Here are some of the the major AI trends shaping 2026 — based on current expert forecasts, industry reports, and recent developments in technology. The material is analyzed using AI tools and final version hand-edited to this blog text:
1. Generative AI Continues to Mature
Generative AI (text, image, video, code) will become more advanced and mainstream, with notable growth in:
* Generative video creation
* Gaming and entertainment content generation
* Advanced synthetic data for simulations and analytics
This trend will bring new creative possibilities — and intensify debates around authenticity and copyright.
2. AI Agents Move From Tools to Autonomous Workers
Rather than just answering questions or generating content, AI systems will increasingly act autonomously, performing complex, multi-step workflows and interacting with apps and processes on behalf of users — a shift sometimes called agentic AI. These agents will become part of enterprise operations, not just assistant features.
3. Smaller, Efficient & Domain-Specific Models
Instead of “bigger is always better,” specialized AI models tailored to specific industries (healthcare, finance, legal, telecom, manufacturing) will start to dominate in many enterprise applications. These models are more accurate, legally compliant, and cost-efficient than general models.
4. AI Embedded Everywhere
AI won’t be an add-on feature — it will be built into everyday software and devices:
* Office apps with intelligent drafting, summarization, and task insights
* Operating systems with native AI
* Edge devices processing AI tasks locally
This makes AI pervasive in both work and consumer contexts.
5. AI Infrastructure Evolves: Inference & Efficiency Focus
More investment is going into inference infrastructure — the real-time decision-making step where models run in production — thereby optimizing costs, latency, and scalability. Enterprises are also consolidating AI stacks for better governance and compliance.
6. AI in Healthcare, Research, and Sustainability
AI is spreading beyond diagnostics into treatment planning, global health access, environmental modeling, and scientific discovery. These applications could help address personnel shortages and speed up research breakthroughs.
7. Security, Ethics & Governance Become Critical
With AI handling more sensitive tasks, organizations will prioritize:
* Ethical use frameworks
* Governance policies
* AI risk management
This trend reflects broader concerns about trust, compliance, and responsible deployment.
8. Multimodal AI Goes Mainstream
AI systems that understand and generate across text, images, audio, and video will grow rapidly, enabling richer interactions and more powerful applications in search, creative work, and interfaces.
9. On-Device and Edge AI Growth
10. New Roles: AI Manager & Human-Agent Collaboration
Instead of replacing humans, AI will shift job roles:
* People will manage, supervise, and orchestrate AI agents
* Human expertise will focus on strategy, oversight, and creative judgment
This human-in-the-loop model becomes the norm.
Sources:
[1]: https://www.brilworks.com/blog/ai-trends-2026/?utm_source=chatgpt.com “7 AI Trends to Look for in 2026″
[2]: https://www.forbes.com/sites/bernardmarr/2025/10/13/10-generative-ai-trends-in-2026-that-will-transform-work-and-life/?utm_source=chatgpt.com “10 Generative AI Trends In 2026 That Will Transform Work And Life”
[3]: https://millipixels.com/blog/ai-trends-2026?utm_source=chatgpt.com “AI Trends 2026: The Key Enterprise Shifts You Must Know | Millipixels”
[4]: https://www.digitalregenesys.com/blog/top-10-ai-trends-for-2026?utm_source=chatgpt.com “Digital Regenesys | Top 10 AI Trends for 2026″
[5]: https://www.n-ix.com/ai-trends/?utm_source=chatgpt.com “7 AI trends to watch in 2026 – N-iX”
[6]: https://news.microsoft.com/source/asia/2025/12/11/microsoft-unveils-7-ai-trends-for-2026/?utm_source=chatgpt.com “Microsoft unveils 7 AI trends for 2026 – Source Asia”
