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.
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How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman
Learn the AI workflow John Blackman used to build a full event platform for his church
https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex
Tomi Engdahl says:
Could a Data Center Rewiring Lead to 6x Faster AI? Cornelis Networks’ congestion-free architecture takes on Ethernet and InfiniBand
https://spectrum.ieee.org/ai-network-architecture
Tomi Engdahl says:
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Tomi Engdahl says:
Introducing Warp 2.0: Reimagining coding with the Agentic Development Environment
https://www.warp.dev/blog/reimagining-coding-agentic-development-environment
Tomi Engdahl says:
Cursor shows unlimited access to Claude 4 Sonnet, but I hit a rate limit error after just two prompt uses
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Tomi Engdahl says:
https://github.blog/changelog/2025-06-24-github-copilot-coding-agent-is-now-available-for-copilot-business-users/
Tomi Engdahl says:
AI fatigue is real, and it’s costing brands more than engagement
From gaming to marketing, backlash against AI is growing. Here’s how to navigate the disillusionment phase wisely.
https://martech.org/ai-fatigue-is-real-and-its-costing-brands-more-than-engagement/
A few weeks ago, I posted a cute little AI song to a hobby group I’m in. It was funny and had a lot of insider terms and jokes. I thought it would make these folks smile. It didn’t.
“Is this more BS AI?”
“Crap.”
“Garbage.”
“Saccharine.”
The flood of hateful comments and downvotes led me to delete the post and leave the group. The comments weren’t personal; they were aimed squarely at using AI. The anger was so intense that dozens of people condemned the song and told me never to use AI again. That’s when I realized the backlash against AI had truly arrived.
The mood is shifting: AI meets public resistance
Tomi Engdahl says:
What’s at the heart of this?
These reactions are not just about low-quality output. They tap into the belief that AI-generated content lacks authenticity and emotional resonance. This belief is especially pronounced among the people who have grown up in a digital-first world.
Up to 45% of Gen Z and 44% of Boomers oppose the use of AI in advertising, CivicScience found. That signals a rare generational consensus — a fear of new technology and a craving for authenticity.
https://martech.org/ai-fatigue-is-real-and-its-costing-brands-more-than-engagement/
Tomi Engdahl says:
Darwin-Gödel Machine: The First Self-Improving AI System Is Here
A deep dive into the novel architecture of the Darwin-Gödel Machine and what its impressive self-improvement capability means for the future of AI.
https://ai.gopubby.com/darwin-g%C3%B6del-machine-the-first-self-improving-ai-system-is-here-078575c1043a
Tomi Engdahl says:
https://www.producthunt.com/p/vibecoding/vibe-coding-more-than-just-aesthetic-it-s-a-mindset
Tomi Engdahl says:
The Fastest Way to Start Coding with AI Tools (No Code) Beginners Guide
https://www.geeky-gadgets.com/beginners-guide-to-ai-coding/#google_vignette
Have you ever had a new idea for an app but felt overwhelmed by the thought of learning to code? You’re not alone. For years, the world of programming seemed like an exclusive club, requiring years of study and technical expertise. But here’s the fantastic option: AI tools are rewriting the rules of app development, making it possible for anyone—even those without a single line of code under their belt—to bring their ideas to life. Imagine building a fully functional application in hours instead of weeks, with AI guiding you through the process like a personal mentor. It’s not just a possibility; it’s happening, and it’s transforming how we think about coding.
Skill Leap AI explain how AI-powered platforms are leveling the playing field, offering tools that simplify coding, automate repetitive tasks, and help you focus on creativity rather than technical roadblocks. Whether you’re curious about generating code with conversational AI, designing sleek apps without writing a single line of code, or tackling advanced projects with full-stack capabilities, there’s a solution tailored to your needs. This isn’t just about learning to code—it’s about unlocking your potential to create, innovate, and solve problems in ways you never thought possible. By the end, you might just realize that coding with AI isn’t as intimidating as it seems—it’s empowering.
