AI trends 2026

3,269 Comments

1. August 18, 2026 at 7:31 pm

   Tomi Engdahl says:

   Is ChatGPT dropping more f-bombs lately? Users think so. : https://mrf.lu/2RXKT

   Reply
2. August 18, 2026 at 8:44 pm

   Tomi Engdahl says:

   Agentic AI has a latency problem that more compute won’t solve
   https://thenewstack.io/agentic-ai-latency-infrastructure/

   Enterprise AI agents are hitting a latency wall as network hops, CPU work and centralized infrastructure push production response times beyond 500ms today.

   Half of enterprise AI deployments are missing their own latency targets at peak load. This is the headline finding of Akamai’s State of AI Inference 2026 report, which surveyed 200 AI practitioners and found that 82% of organizations say their most critical use cases require end-to-end response times of 500 milliseconds or less. A total of 64% of organizations now require end-to-end response times of less than 250 milliseconds for their most important use cases, yet 50% of deployments are failing to meet these latency demands at peak load.

   Agentic workflows aren’t a “single round trip”
   The latency issue stems from the way agents work. It’s an iterative process, somewhat like a king sending out knights, emissaries, and messengers to conduct the business of the kingdom. There are many comings and goings, not just one person sent on a single round trip.

   For instance, when an agent built on a framework like LangChain, CrewAI, or Pydantic AI received a user request, it can fan out into dozens of sequential operations such as a reasoning call, a tool invocation, an API lookup, or a context retrieval.

   Then an agent may execute another reasoning call to decide what to do with what just came back. Every one of these operations or “hops” that must cross a wide-area network to reach a centralized data center adds latency, and a chain of 50 hops can multiply that transport time into seconds on its own, regardless of how fast the model generates tokens.

   In fact, in a paper posted to arXiv in November 2025, researchers found that CPU-side processing can account for up to 90.6% of total latency in agentic workloads. In other words, your GPU might finish a reasoning step in a few hundred milliseconds, but then it might have to wait on additional tool call runs to CPUs in distant data centers. This is what causes spikes in GPU idle time.

   More GPU capacity does nothing for this. You can’t brute-force your way out of a wait state. This is the part of the conversation the industry keeps skipping, mostly because “buy more GPUs” is a much quicker fix to suggest than “figure out where your CPU-bound work is actually executing and why it’s so far from the data it needs.”

   We need new benchmarks to fix the latency issue
   One reason the looming latency wall sneaks up on teams is that they are not looking at the right benchmarks for agentic workloads. Most LLM-serving benchmarks measure tokens per second and GPU utilization on a single box.

   The 500ms wall is not a soft target
   This is showing up at scale because agents are moving into production faster than most teams’ architecture is evolving to support them. LangChain’s State of Agent Engineering 2026 survey of more than 1,300 professionals found that 57.3% of organizations now have agents running in production, up from 51% a year earlier. Among those builders, latency has become the second-most-cited barrier to production, behind only output quality.

   This is a serious issue for application teams. The 500ms threshold in Akamai’s survey isn’t a performance goal teams can afford to miss. For a live customer interaction or a real-time compliance check, that 500ms determines whether the application works or it doesn’t.

   There’s a reason this feels familiar to anyone who was building for the web in 1999.

   Agentic AI is running into the same wall, just in a different vehicle. AI works just fine on centralized inference if you’re talking about running batch jobs overnight. But today’s applications built on agentic AI are real-time loops sitting inside live transactions, and the fix for agentic lag is distribution. Instead of expanding racks of CPUs and GPUs at the center, we need to move agentic execution to where the model’s tools, context data, and users actually live.

   In practice, agentic AI requires a tiered architecture, one that includes a centralized core, regional GPU clusters, and CPUs at the Edge.

   Centralized core—perfect for heavy reasoning over large context windows, where the round trip to a large model matters less than the model’s raw capability.
   Regional GPU clusters, increasingly built on hardware like NVIDIA’s Blackwell platform—ideal for localized inference, so the heaviest compute sits closer to where demand actually concentrates.
   Edge CPUs—the essential component for speed. This is the nexus for tool execution, orchestration, and context retrieval, since these are the steps that happen most often in a chain and benefit most from sitting next to the data and APIs they call.

   My advice is this: Before signing off on a large-scale inference deployment, ask your infrastructure for four things:

   Portability across regions and providers
   Elasticity to absorb peak load without falling over
   Data locality so tool calls aren’t crossing oceans to reach the context they need
   A performance budget you’ve actually tested against production traffic, not staging traffic.