[7]: https://www.risingtrends.co/blog/generative-ai-trends-2026?utm_source=chatgpt.com “7 Generative AI Trends to Watch In 2026″
[8]: https://www.fool.com/investing/2025/12/24/artificial-intelligence-ai-trends-to-watch-in-2026/?utm_source=chatgpt.com “3 Artificial Intelligence (AI) Trends to Watch in 2026 and How to Invest in Them | The Motley Fool”
[9]: https://www.reddit.com//r/AI_Agents/comments/1q3ka8o/i_read_google_clouds_ai_agent_trends_2026_report/?utm_source=chatgpt.com “I read Google Cloud’s “AI Agent Trends 2026” report, here are 10 takeaways that actually matter”
414 Comments
Tomi Engdahl says:
https://www.microsoft.com/en-us/microsoft-copilot/for-individuals/do-more-with-ai/ai-art-and-creativity/how-to-use-ai-grammar-and-writing-tools/
Tomi Engdahl says:
https://thenewstack.io/openclaw-gsd/
Tomi Engdahl says:
How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory
https://www.marktechpost.com/2026/02/06/how-to-build-a-production-grade-agentic-ai-system-with-hybrid-retrieval-provenance-first-citations-repair-loops-and-episodic-memory/
Tomi Engdahl says:
Claude Code is the Inflection Point
What It Is, How We Use It, Industry Repercussions, Microsoft’s Dilemma, Why Anthropic Is Winning
https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point
4% of GitHub public commits are being authored by Claude Code right now. At the current trajectory, we believe that Claude Code will be 20%+ of all daily commits by the end of 2026. While you blinked, AI consumed all of software development.
Our sister publication Fabricated Knowledge described software like linear TV during the rise of the internet and thinks that the rise of Claude Code is going to be a new layer of intelligence on top of software akin to DRAM versus NAND. Today SemiAnalysis is going to dive into the repercussions of Claude Code, what it is, and why Claude is so good.
Tomi Engdahl says:
Scale Customer Onboarding
without Scaling Chaos
What works for 10 customers breaks at 100.
Structure unlocks AI, cuts effort by 60%, and makes onboarding predictable.
https://www.micromerce.com/p/onboarding-at-scale?fbclid=Iwb21leAP2e8ZjbGNrA_Z532V4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHvwUHHgPy-yIsm-FMhbiybxqhOYmtu9nwrHDCG2bnl01mWVGwx5VbJ_CQGrX_aem_7hIlOS_t7vQcATviF-q1Fg&utm_medium=paid&utm_source=fb&utm_id=120238280130810417&utm_content=120238813933440417&utm_term=120238813933450417&utm_campaign=120238280130810417
Tomi Engdahl says:
Cloudflare Demonstrates Moltworker, Bringing Self-Hosted AI Agents to the Edge
https://www.infoq.com/news/2026/02/cloudflare-moltworker/
Cloudflare has introduced Moltworker, an open-source implementation that enables running Moltbot—a self-hosted personal AI agent—on Cloudflare’s Developer Platform, removing the need for dedicated local hardware. Moltbot, recently renamed from Clawdbot, is designed to operate as a personal assistant through chat applications, integrating with AI models, browsers, and third-party tools while remaining user-controlled.
Moltworker adapts Moltbot to Cloudflare Workers by combining an entrypoint Worker with isolated Sandbox containers. The Worker acts as an API router and administration layer, while the Moltbot runtime and its integrations execute inside Sandboxes. Persistent state, including conversation memory and session data, is stored in Cloudflare R2, addressing the ephemeral nature of containers.
The implementation leverages recent enhancements in Node.js compatibility within Cloudflare Workers.
Tomi Engdahl says:
Googlen uusi palvelu on kuin tieteissarjasta – Hinta on kuitenkin valtava
Palvelu on toistaiseksi saatavilla hyvin rajoitetusti.
https://www.iltalehti.fi/digiuutiset/a/518c4b52-b8ec-4199-a1ce-75ee32f42c0f
Google Deepmind julkaisi viime viikolla Project Genie -nimisen palvelun, jossa käyttäjät voivat pelata kokonaan tekoälyn luomassa pelimaailmassa. Pelimaailmoja ei ole määritelty ennalta, vaan sellaisen voi pyytää itse luotavaksi oman kuvauksen eli syötteen perusteella.
