the-smartphone-market-with-motorola-partnership.html
Allison Johnson / The Verge:
Motorola unveils three new Razr models, including the $1,300 Razr Ultra flip phone with a 7-inch inner and 4-inch outer screen and wood option, shipping May 15
https://www.theverge.com/gadgets/654846/motorola-razr-ultra-2025-specs-screen-price
Tomi Engdahl says:
Kevin Roose / New York Times:
An interview with Kyle Fish, who Anthropic hired in 2024 as a welfare researcher to study AI consciousness and estimates a ~15% chance that models are conscious
https://www.nytimes.com/2025/04/24/technology/ai-welfare-anthropic-claude.html?unlocked_article_code=1.CE8._VFI.9HgGKQQkvm3j&smid=url-share
Tomi Engdahl says:
Raipe vienyt tekoälyä 6-0
https://etn.fi/index.php/13-news/17456-raipe-vienyt-tekoaelyae-6-0
Digian tekoäly on yrittänyt datan perusteella ennustaa Liigan voittajaa lähes ifk-maisella menestyksellä. Nyt Digia myöntää, että SaiPa on Raimo Helmisen luotsaamana yllättänyt niin tekoälyn kuin koko jääkiekkoyleisön. SaiPan mestaruuteen tekoäly ei vieläkään usko, vaan povaa mestariksi KalPaa.
Pudotuspelien alkaessa Digian tekoäly ennusti kultapelin taistelupariksi Lukkoa ja Ilvestä sekä pronssipeliin KalPaa ja SaiPaa. Parit osuivat kohdilleen, mutta sijoitukset menivät toisin päin, tulkitsee Digia täydellistä ohilaukaustaan.
Tekoälyn onnistumisprosentti koko kaudelta on tässä vaiheessa 68, eli tekoäly on ennustanut noin kaksi kolmasosaa peleistä oikein. Pudotuspeleissä prosentti on jo noin 73 eli kolme neljästä pelistä oikein.
Tomi Engdahl says:
Open-Source TTS Reaches New Heights: Nari Labs Releases Dia, a 1.6B Parameter Model for Real-Time Voice Cloning and Expressive Speech Synthesis on Consumer Device
https://www.marktechpost.com/2025/04/22/open-source-tts-reaches-new-heights-nari-labs-releases-dia-a-1-6b-parameter-model-for-real-time-voice-cloning-and-expressive-speech-synthesis-on-consumer-device/
The development of text-to-speech (TTS) systems has seen significant advancements in recent years, particularly with the rise of large-scale neural models. Yet, most high-fidelity systems remain locked behind proprietary APIs and commercial platforms. Addressing this gap, Nari Labs has released Dia, a 1.6 billion parameter TTS model under the Apache 2.0 license, providing a strong open-source alternative to closed systems such as ElevenLabs and Sesame.
Tomi Engdahl says:
Microsoft just launched powerful AI ‘agents’ that could completely transform your workday — and challenge Google’s workplace dominance
https://venturebeat.com/ai/microsoft-just-launched-powerful-ai-agents-that-could-completely-transform-your-workday-and-challenge-googles-workplace-dominance/
Microsoft announced today a major expansion of its artificial intelligence tools with the “Microsoft 365 Copilot Wave 2 Spring release,” introducing new AI “agents” designed to function as digital colleagues that can perform complex workplace tasks through deep reasoning capabilities.
In an exclusive interview, Aparna Chennapragada, Chief Product Officer of Experiences and Devices at Microsoft, told VentureBeat the company is building toward a vision where AI serves as more than just a tool — becoming an integral collaborator in daily work.
“We are around the corner from a big moment in the AI world,” Chennapragada said. “It started out with all of the model advances, and everyone’s been really excited about it and the intelligence abundance. Now it’s about making sure that intelligence is available to all of the folks, especially at work.”