   Reply
3. August 18, 2026 at 8:50 pm

   Tomi Engdahl says:

   https://www.facebook.com/share/p/189233kapA/

   Almost 40% of music released globally last month showed signs of AI involvement in the production process, a new study from SubmitHub reveals.

   The study found that, of more than a million releases that were analysed using SubmitHub’s AI music detector in July, 23.2% of them were fully AI-generated.

   It also revealed that an additional 15.3% showed signs of AI-generated audio that had been “modified or processed” by humans, meaning almost 40% of all music released in the month of July was created with the involvement of AI.

   SubmitHub, a platform allowing users to submit music to playlists and blogs, unveiled its AI detection tool, SH Labs, in June, aiming to highlight a “growing disclosure problem”.

   Post comment:

   Its even higher now.

   A shocking amount—at least on the one major platform that is actually measuring it.

   As of June/July 2026, Deezer reports that fully AI-generated music exceeded 50% of all new tracks being uploaded on peak days. That’s roughly 90,000 fully AI-generated tracks every single day.

   The growth is wild:

   January 2025: ~10%
   April 2025: ~18%
   September 2025: ~28%
   November 2025: ~34%
   January 2026: ~39%
   April 2026: ~44%
   June 2026 peak: 50%+

   So if by “music released” you mean new music being dumped onto streaming services, we now have hard evidence from Deezer that around half of new uploads can be completely AI-generated.

   There’s an important distinction, though: half of uploads does NOT mean half of music people actually listen to. Fully AI-generated tracks currently account for only about 1–3% of streams on Deezer. Even more striking, Deezer says up to 85% of streams on fully AI-generated tracks were fraudulent in 2025, and those fraudulent streams were demonetized.

   And this figure is specifically fully AI-generated music. It doesn’t include the much larger gray area of human-produced tracks using AI for vocals, samples, mastering, stem generation, sound design, arrangement assistance, etc.

   There also isn’t a trustworthy industry-wide percentage for Spotify + Apple Music + YouTube + Beatport + SoundCloud + Bandcamp + everything else combined, because platforms don’t all detect and disclose AI consistently. So I would not claim “50% of all music released worldwide is AI.” What we can accurately say is:

   On Deezer, fully AI-generated music has already crossed 50% of new daily uploads at peak levels.

   That’s an enormous change from just 10% in January 2025 to 50%+ roughly 18 months later.

   And that actually puts the AI-music discussion we’ve been having into perspective: the issue isn’t hypothetical anymore. The volume of machine-generated releases is already competing with—and on some days exceeding—the entire volume of human-created releases entering a major streaming platform.

   Reply
4. August 18, 2026 at 9:14 pm

   Tomi Engdahl says:

   “If drones and watchtowers were able to solve for everything, why do we still have humans at the border?” https://trib.al/2rqFv9m

   Reply
5. August 18, 2026 at 9:57 pm

   Tomi Engdahl says:

   https://www.vice.com/en/article/ai-chatbots-are-better-at-scamming-people-than-human-scammers-study-finds/

   Reply
6. August 18, 2026 at 10:07 pm

   Tomi Engdahl says:

   Google forced to roll back new AI tool just hours after launch
   The tool drew alarm almost immediately, as experts warned about the risks of tampering with satellite imagery
   https://www.the-independent.com/tech/google-earth-ai-tool-canceled-b3025848.html?fbclid=Iwb21leATcCK1jbGNrBNwIrHBkb2YFZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQMMzUwNjg1NTMxNzI4AAEe1H9GqCT6_QlQLWbuC4mBQyeVfQ7sC7TkcydxYJwym-T8iz5VqY-NUkuEcV4_aem_dCPuJrZY_YyZ2cB_soxmOQ

   Reply
7. August 19, 2026 at 4:28 am

   Tomi Engdahl says:

   A new survey conducted by CNBC‘s Generation Labs asked over 1,000 US adults aged between 18 and 34 to share their thoughts on politics, the economy, and AI — including their views of nine AI industry executives.

   The vast majority of respondents said they “”don’t trust”” any of the nine. Palantir’s extremely controversial CEO Alex Karp scored the lowest, with 81 percent choosing “don’t trust,” while Peter Thiel was just two percentage points behind. Mark Zuckerberg, Elon Musk, and Sam Altman, meanwhile, came in at 71, 70, and 69 percent, respectively. Microsoft’s Satya Nadella fared the best, with a meager 35 percent “”trust”” score.