Tomi Engdahl says:
https://www.thetransmitter.org/from-bench-to-bot/many-students-want-to-learn-to-use-artificial-intelligence-responsibly-but-their-professors-are-struggling-to-meet-that-need/
Tomi Engdahl says:
https://www.marktechpost.com/2025/06/22/deepseek-researchers-open-sources-a-personal-project-named-nano-vllm-a-lightweight-vllm-implementation-built-from-scratch/
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Lessons from letting AI vibe code a landing page
An AI-powered experiment in copy, research and vibe coding — delivering faster results, fewer revisions and a few unexpected hurdles.
https://martech.org/lessons-from-letting-ai-vibe-code-a-landing-page/
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What AI Still Can’t Do — And Why It Matters More Than Ever
https://www.forbes.com/sites/kolawolesamueladebayo/2025/06/25/what-ai-still-cant-do—and-why-it-matters-more-than-ever/
When Sal Khan introduced GPT-4 into Khan Academy’s tutoring platform in 2023, it rocked the education world. For the first time, a generative AI model could walk students through complex AP problems, provide Socratic-style coaching and give instant feedback across subjects. It was fast, accessible and, in many ways, effective.
But as The New York Times reported, there were mixed reactions to Khan Academy’s AI tutor, Khanmigo. Educators voiced concern that the bot sometimes does “too much of the thinking work,” potentially hindering students’ critical thinking. Others said the AI would be more effective if it could identify misconceptions and respond with open-ended questions — much like a human teacher would.
A study from The Wharton School, summarized by Axios, found that students who used AI tools during practice “performed significantly worse on exams, where they can’t rely on artificial intelligence.” The National Council of Teachers of Mathematics noted that while these AI tools are helpful, “students will need teachers to help them create a bridge between prior knowledge, new knowledge and shared knowledge.”
It’s a strong reminder that even in a world where AI can correct grammar in milliseconds and potentially transform global education by breaking language barriers and expanding access to underserved communities, according to UNESCO, there are still many things it can’t do.
According to experts like Selva Pankaj, joint CEO of the Regent Group, as our relationship with AI deepens, the gap between what AI can do and what only humans should do is where our focus should be. One of the most critical steps to take, Pankaj told me in an interview, is modernizing our education systems to prepare students for a world where algorithms and natural intelligence work together in harmony.
Rethinking Skills In An AI-Driven World
AI tools are no longer just nice-to-haves. They now function as intelligent assistants — managing tasks, scheduling meetings, drafting emails, generating artwork and parsing vast datasets — often with a single prompt.
That trend isn’t slowing down and it’s already reshaping the job market in many ways. According to The World Economic Forum’s 2023 Future of Jobs Report, the demand for roles such as data entry clerks and bank tellers will decline quickly. While that doesn’t necessarily mean that hard skills like graphic design or data analysis are disappearing, it certainly means they’re no longer sufficient on their own.
Pankaj believes the global curriculum must evolve to reflect AI’s growing impact on us. “Education has largely remained unchanged for the last 500 years. We are at the cusp of a fourth educational revolution where we can leverage AI to improve education for all.”
When I asked how that looks in practice, he explained how AI is already helping educators: “An AI system can quickly analyze test results and provide insights into where students are struggling and why,” he said. “This saves time and helps teachers take targeted action.”
One article by the WEF argues that the future of education lies in hybrid intelligence, where AI and humans complement one another. “By combining the analytical power of AI with the irreplaceable human element of teaching, we can truly transform education for the next generation,” the report said.
Preparing For A Post-AI Classroom
Many universities have introduced AI-related courses, but the core of most curriculums still rewards memorization and standardized testing — skills that machines now perform better and faster. According to Pankaj, “that needs to change very quickly.”
To stay relevant, educators must move students beyond passive learning toward empathy, curiosity, adaptability and the kind of moral reasoning no algorithm can replicate. These are the skills that future employers and society will value most.
In an article published on Harvard Business Impact, Cherie Werhun, associate vice provost of human development and potential at Sheridan College, emphasized the need to nurture what she calls “self-intelligence.” “If we don’t devote attention to students’ self-intelligence skills,” she wrote, “we are equipping them with only half of the education they will need to not only survive, but thrive.”