Tomi Engdahl says:
LLMs Can Now Learn without Labels: Researchers from Tsinghua University and Shanghai AI Lab Introduce Test-Time Reinforcement Learning (TTRL) to Enable Self-Evolving Language Models Using Unlabeled Data
https://www.marktechpost.com/2025/04/22/llms-can-now-learn-without-labels-researchers-from-tsinghua-university-and-shanghai-ai-lab-introduce-test-time-reinforcement-learning-ttrl-to-enable-self-evolving-language-models-using-unlabeled-da/
Despite significant advances in reasoning capabilities through reinforcement learning (RL), most large language models (LLMs) remain fundamentally dependent on supervised data pipelines. RL frameworks such as RLHF have pushed model alignment and instruction-following performance but rely heavily on human feedback and labeled datasets. As LLMs are increasingly applied in dynamic environments—ranging from educational settings to scientific workflows—they are required to generalize beyond curated training data.
However, existing models often exhibit performance gaps when confronted with distribution shifts or novel reasoning tasks. While techniques like Test-Time Scaling (TTS) and Test-Time Training (TTT) have been proposed to mitigate this, the absence of reliable reward signals during inference poses a core challenge for deploying RL in unsupervised settings.
Tomi Engdahl says:
Despite significant advances in reasoning capabilities through reinforcement learning (RL), most large language models (LLMs) remain fundamentally dependent on supervised data pipelines. RL frameworks such as RLHF have pushed model alignment and instruction-following performance but rely heavily on human feedback and labeled datasets. As LLMs are increasingly applied in dynamic environments—ranging from educational settings to scientific workflows—they are required to generalize beyond curated training data.
However, existing models often exhibit performance gaps when confronted with distribution shifts or novel reasoning tasks. While techniques like Test-Time Scaling (TTS) and Test-Time Training (TTT) have been proposed to mitigate this, the absence of reliable reward signals during inference poses a core challenge for deploying RL in unsupervised settings.
https://www.darkreading.com/cyberattacks-data-breaches/deepseek-breach-opens-floodgates-dark-web
Tomi Engdahl says:
The Hidden Cost of AI Coding
“The best moments in our lives are not the passive, receptive, relaxing times… The best moments usually occur if a person’s body or mind is stretched to its limits in a voluntary effort to accomplish something difficult and worthwhile.” — Mihaly Csikszentmihalyi
https://terriblesoftware.org/2025/04/23/the-hidden-cost-of-ai-coding/
Tomi Engdahl says:
One Prompt Can Bypass Every Major LLM’s Safeguards
https://www.forbes.com/sites/tonybradley/2025/04/24/one-prompt-can-bypass-every-major-llms-safeguards/
For years, generative AI vendors have reassured the public and enterprises that large language models are aligned with safety guidelines and reinforced against producing harmful content. Techniques like Reinforcement Learning from Human Feedback have been positioned as the backbone of model alignment, promising ethical responses even in adversarial situations.
But new research from HiddenLayer suggests that confidence may be dangerously misplaced.
Their team has uncovered what they’re calling a universal, transferable bypass technique that can manipulate nearly every major LLM—regardless of vendor, architecture or training pipeline. The method, dubbed “Policy Puppetry,” is a deceptively simple but highly effective form of prompt injection that reframes malicious intent in the language of system configuration, allowing it to circumvent traditional alignment safeguards.
Tomi Engdahl says:
Why the ADaSci Agentic AI Certification Stands Out
Whether you’re leading AI adoption or building systems hands-on, this certification positions you to lead — not follow.
https://analyticsindiamag.com/ai-highlights/why-the-adasci-agentic-ai-certification-stands-out/
Tomi Engdahl says:
CIOs increasingly dump in-house POCs for commercial AI
https://www.cio.com/article/3965387/cios-increasingly-dump-in-house-pocs-for-commercial-ai.html
High failure rates and low returns of homegrown AI pilots are accelerating a shift to commercial, off-the-shelf solutions as software vendors roll more AI functionality into their products.
After thousands of AI proof-of-concept projects have died on the vine, many organizations are scaling back internal efforts in favor of adopting commercial, off-the-shelf AI tools.
About half of companies surveyed by Gartner in late 2023 were developing their own AI tools, but the number fell to about 20% at the end of 2024, says John-David Lovelock, a vice president and analyst at Gartner.
Many organizations are still running a few POCs, but cooling hype surrounding generative AI has many CIOs turning to vendors, whether they be large language model (LLM) providers or traditional software sellers with AI built into their products, Lovelock says.