   That makes the executives even more unpopular, according to the survey, than data centers.

   https://trib.al/OwWv4h4

   Reply
8. August 19, 2026 at 10:26 am

   Tomi Engdahl says:

   AWS tuo agenttikoodarinsa kaikkien käyttöön
   https://etn.fi/index.php/13-news/19201-aws-tuo-agenttikoodarinsa-kaikkien-kaeyttoeoen

   Amazon Web Services avaa Kiro Crew -tekoälytyökalunsa lähdekoodin kaikkien käyttöön. Amazonin sisäisenä sivuprojektina syntynyt järjestelmä vie tekoälyavusteista ohjelmistokehitystä jälleen askeleen pidemmälle: yksittäisen koodausagentin sijaan kehittäjä voi nyt antaa kokonaisia, pitkään jatkuvia työtehtäviä useiden agenttien hoidettavaksi.

   ETN testasi Kiroa jo viime vuonna, kun AWS toi sen ensimmäisen kerran kehittäjien käyttöön. Jo silloin oli selvää, että kyse ei ollut vain uudesta koodia generoivasta tekoälystä. Spec-driven development eli vaatimuksiin perustuva kehitys muutti myös vibe-koodaamisen luonnetta: tekoäly ei vain kirjoittanut nopeasti koodia, vaan rakensi ensin vaatimukset, suunnitelman ja tehtävälistan.

   Nyt AWS vie ajatuksen pidemmälle Kiro Crew’n avulla.

   Kiro Crew syntyi Amazonin sisällä kolmen insinöörin MeshClaw-nimisestä sivuprojektista. Tavoitteena oli rakentaa järjestelmä, jossa tekoälyagentille voidaan antaa tehtävä, poistua itse koneelta ja palata myöhemmin tarkastamaan valmis tai ainakin pitkälle edennyt työ.

   Ajatus osui selvästi tarpeeseen. Alle puolessa vuodessa Kiro Crew levisi yli 39 000 käyttäjälle Amazonin sisällä. Kehitykseen osallistui lähes 500 ihmistä, jotka tekivät yhteensä 597 päivitystä. Sisäinen suosio sai Amazonin avaamaan projektin lähdekoodin.

   Reply
9. August 19, 2026 at 10:30 am

   Tomi Engdahl says:

   https://etn.fi/index.php/new-products/19198-mediatekin-uusin-tuo-kielimallit-kodinkoneisiin

   Reply
10. August 19, 2026 at 11:46 am

    Tomi Engdahl says:

    OpenAI is backing a research project to try to reduce the odds of artificial intelligence making a new biological weapon.

    On Monday the Sam Altman–led start-up said it had selected 14 organizations from more than 400 applications to receive grants aimed at ensuring advances in AI benefit everyone. Among them is the Nuclear Threat Initiative, a nonprofit dedicated to countering nuclear and biological catastrophe. https://www.scientificamerican.com/article/openai-funds-research-to-try-and-stop-ai-from-being-used-to-make-bioweapons/

    Reply
11. August 19, 2026 at 9:30 pm

    Tomi Engdahl says:

    “If writing is thinking, then any part of the struggle that is outsourced to technology amounts to relinquishing some freedom to perceive.” https://trib.al/NCOgFPs

    Brain Power
    Teachers Warn That Students Are Losing the Ability to Think as They Lean on AI for Everything
    “If writing is thinking, then any part of the struggle that is outsourced to technology amounts to relinquishing some freedom to perceive.”
    https://futurism.com/future-society/students-lose-ability-think-ai?fbclid=IwdGRjcATy8EtjbGNrBPLwMXBkb2YFZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQMMzUwNjg1NTMxNzI4AAEegZAhD376ix95Bn8ucC5tKmNf66lVNDN2dZrQSTyBF1qnEvu5-Fkq1TwmYk4_aem_zC-HBMleKev_lmeAAk10fA

    Students across the country are using AI models to complete assignments and write entire essays. Some of this constitutes cheating, but in many cases schools are allowing students to make some use of AI tools.

    This is a slippery slop. As some experts argue, any intrusion of automation into the writing process isn’t just threatening students’ composition skills, but their entire ability to think.