Tomi Engdahl says:
Tekoäly syrjäyttää ihmiset näistä kahdesta ammatista lähitulevaisuudessa
https://sepantalo.fi/article/tekoaly-syrjayttaa-ihmiset-naista-kahdesta-ammatista-lahitulevaisuudessa
Juridiikka
Rekrytointi
Tomi Engdahl says:
https://muropaketti.com/tietotekniikka/tietotekniikkauutiset/nain-komeaa-jalkea-tekee-grokin-imagine-1-0-videotyokalu/
Tomi Engdahl says:
https://medium.com/@joe.njenga/i-tried-new-claude-code-agent-teams-and-discovered-new-way-to-swarm-28a6cd72adb8
Tomi Engdahl says:
Is artificial general intelligence already here? A new case that today’s LLMs meet key tests
https://techxplore.com/news/2026-02-artificial-general-intelligence-case-today.html
Tomi Engdahl says:
https://thenewstack.io/how-ai-coding-makes-developers-56-faster-and-19-slower/
Tomi Engdahl says:
Large Language Model Reasoning Failures
https://arxiv.org/abs/2602.06176?fbclid=IwdGRjcAP4lwpjbGNrA_iXBmV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHkNWC9wZAVhy2g3stw_EwzdK82TzX6ya0l_zQonNZEzWYFgafWRTVCOAjXv1_aem_pHN3G4GHxKNjOKmYU7NFZQ
Tomi Engdahl says:
https://www.facebook.com/share/p/18AGWnwaKm/
Holy shit… Stanford just published the most uncomfortable paper on LLM reasoning I’ve read in a long time.
This isn’t a flashy new model or a leaderboard win. It’s a systematic teardown of how and why large language models keep failing at reasoning even when benchmarks say they’re doing great.
The paper does one very smart thing upfront: it introduces a clean taxonomy instead of more anecdotes. The authors split reasoning into non-embodied and embodied.
Non-embodied reasoning is what most benchmarks test and it’s further divided into informal reasoning (intuition, social judgment, commonsense heuristics) and formal reasoning (logic, math, code, symbolic manipulation).
Embodied reasoning is where models must reason about the physical world, space, causality, and action under real constraints.
Across all three, the same failure patterns keep showing up.
> First are fundamental failures baked into current architectures. Models generate answers that look coherent but collapse under light logical pressure. They shortcut, pattern-match, or hallucinate steps instead of executing a consistent reasoning process.
> Second are application-specific failures. A model that looks strong on math benchmarks can quietly fall apart in scientific reasoning, planning, or multi-step decision making. Performance does not transfer nearly as well as leaderboards imply.
> Third are robustness failures. Tiny changes in wording, ordering, or context can flip an answer entirely. The reasoning wasn’t stable to begin with; it just happened to work for that phrasing.
One of the most disturbing findings is how often models produce unfaithful reasoning. They give the correct final answer while providing explanations that are logically wrong, incomplete, or fabricated.
This is worse than being wrong, because it trains users to trust explanations that don’t correspond to the actual decision process.
Embodied reasoning is where things really fall apart. LLMs systematically fail at physical commonsense, spatial reasoning, and basic physics because they have no grounded experience.
Even in text-only settings, as soon as a task implicitly depends on real-world dynamics, failures become predictable and repeatable.
The authors don’t just criticize. They outline mitigation paths: inference-time scaling, analogical memory, external verification, and evaluations that deliberately inject known failure cases instead of optimizing for leaderboard performance.
But they’re very clear that none of these are silver bullets yet.
The takeaway isn’t that LLMs can’t reason.
It’s more uncomfortable than that.
LLMs reason just enough to sound convincing, but not enough to be reliable.
And unless we start measuring how models fail not just how often they succeed we’ll keep deploying systems that pass benchmarks, fail silently in production, and explain themselves with total confidence while doing the wrong thing.
That’s the real warning shot in this paper.
Paper: Large Language Model Reasoning Failures