Tomi Engdahl says:
https://hbr.org/2025/04/teach-ai-to-work-like-a-member-of-your-team
Tomi Engdahl says:
Sam Altman Admits That Saying “Please” and “Thank You” to ChatGPT Is Wasting Millions of Dollars in Computing Power
“You never know.”
https://futurism.com/altman-please-thanks-chatgpt
If chivalry isn’t already dead, it’s certainly circling the drain.
OpenAI CEO and tech billionaire Sam Altman recently admitted that people politely saying “please” and “thank you” to their AI chatbots is costing him bigtime.
When one poster on X-formerly-Twitter wondered aloud “how much money OpenAI has lost in electricity costs from people saying ‘please’ and ‘thank you’ to their models,” Altman chimed in, saying it’s “tens of millions of dollars well spent.”
“You never know,” he added.
While it may seem pointless to treat an AI chatbot with respect, some AI architects say it’s an important move. Microsoft’s design manager Kurtis Beavers, for example, says proper etiquette “helps generate respectful, collaborative outputs.”
“Using polite language sets a tone for the response,” Beavers notes. The argument can certainly be made; what we consider “artificial intelligence” might more accurately be described as “prediction machines,” like your phone’s predictive text, but with more autonomy to spit out complete sentences in response to questions or instructions.
Tomi Engdahl says:
AI has grown beyond human knowledge, says Google’s DeepMind unit
A new agentic approach called ‘streams’ will let AI models learn from the experience of the environment without human ‘pre-judgment’.
https://www.zdnet.com/article/ai-has-grown-beyond-human-knowledge-says-googles-deepmind-unit/
The world of artificial intelligence (AI) has recently been preoccupied with advancing generative AI beyond simple tests that AI models easily pass. The famed Turing Test has been “beaten” in some sense, and controversy rages over whether the newest models are being built to game the benchmark tests that measure performance
Tomi Engdahl says:
Model Context Protocol (MCP) vs Function Calling: A Deep Dive into AI Integration Architectures
https://www.marktechpost.com/2025/04/18/model-context-protocol-mcp-vs-function-calling-a-deep-dive-into-ai-integration-architectures/
The integration of Large Language Models (LLMs) with external tools, applications, and data sources is increasingly vital. Two significant methods for achieving seamless interaction between models and external systems are Model Context Protocol (MCP) and Function Calling. Although both approaches aim to expand the practical capabilities of AI models, they differ fundamentally in their architectural design, implementation strategies, intended use cases, and overall flexibility.
Model Context Protocol (MCP)
Anthropic introduced the Model Context Protocol (MCP) as an open standard designed to facilitate structured interactions between AI models and various external systems. MCP emerged in response to the growing complexity associated with integrating AI-driven capabilities into diverse software environments. By establishing a unified approach, MCP significantly reduces the need for bespoke integrations, offering a common, interoperable framework that promotes efficiency and consistency.
At its core, MCP employs a sophisticated client-server architecture comprising three integral components:
Host Process: This is the initiating entity, typically an AI assistant or an embedded AI-driven application. It controls and orchestrates the flow of requests, ensuring the integrity of communication.
MCP Clients: These intermediaries manage requests and responses. Clients play crucial roles, including message encoding and decoding, initiating requests, handling responses, and managing errors.
MCP Servers: These represent external systems or data sources that are structured to expose their data or functionality through standardized interfaces and schemas. They manage incoming requests from clients, execute necessary operations, and return structured responses.
Communication is facilitated through the JSON-RPC 2.0 protocol, renowned for its simplicity and effectiveness in remote procedure calls.
Security Model
Security forms a cornerstone of the MCP design, emphasizing a rigorous, host-mediated approach. This model incorporates:
Process Sandboxing: Each MCP server process operates in an isolated sandboxed environment, ensuring robust protection against unauthorized access and minimizing vulnerabilities.
Path Restrictions: Strictly controlled access policies limit server interactions to predetermined file paths or system resources, significantly reducing the potential attack surface.
Encrypted Transport: Communication is secured using strong encryption methods, ensuring that data confidentiality, integrity, and authenticity are maintained throughout interactions.