Neglecting those capabilities doesn’t just shortchange students; it could lead to a wider unemployment crisis. Today’s jobs increasingly reward human strengths like emotional intelligence and problem-solving, not rote knowledge.
That means encouraging students to evaluate, reflect and creatively solve problems, not just absorb information.
Tomi Engdahl says:
https://dev.to/rohan_sharma/creating-a-chatbot-from-scratch-and-vibe-coding-the-ui-1bij
Tomi Engdahl says:
https://www.marktechpost.com/2025/06/20/meta-ai-researchers-introduced-a-scalable-byte-level-autoregressive-u-net-model-that-outperforms-token-based-transformers-across-language-modeling-benchmarks/
Tomi Engdahl says:
https://metapress.com/claude-turned-meeting-notes-into-action-items-productivity-skyrocketed/
Tomi Engdahl says:
https://venturebeat.com/ai/anthropic-study-leading-ai-models-show-up-to-96-blackmail-rate-against-executives/
Tomi Engdahl says:
https://www.businessinsider.com/godfather-of-ai-geoffrey-hinton-openai-google-difference-safety-2025-6
Tomi Engdahl says:
https://madza.hashnode.dev/9-ai-productivity-tools-you-will-be-amazed-to-discover
Tomi Engdahl says:
The Entire Internet Is Reverting to Beta
The AI takeover is changing everything about the web—and not necessarily for the better.
https://www.theatlantic.com/technology/archive/2025/06/ai-janky-web/683228/
Tomi Engdahl says:
https://www.postermywall.com/
Tomi Engdahl says:
https://www.synthesia.io/ads/meta/learning-and-development?fbclid=IwQ0xDSwLKiiZleHRuA2FlbQIxMQABHk2H-ZEkXgY-fxEvwWL-y-AsBAGOtgxWmz4Ya0FZwIQnO9kaXGXUAagzhT-f_aem_YhkWICUIboROa8cfGKsYng&utm_campaign=CELL%20A%20-%20VALUE%20-%20EUOCT2%20-%20L%26D%20-%20ASC&utm_content=605%2F%2FSadAboutGoodThings%2F%2FL%26D%2F%2FWouldn%E2%80%99t-Recommend-Short-Equipment-UGC&utm_source=facebook&utm_medium=paid&hsa_acc=378186399695961&hsa_cam=120227407264110061&hsa_grp=120227407264090061&hsa_ad=120227415754640061&hsa_src=fb&hsa_net=facebook&hsa_ver=3
Tomi Engdahl says:
chatgpt: If you’re wondering about the programming language used to build me — that would mostly be Python, along with other systems-level languages like C++ for performance-critical components.
Tomi Engdahl says:
OpenAI has a major problem with its biggest financial backer. https://trib.al/P7Qxenx
“Nonsensical Benchmark Hacking”: Microsoft No Longer Believes OpenAI Is Capable of Achieving AGI
OpenAI has a major problem with its biggest financial backer.
https://futurism.com/microsoft-belief-openai-agi?fbclid=IwY2xjawLMcEtleHRuA2FlbQIxMQABHrwM9mHbgPY9-D7IQx02BLWplTDQQ_reSFVKx1Wgc4VxNuj0HS9TUjbyYzn3_aem_ZLxrsazcHVl2UdCGeJ5ZJw
The love’s gone bad between Microsoft and OpenAI, whose lucrative partnership ushered in our age of AI hype. OpenAI is trying to convert into a for-profit company, but it’s so far failed to secure its benefactor’s approval and negotiate a new contract.
The frustration is running so high that the ChatGPT maker is reportedly considering bring an antitrust suit against Microsoft if it doesn’t get its way.
Tomi Engdahl says:
Agentic AI won’t wait for your data architecture to catch up
https://www.infoworld.com/article/4011064/agentic-ai-wont-wait-for-your-data-architecture-to-catch-up.html
The technology, which enables rapid business transformation, requires a new data layer—one built for speed, scale, and diverse teams
A decade ago, the cloud ignited a massive replatforming of application and server infrastructure. Open-source technologies like Docker and Kubernetes transformed software velocity and operational flexibility, launching a new era.