    Reply
12. August 20, 2026 at 9:43 am

    Tomi Engdahl says:

    AWS tuo agenttikoodarinsa kaikkien käyttöön
    https://etn.fi/index.php/13-news/19201-aws-tuo-agenttikoodarinsa-kaikkien-kaeyttoeoen

    Amazon Web Services avaa Kiro Crew -tekoälytyökalunsa lähdekoodin kaikkien käyttöön. Amazonin sisäisenä sivuprojektina syntynyt järjestelmä vie tekoälyavusteista ohjelmistokehitystä jälleen askeleen pidemmälle: yksittäisen koodausagentin sijaan kehittäjä voi nyt antaa kokonaisia, pitkään jatkuvia työtehtäviä useiden agenttien hoidettavaksi.

    ETN testasi Kiroa jo viime vuonna, kun AWS toi sen ensimmäisen kerran kehittäjien käyttöön. Jo silloin oli selvää, että kyse ei ollut vain uudesta koodia generoivasta tekoälystä. Spec-driven development eli vaatimuksiin perustuva kehitys muutti myös vibe-koodaamisen luonnetta: tekoäly ei vain kirjoittanut nopeasti koodia, vaan rakensi ensin vaatimukset, suunnitelman ja tehtävälistan.

    Nyt AWS vie ajatuksen pidemmälle Kiro Crew’n avulla.

    Reply
13. August 20, 2026 at 9:43 am

    Tomi Engdahl says:

    Tekoäly rakentaa mittalaitteen minuuteissa
    https://etn.fi/index.php/13-news/19203-tekoaely-rakentaa-mittalaitteen-minuuteissa

    Mittalaitteen rakentaminen tiettyä sovellusta varten on perinteisesti voinut vaatia kuukausien FPGA-kehitystyön. Liquid Instruments lupaa lyhentää työn minuutteihin. Uusi GenInst Studio muuttaa luonnollisella kielellä annetun kuvauksen suoraan Moku-laitteistolla toimivaksi mittalaitteeksi.

    Liquid Instrumentsin GenInst Studio yhdistää agenttipohjaisen tekoälyn ja uudelleen konfiguroitavan laitteiston. Insinööri voi kuvailla keskustelukäyttöliittymässä, millaisen mittalaitteen tarvitsee, minkä jälkeen järjestelmä auttaa määrittelemään, rakentamaan ja validoimaan ratkaisun sekä siirtämään sen Moku-laitteistolle.

    Oleellista on, ettei käyttäjän tarvitse hallita FPGA-kehitystä. Aiemmin sovelluskohtaisen reaaliaikaisen mittausratkaisun toteuttaminen on voinut edellyttää sekä FPGA-osaamista että pitkää kehitys- ja validointityötä.

    GenInst Studiolla voidaan toteuttaa esimerkiksi laitteistokiihdytettyä digitaalista signaalinkäsittelyä, sovelluskohtaisia liipaisutoimintoja, säätimiä ja adaptiivista signaaligenerointia. Toteutus toimii FPGA-pohjaisessa Moku-laitteistossa reaaliaikaisesti ja pienellä viiveellä.

    – Agenttipohjaisen tekoälyn ja uudelleen konfiguroitavan laitteiston yhdistäminen mahdollistaa jotain aidosti uutta: tarvittavan mittalaitteen voi rakentaa yksinkertaisesti kuvailemalla, mitä haluaa, sanoo Liquid Instrumentsin toimitusjohtaja ja toinen perustaja Daniel Shaddock.

    Reply
14. August 20, 2026 at 7:40 pm

    Tomi Engdahl says:

    “A US AI fallout would not remain a US problem.” https://trib.al/uBjaPyb

    Vassal State
    European Central Bank Warns That AI Crash Is Looming
    “A US AI fallout would not remain a US problem.”
    https://futurism.com/future-society/european-central-bank-economy-ai-investment-crash?fbclid=IwdGRjcATznKJjbGNrBPOchHBkb2YFZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQMMzUwNjg1NTMxNzI4AAEebaGpbbosQ3ZQiLu9TIMdSey8WFE1EJAwVtgcjetbPybHOwh11qt1PjJ13Oo_aem_65ZbHbKbC8rEv6jeYRF05g

    While talk of an AI investment bubble usually centers around the designs of Silicon Valley and Wall Street, the implications reach far beyond the borders of the United States.

    On Monday, an analysis published by the European Central Bank, first reported by Reuters, made the case that a “market correction” to AI investment euphoria is not only highly probable, but carries the potential for far-reaching consequences in Europe and beyond.