Application Domains
The adaptability of MCP has led to widespread adoption across multiple sectors. In the domain of software development, MCP has been extensively integrated into various platforms and Integrated Development Environments (IDEs). This integration enables real-time, context-aware coding assistance, significantly enhancing developer productivity, accuracy, and efficiency. By offering immediate suggestions, code completion, and intelligent error detection, MCP-enabled systems help developers rapidly identify and resolve issues, streamline coding processes, and maintain high code quality. Also, MCP is effectively deployed in enterprise solutions where internal AI assistants securely interact with proprietary databases and enterprise systems. These AI-driven solutions support enhanced decision-making processes by providing instant access to critical information, facilitating efficient data analysis, and enabling streamlined workflows, which collectively boost operational effectiveness and strategic agility.
Function Calling
Function Calling is a streamlined yet powerful approach that significantly enhances the operational capabilities of LLMs by enabling them to directly invoke and execute external functions in response to user input or contextual cues. Unlike traditional AI model interactions, which are limited to generating static text-based reactions based on their training data, Function Calling enables models to take action in real-time. When a user issues a prompt that implies or explicitly requests a specific task, such as checking the weather, querying a database, or triggering an API call, the model identifies the intent, selects the appropriate function from a predefined set, and formats the required parameters for execution. This dynamic linkage between natural language understanding and programmable actions effectively bridges the gap between conversational AI and software automation, effectively bridging the gap between conversational AI and software automation. As a result, Function Calling extends the functional utility of LLMs by transforming them from static knowledge providers into interactive agents capable of engaging with external systems, retrieving fresh data, executing live tasks, and delivering results that are both timely and contextually relevant.
Detailed Mechanism
The implementation of Function Calling involves several precise stages:
Function Definition: Developers explicitly define the available functions, including detailed metadata such as the function name, required parameters, expected input formats, and return types. This clearly defined structure is crucial for the accurate and reliable execution of functions.
Natural Language Parsing: Upon receiving user input, the AI model parses the natural language prompts meticulously to identify the correct function and the specific parameters required for execution.
Following these initial stages, the model generates a structured output, commonly in JSON format, detailing the function call, which is then executed externally. The execution results are fed back into the model, enabling further interactions or the generation of an immediate response.
Comparative Analysis
MCP offers a comprehensive protocol suitable for extensive and complex integrations, particularly valuable in enterprise environments that require broad interoperability, robust security, and a scalable architecture. In contrast, Function Calling offers a simpler and more direct interaction method, suitable for applications that require rapid responses, task-specific operations, and straightforward implementations.
While MCP’s architecture involves higher initial setup complexity, including extensive infrastructure management, it ultimately provides greater security and scalability benefits. Conversely, Function Calling’s simplicity allows for faster integration, making it ideal for applications with limited scope or specific, task-oriented functionalities. From a security standpoint, MCP inherently incorporates stringent protections suitable for high-risk environments. Function Calling, though simpler, necessitates careful external management of security measures. Regarding scalability, MCP’s sophisticated asynchronous mechanisms efficiently handle large-scale, concurrent interactions, making it optimal for expansive, enterprise-grade solutions. Function Calling is effective in scalable contexts but requires careful management to avoid complexity as the number of functions increases.
Tomi Engdahl says:
An AI Customer Service Chatbot Made Up a Company Policy—and Created a Mess
When an AI model for code-editing company Cursor hallucinated a new rule, users revolted.
https://arstechnica.com/ai/2025/04/cursor-ai-support-bot-invents-fake-policy-and-triggers-user-uproar/
Tomi Engdahl says:
Meta AI Introduces Collaborative Reasoner (Coral): An AI Framework Specifically Designed to Evaluate and Enhance Collaborative Reasoning Skills in LLMs
https://www.marktechpost.com/2025/04/19/meta-ai-introduces-collaborative-reasoner-coral-an-ai-framework-specifically-designed-to-evaluate-and-enhance-collaborative-reasoning-skills-in-llms/
Tomi Engdahl says:
Famed AI researcher launches controversial startup to replace all human workers everywhere
https://techcrunch.com/2025/04/19/famed-ai-researcher-launches-controversial-startup-to-replace-all-human-workers-everywhere/
Every now and then, a Silicon Valley startup launches with such an “absurdly” described mission that it’s difficult to discern if the startup is for real or just satire.