But it didn’t happen overnight. Enterprises had to adapt to shifting foundations, talent gaps, and an open-source ecosystem evolving faster than most teams could absorb.
Today, agentic AI is catalyzing a similar, profound replatforming. This shift centers on real-time data interaction, where success is measured in milliseconds, not minutes. What’s at stake is your company’s ability to thrive in new marketplaces shaped by intelligent systems.
To navigate this transition, here are key considerations for preparing your data infrastructure for agentic AI.
The AI data layer must serve polyglot, multi-persona teams
Traditional data platforms, which primarily served SQL analysts and data engineers, are no longer sufficient. Today’s AI landscape demands real-time access for a vastly expanded audience: machine learning engineers, developers, product teams, and crucially, automated agents – all needing to work with data in tools like Python, Java, and SQL.
Much as Docker and Kubernetes revolutionized cloud-native application development, Apache Iceberg has become the foundational open-source technology for this modern AI data infrastructure. Iceberg provides a transactional format for evolving schemas, time travel, and high-concurrency access.
Combined with a powerful and scalable serverless data platform, this enables real-time dataflows for unpredictable, agent-driven workloads with strict latency needs.
Together, these technologies enable fluid collaboration across diverse roles and systems.
Common challenges include:
Lineage and compliance: Tracking data origins, managing changes, and supporting deletion for regulations like GDPR are complex and crucial.
Resource efficiency: Without smart provisioning, GPU and TPU costs can quickly escalate. Managed cloud offerings for OSS products help by abstracting compute management.
Access control and security: Misconfigured permissions present a significant risk. Overly broad access can easily lead to critical data being exposed.
Discovery and context: Even with tools like Iceberg, teams struggle to find the metadata needed for just-in-time dataset access.
Ease of use: Managing modern data tools can burden teams with unnecessary complexity. Simplifying workflows for developers, analysts, and agents is essential to keep productivity high and barriers low.
Tomi Engdahl says:
The agentic AI reset is here
https://www.cio.com/article/4012453/the-agentic-ai-reset-is-here.html
Now we can get down to serious AI integration and production-grade implementations.
In ServiceNow’s second annual AI Maturity Index, over 4,500 worldwide private and public sector leaders were surveyed and findings revealed that this year’s average maturity score actually dropped from last year, from 44 to 35 (out of 100 points). In addition, fewer than 1% of respondents scored over 50 on their 100-point AI maturity scale. But this is, in fact, good news for the industry and for AI adoption and here we explore why that is and how CIOs are moving forward.
In a previous article, 4 recs for CIOs as they implement agentic AI, I noted that despite the hype, CIOs agree there’s an approaching reset of agentic AI expectations. The ServiceNow report is further confirmation that the reset is underway, which is due to a number of factors. In implementing agentic AI, organizations began to know what they didn’t know. Unlike gen AI, which could be implemented as a standalone or bolted on somewhere, agentic AI requires far deeper integration — at least if you want to utilize it for maximum benefit.
Tomi Engdahl says:
Bernie Sanders: If AI Is Doing Such Amazing Work, Everyone Should Get a Four-Day Workweek
“Not a radical idea.”
https://futurism.com/bernie-sanders-ai-four-day
In 2025, we’re constantly told, artificial intelligence is bringing about a workplace revolution. Countless billionaires have waxed poetic about the “coming recession” and “unemployment crisis” that their hyped up AI chatbots are sure to bring.
Bernie Sanders, the progressive senator from Vermont, has been listening.
Calling the US tech industry on its AI hype — which mostly involves generating shareholder value — Sanders recently posed a rhetorical question on the Joe Rogan podcast: if AI is as powerful as they say, why not give workers a 30-hour week?
“Technology is gonna work to improve us, not just the people who own the technology and the CEOs of large corporations,” Sanders said. “You are a worker, your productivity is increasing because we give you AI, right? Instead of throwing you out on the street, I’m gonna reduce your work week to 32 hours.”