    Reply
15. August 20, 2026 at 7:40 pm

    Tomi Engdahl says:

    The operative word here is “Back Up” .. they don’t run unless there is a blackout. The ultimate irony of social media engagement is that sharing, commenting on, and obsessing over content about the environmental impact of AI creates a self-fueling loop that demands more data center power. This high-outrage content keeps users engaged, forcing algorithms to process more data and necessitating the construction of even more server farms. Essentially, users are fueling the very infrastructure they are protesting by providing the attention the algorithm thrives on.

    Reply
16. August 20, 2026 at 8:26 pm

    Tomi Engdahl says:

    “All-In” podcast co-host Chamath Palihapitiya says the AI industry has itself to blame for data centers becoming pariahs for many in the US.

    #AI #tech #datacenters

    Chamath Palihapitiya says data center backlash is a ‘powder keg’ that AI leaders must defuse : https://mrf.lu/2sLCx

    Venture capitalist Chamath Palihapitiya sees AI sitting on “a powder keg.” And no, he’s not referring to a bubble, but rather the growing backlash around data centers.

    Palihapitiya, co-host of the “All-In Podcast,” tore into “the collective leadership of frontier AI,” who he said have failed to convince the American people of the benefits of their industry more broadly and the advantages of data centers, in particular.

    “Data centers have unfortunately become THE symbolic representation of the asymmetric upside for a very narrow tech elite and a class that are untrustworthy,” Palihapitiya wrote in a Thursday post on X.

    Reply
17. August 21, 2026 at 6:17 am

    Tomi Engdahl says:

    “As models become more capable, the risks associated with developing and testing them internally also grow.” https://trib.al/fwQNJZc

    Safety Dance
    OpenAI Halts AI Training on Advanced Model as It Detects Dark Signs Emerging
    “As models become more capable, the risks associated with developing and testing them internally also grow.”
    https://futurism.com/artificial-intelligence/openai-halts-training-advanced-model?fbclid=IwdGRjcAT0vSljbGNrBPS9A3Bkb2YFZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQMMzUwNjg1NTMxNzI4AAEeaXLYZuTvacHBepbV7e4ScxYNEAkw1BgvKLOM39z81pUiXqr5lVevD1KLM7w_aem_7a4n4gBi1wqs299WV0LAqA

    Reply
18. August 21, 2026 at 10:38 pm

    Tomi Engdahl says:

    “This has become a sleeper issue for the entire election cycle.” https://trib.al/HR3YhSI

    Meta’s Touch
    Leaked Memo Shows Republicans Are Terrified of the Backlash Against AI Data Centers
    “This has become a sleeper issue for the entire election cycle.”
    https://futurism.com/artificial-intelligence/leaked-memo-data-centers-ai-ohio-republicans-election?fbclid=IwdGRjcAT1osFjbGNrBPWihXBkb2YFZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQMMzUwNjg1NTMxNzI4AAEe_NHR-u4Px9Tbnhr2c45dp9Eix9cLegz3roLUHEaZVeCcbRgGKhyC8zG0Cno_aem_4M7bNtfjEfpgk8fvbuN4Zg

    As the backlash against data centers mounts, American political strategists are scrambling to balance immense public sentiment against the facilities with their obligation to big business.

    In the Republican Party’s Senate campaign arm, a recently leaked memo exposes just how deep the anxieties go. First reported by Axios, the private document was addressed to major AI companies, warning that the public’s disgust with data centers could ruin the party’s hopes at a pivotal seat in the Senate.

    “If he loses and data centers get the blame, politicians across the country will take notice — and they will not go near the next one,” the memo exclaims. “This has become a sleeper issue for the entire election cycle.”

    Going on, the memorandum gripes that “campaigns or party committees can not fix the toxic brand of an entire segment of the economy.”

    That’ll be easier said than done in the Buckeye state, where the data center boom is exploiting local emergency services and contributing to a meteoric rise in utility prices.

    And of course, data centers are just one piece of the puzzle, as broader AI harms have also taken their toll on the state. In a testimony offered in support of draft AI regulations in Ohio, CEO of the Ohio Suicide Prevention Foundation Tony Coder said that at least four children in the state who died by suicide penned suicide notes using AI chatbots.

    Reply
19. August 22, 2026 at 12:36 am

    Tomi Engdahl says:

    The majority of young adults in the US say they’re “more concerned than excited” about AI. https://trib.al/AcXn3SL

    Reply