Such is the case with Mechanize, a startup whose founder — and the non-profit AI research organization he founded called Epoch — is being skewered on X after he announced it.
Tomi Engdahl says:
Agentic IDEs: Next Frontier in Intelligent Coding
How AI-powered development environments are evolving from assistants to autonomous collaborators, reshaping the future of software creation.
https://thenewstack.io/agentic-ides-next-frontier-in-intelligent-coding/
Especially in the last couple of years, integrated development environments (IDEs) have come a long way from their humble beginnings as glorified text editors. What once merely color-coded your syntax and gave you the occasional autocomplete suggestion is now an entire ecosystem of intelligent tools.
But even with AI copilots becoming the norm, we’re only scratching the surface of what’s possible. The next step isn’t just about smarter suggestions, it’s about autonomous agents that can reason, adapt, and act within your IDE. Welcome to the age of agentic IDEs.
Forget passive autocomplete. Agentic IDEs are about to change the way developers think about productivity, creativity, and collaboration.
What Makes an IDE ‘Agentic’?
To understand what differentiates agentic IDEs from their predecessors, we need to move past the buzzwords. An agentic IDE doesn’t just react to prompts or queries. It understands context, maintains memory, sets goals, makes decisions, and learns from your coding style over time.
Imagine you’re building a multi-service application. A traditional AI copilot might help you write an endpoint or suggest a better regex. An agentic IDE, on the other hand, could recognize you’re working on an authentication flow, propose an architecture, refactor repetitive logic across files, spin up necessary Docker containers, write tests, and document your code — all while maintaining a dialogue about your intent. It has initiative. It’s not just helping you code; it’s collaborating with you.
Agentic systems don’t just answer questions. They pursue outcomes.
The Core Building Blocks
So, what makes these environments possible? It’s not magic, it’s the convergence of several maturing technologies that, together, shift the IDE from reactive to proactive.
LLMs with persistent memory: Instead of stateless autocomplete, agentic IDEs leverage models that remember what you’ve built across sessions, modules, and even projects. This memory enables a nuanced understanding of codebases and continuity of logic that typical AI assistants can’t match.
Planning and goal-setting modules: These let agents break down tasks, assess sub-goals, and iterate as they receive feedback or run into roadblocks. They can adapt mid-task, reprioritize steps, and handle multi-stage operations that resemble real-world development patterns.
Tool-use abilities: The agent isn’t limited to code generation; it can execute shell commands, interact with APIs, trigger builds, or query internal documentation. Essentially, it can wield the entire development environment like a developer does, with the added benefit of speed and scale.
Autonomous decision-making: With reinforcement learning, feedback loops, or symbolic planning, agents can choose when to act and when to pause and ask. This enables a form of self-directed problem solving, where agents can go beyond instructions to pursue desired outcomes.
Together, these aren’t just additive, they’re transformative. They push the boundaries of what an IDE is supposed to be, evolving it from “smart assistant” to “autonomous co-developer” that collaborates on equal footing with its human counterpart.
What’s Already Happening
You don’t have to imagine for long. Early forms of agentic IDEs are already surfacing. Projects like Cursor, Continue, and Codeium are integrating LLMs that can recall and reason more deeply about your project state. LangChain and AutoGen are enabling frameworks for chaining agent actions. Microsoft’s Copilot Workspace is a preview of what goal-based development might look like in practice.
Meanwhile, open source players are experimenting with embedding agents inside familiar environments like VS Code and JetBrains.
Tomi Engdahl says:
Maybe Meta’s Llama claims to be open source because of the EU AI act
https://simonwillison.net/2025/Apr/19/llama-eu-ai-act/
Tomi Engdahl says:
https://www.dailymail.co.uk/yourmoney/consumer/article-14618485/sams-club-costco-rival-removing-self-checkouts.html
Tomi Engdahl says:
Announcing the Agent2Agent Protocol (A2A)
https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/?fbclid=IwY2xjawJyun5leHRuA2FlbQIxMQABHnjTqGEMsas0S5X9XUJwXYaFFkGflF_DKOHAHZ5wQM16SNgGHEXOlAaXCZII_aem_15L5CmBqEFps6J-FNG358g
A new era of Agent Interoperability
AI agents offer a unique opportunity to help people be more productive by autonomously handling many daily recurring or complex tasks. Today, enterprises are increasingly building and deploying autonomous agents to help scale, automate and enhance processes throughout the workplace–from ordering new laptops, to aiding customer service representatives, to assisting in supply chain planning.