While a 30-hour work week may sound untenable to some, it’s important to remember that the 40 hour week is less than a century old, only becoming federally law in 1940. One could look at that legislation as a concession to placate industrial workers, who in 1933 were agitating for the same 30-hour week which most of us in 2025 can hardly imagine.
Even Bernie agrees. It’s “not a radical idea,” he told Rogan, adding that “there are companies around the world that are doing it with some success.”
However, the reality is that AI is far from ready to bring about optimistic labor reforms like Sanders’ laudable 30-day week, or even OpenAI CEO Sam Altman’s guilt-ridden idea for universal basic income.
Despite widespread fear of AI-fueled layoffs and a job market in shambles, AI’s main function is currently to give corporations cover as they outsource high-paying jobs to lower-wage workers. As time goes on, more and more corporate executives are realizing that AI — buggy, inefficient, and stubbornly prone to hallucinations — is no match for human beings.
Tomi Engdahl says:
Forget about AI costs: Google just changed the game with open-source Gemini CLI that will be free for most developers
https://venturebeat.com/ai/google-is-redefining-enterprise-ai-economics-with-open-source-gemini-cli-that-will-be-free-for-the-majority-of-developers/
For power users and many developers, the command line is the foundational interface for controlling a system and its applications.
Also sometimes referred to as a terminal, the command line interface (CLI) is how users issue commands and build applications as an alternative, or as a complement, to an integrated developer environment (IDE) tool. While it might seem almost anachronistic that a text-only interface accessible with a keyboard (CLI doesn’t even use a mouse) can be modern, it remains a mainstay of developers around the world. In the modern era of generative AI, it’s becoming more powerful too.
Today Google announced its open-source Gemini-CLI that brings natural language command execution directly to developer terminals. Beyond natural language, it brings the power of Google’s Gemini Pro 2.5 — and it does it mostly for free.
The free tier provides 60 model requests per minute and 1,000 requests per day at no charge, limits that Google deliberately set above typical developer usage patterns. Google first measured its own developers’ usage patterns, then doubled that number to set the 1,000 limit.
“To be very clear, for the vast majority of developers, Gemini CLI will be completely free of charge,” Ryan J. Salva, senior director for product management at Google, said in response to a question from VentureBeat during a press briefing. “We do not want you having to watch that token meter like it’s a taxi meter and holding back on your creativity.”
How Google Gemini CLI disrupts the enterprise AI market
Gemini CLI is far from being the first or only AI tool for the command line. OpenAI Codex has a CLI version, as does Anthropic with Claude Code.
Google Gemini CLI, however, is quite different from its two primary commercial rivals in that the tool is open source under the Apache 2.0 license. Then, of course, is the cost. While Gemini CLI is mostly free, OpenAI and Anthropic’s tools are not.
Extensibility through Model Context Protocol and custom extensions
Another key differentiator for Gemini CLI lies in its extensibility architecture, built around the emerging Model Context Protocol (MCP) standard. This approach lets developers connect external services and add new capabilities and positions the tool as a platform rather than a single-purpose application.
During the briefing, Google demonstrated this extensibility through a pre-recorded video showing Gemini CLI integrated with Google’s creative AI tools. An agent creating a cat video set in Australia first generated images using Imagen APIs, then wove them into an animated video using Veo technology.
The extensibility model includes three layers: Built-in MCP server support, bundled extensions that combine MCP servers with configuration files and custom Gemini.md files for project-specific customization. This architecture allows individual developers to tailor their experience while enabling teams to standardize workflows across projects.
Where Google starts charging: Enterprise features and scale
While individual developers enjoy generous free access, Google’s monetization strategy becomes clear for enterprise use cases. The company maintains a clear delineation between free individual use and paid enterprise features.
Accessing Gemini CLI only requires a Google login. It does not require any sort of API key or credit card on file in order to use. While there is a very generous free tier, there can be costs involved for enterprise users.
Salva noted that if an organization wants to run multiple Gemini CLI agents in parallel, or if there are specific policy, governance or data residency requirements, a paid API key comes in. The key could be for access to Google Vertex AI, which provides commercial access to a series of models including, but not limited to, Gemini Pro 2.5
Gemini CLI operates as a local agent with built-in security measures that address common concerns about AI command execution. The system requires explicit user confirmation for each command, with options to “allow once,” “always allow” or deny specific operations.
The tool’s security model includes multiple layers of protection. Users can use native macOS Seatbelt support for sandboxing, run the agent in Docker or Podman containers, and route all network traffic through proxies for inspection. The open-source nature under Apache 2.0 licensing allows complete code auditing.
“You have complete transparency into it,” Salva noted. “The tool only has access to the information that you explicitly provide in a prompt or a reference file path and you decide what context to share with the model on a prompt by prompt by prompt basis.”
While Gemini CLI runs as a local agent it’s important to note that it doesn’t currently run the models locally. That is, the Gemini Pro 2.5 model is accessed from the cloud and Google is not providing support to run a local model. Mullen noted that although there is a subset of tasks which could probably be done with a local model, Google is not shipping local model support today.
Strategic impact on AI development tool economics
For enterprises looking to lead in AI, the extremely generous free tier for Gemini CLI will be an option that should be considered for some use cases.
For individual developers within enterprises, it represents a no-barrier entry for AI access. The open-source architecture addresses common enterprise security concerns by enabling complete code auditing and on-premises deployment options. Organizations can evaluate production-grade AI capabilities without vendor lock-in risks or complex procurement cycles.
“It doesn’t matter if you’ve got dust or dollars, whether you’re a student, hobbyist, a freelancer or a developer at a very well funded company, you should have access to the same tools,” said Salva. “So that is why we’re making Gemini CLI free with genuinely unmatched usage limits right from the get go.”
Tomi Engdahl says:
https://www.thehindu.com/news/cities/bangalore/the-big-ai-shake-up/article69735683.ece
Tomi Engdahl says:
https://www.forbes.com/sites/rachelwells/2025/06/26/5-chatgpt-prompts-to-launch-a-high-paying-side-hustle-no-experience/
Tomi Engdahl says:
AI begins to reshape the IT job landscape as layoffs rise
https://www.cio.com/article/4012162/ai-begins-to-reshape-the-it-job-landscape-as-layoffs-rise.html
As more companies cite AI as a main driver for layoffs, IT pros are left to wonder whether career anxieties are being realized or the industry is simply adjusting to another new paradigm.
Tomi Engdahl says:
”One reason all these companies are willing to shower recruits with cash is that even a superteam of AI engineers costs only a fraction of the price of AI infrastructure like data centers.” – Aivot ovat halpoja (miljoonia), rauta kallista (miljardeja).
It’s Known as ‘The List’—and It’s a Secret File of AI Geniuses
Only a select few researchers have the skills for the hottest area in tech. Mark Zuckerberg and his rivals want to hire them— even if it takes pay packages of $100 million.
https://www.wsj.com/tech/meta-ai-recruiting-mark-zuckerberg-openai-018ed7fc?st=nKj7Vh&fbclid=IwY2xjawLM3w9leHRuA2FlbQIxMQABHpVsVzoiOBMPvI9esI4FfG3HtApDdAwyfbwDkaVDRGl6jClqhw4ANjIBZN2W_aem_kGhKG4xTIR5oNpcmfAzqLA
All over Silicon Valley, the brightest minds in AI are buzzing about “The List,” a compilation of the most talented engineers and researchers in artificial intelligence that Mark Zuckerberg has spent months putting together.
The recruits on “The List” typically have Ph.D.s from elite schools like Berkeley and Carnegie Mellon. They have experience at places like OpenAI in San Francisco and Google DeepMind in London. They are usually in their 20s and 30s—and they all know each other. They spend their days staring at screens to solve the kinds of inscrutable problems that require spectacular amounts of computing power.
And their previously obscure talents have never been so highly valued.
The chief executives of tech goliaths and heavyweight venture capitalists are cozying up with a few dozen nerdy researchers because their specialized knowledge is the key to cashing in on the artificial-intelligence revolution.