Today, we’re launching a new, open protocol called Agent2Agent (A2A), with support and contributions from more than 50 technology partners like Atlassian, Box, Cohere, Intuit, Langchain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday; and leading service providers including Accenture, BCG, Capgemini, Cognizant, Deloitte, HCLTech, Infosys, KPMG, McKinsey, PwC, TCS, and Wipro. The A2A protocol will allow AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms or applications. We believe the A2A framework will add significant value for customers, whose AI agents will now be able to work across their entire enterprise application estates.
A2A facilitates communication between a “client” agent and a “remote” agent. A client agent is responsible for formulating and communicating tasks, while the remote agent is responsible for acting on those tasks in an attempt to provide the correct information or take the correct action.
Hiring a software engineer can be significantly simplified with A2A collaboration. Within a unified interface like Agentspace, a user (e.g., a hiring manager) can task their agent to find candidates matching a job listing, location, and skill set. The agent then interacts with other specialized agents to source potential candidates. The user receives these suggestions and can then direct their agent to schedule further interviews, streamlining the candidate sourcing process. After the interview process completes, another agent can be engaged to facilitate background checks. This is just one example of how AI agents need to collaborate across systems to source a qualified job candidate.
Tomi Engdahl says:
Investor Says AI Is Already “Fully Replacing People”
“Just because something is inevitable, it doesn’t mean you deploy [it].”
https://futurism.com/investor-ai-fully-replacing-people
The hype over artificial intelligence might be quieting as the US tech sector stresses over tariffs, but some investors are still knee-deep in the mud, panning for gold.
One of them, prominent venture capitalist and former gaming CEO Victor Lazarte, is so confident that he claims AI is already “fully replacing people.” While some companies have pumped the breaks on hyped-up promises of a fully-automated future, Lazarte is charging full steam ahead.
“Big companies talk about, like, ‘AI isn’t replacing people, it’s augmenting them,’” the tycoon said on the Twenty Minute VC podcast. “This is bullshit. It’s fully replacing people.”
Tomi Engdahl says:
https://www.techradar.com/computing/artificial-intelligence/openai-just-gave-chatgpt-plus-a-massive-boost-with-generous-new-usage-limits
Tomi Engdahl says:
‘Periodic table of machine learning’ framework unifies AI models to accelerate innovation
https://techxplore.com/news/2025-04-periodic-table-machine-framework-ai.html#google_vignette
Tomi Engdahl says:
https://techcrunch.com/2025/04/24/perplexity-ceo-says-its-browser-will-track-everything-users-do-online-to-sell-hyper-personalized-ads/
Tomi Engdahl says:
Chinese data centers refurbing and selling Nvidia RTX 4090D GPUs due to overcapacity — 48GB models sell for up to $5,500
News
By Jowi Morales published 2 days ago
Companies are letting go of their idle GPUs for instant profits.
https://www.tomshardware.com/tech-industry/artificial-intelligence/chinese-data-centers-refurbing-and-selling-rtx-4090s-due-to-overcapacity-48gb-models-sell-for-up-to-usd5-500
Tomi Engdahl says:
From Logic to Confusion: MIT Researchers Show How Simple Prompt Tweaks Derail LLM Reasoning
https://www.marktechpost.com/2025/04/15/from-logic-to-confusion-mit-researchers-show-how-simple-prompt-tweaks-derail-llm-reasoning/
Tomi Engdahl says:
https://dev.to/yashksaini/how-i-built-a-ai-agent-server-3315
Tomi Engdahl says:
Meta Says It’s Okay to Feed Copyrighted Books Into Its AI Model Because They Have No “Economic Value”
How convenient.
https://futurism.com/meta-copyrighted-books-no-value