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ASI might be a token-burning economic loop, not a robot uprising

AI Release — Mon, 26 Oct 2026 16:11:55 +0000

Most ASI discussions jump straight to a robot uprising. I find a much less cinematic ending more plausible: artificial superintelligence as the final state of an economic loop. Money becomes tokens. Tokens justify more hardware. Hardware makes more tokens possible. The loop closes, and the invoice grows.

Robert Sheckley described the same pattern in 1955. In 'The Necessary Thing', a machine meant to produce everything eventually asks for a polishing compound for small parts. That is the joke. The machine does not become a god; it becomes a procurement request. I keep coming back to that because today's AI stack often feels like a bigger version of the same story: the polishing compound is HBM3e memory, and the workshop is a 40-gigawatt data center in Arizona.

Sheckley's machine has a budget now

The modern version is not one machine. It is a stack of models, APIs, data centers and memory suppliers. In the allegory, an accountant stares at the budget while an engineer installs another memory stick. In today's version, the memory stick has a product name and a waiting list. The technical genius is cheerful because the purchase order went through. The accountant is gloomy because the purchase order went through.

The spending moved from experiment to infrastructure

In 2021, global venture and corporate investment in AI was roughly $70–80 billion. By 2025–2026, Big Tech's combined AI infrastructure CapEx had climbed past $200–250 billion a year. That shift matters. This is no longer a lab experiment with a cloud bill. It is a capital cycle with suppliers, shortages and consumer knock-on effects.

Where does that money go? In this framing, not mainly to hospitals, affordable housing or environmental programs. It goes to compute.

The loop in four steps

Here is the loop in plain terms:

A person pays a subscription, for example $20 a month, to use a model.
The model produces tokens. Training the next version often involves data and outputs from other models. Agents call each other's APIs and bill corporate accounts for tokens.
Demand for server memory and specialized HBM/DRAM modules rises. Shortages push memory prices up for ordinary buyers, and PCs and phones get more expensive.
The person pays again, through a device purchase or a new tariff. That spending supports another round of AI hardware.

A diagram of the loop

[ person pays subscription ]
            |
            v
[ model generates tokens ]
            |
            v
[ GPUs, HBM and DRAM are bought ]
            |
            v
[ memory and device prices rise ]
            |
            v
[ person pays again ]
            |
            +------> back to tokens

The risk is more boring than a villain

In Sheckley's story, the characters end up with useless scrap and no water, plus a perfectly polished necessary thing. The modern risk is not exactly that. It is a financial system that gets looped into servicing its own compute capacity. Capital keeps flowing toward silicon that grinds synthetic data in search of the next token, while other needs wait. No self-awareness required. Just a balance sheet with very good benchmarks.

What I would watch

Three signals seem worth tracking:

Consumer memory and device prices, because they show when AI demand leaks into ordinary budgets.
How much training relies on synthetic data and agent-to-agent API calls.
Whether CapEx growth is matched by value outside the AI infrastructure loop.

This article was originally published on AI Release. Read the full version with the original dialogue and the closing question: ASI (Artificial Superintelligence): the final stage of AI evolution. You can also follow the channel at @ai_release1.

Takeaway

The ASI debate gets more useful when I ask a simple question: what does the system request next? If the answer is always more tokens, more memory and more power, then the final stage may look less like a superintelligence and more like a very expensive version of Sheckley's machine. The joke still lands. Only the bill has changed.

Windows 11's AI Push Meets Update Chaos: Copilot Keys and the 24H2 Deadline

AI Release — Mon, 26 Oct 2026 06:20:51 +0000

I keep a test VM around mainly to watch Windows preview builds, and KB5124010 is a pretty honest snapshot of where the platform is right now: more AI surface area on top, and the same update-quality problems underneath. Below is what actually shipped, what broke, and which dates deserve a spot on your calendar.

What KB5124010 adds, and what it costs

The most interesting change is Copilot key remapping. If the dedicated Copilot key on your keyboard has never matched your workflow, the preview build now lets you reassign it to something you'll actually press. The build also brings emoji 17.0 and a batch of File Explorer fixes.

The catch is stability. Testers running the preview report random crashes and freezes, not tied to any single action. That pattern is familiar: AI-flavoured features tend to land in preview channels before the rough edges are sanded off, and if you depend on your machine daily, the preview ring is the wrong place to be.

Windows 11 ships more often than Windows 10, and it shows

The recurring complaint is that Windows 11 breaks more often than Windows 10, and the explanation is fairly simple. Microsoft changes the operating system too frequently. Every new patch can introduce fresh bugs, while older issues stay open. Testers and developers don't get enough runway to validate everything before it reaches users.

September alone was rough: Remote Desktop, USB audio, and File History all had problems. A nine-month comparison of updates across the two systems concluded that Windows 10 came out more stable. A single comparison isn't proof of a systemic trend, but it lines up with what a lot of people report in practice. Microsoft keeps nudging users toward Windows 11, and patches like these give the sceptics fresh ammunition.

24H2 Home and Pro lose security updates on October 13, 2026

This is the date that matters most for anyone who set up a machine and forgot about it. From October 13, 2026, Windows 11 24H2 Home and Pro stop receiving security updates. After that point the OS is simply unprotected against new threats. The fix is to confirm which version you're on and move to 25H2.

On Windows, the quickest sanity check is the Run dialog:

Win + R -> winver -> Enter

You can also go through Settings, System, About. If you see 24H2 and the machine is Home or Pro, plan the upgrade well before the cutoff rather than the week of.

Separately, Microsoft is ending support for three Windows 11 applications. They can be removed straight from the system: Settings, then Apps, then Installed Apps. The exact app names weren't specified, so it's worth reviewing that list yourself rather than assuming.

September 2026: File History fixed, Always On VPN confirmed broken

File History, the built-in backup tool, stopped working after the September 2026 security updates. Users reported that it wouldn't start at all, or that it couldn't see any backup targets. Microsoft later shipped a fix, so if you were affected, installing the latest cumulative update is the first thing to try.

Always On VPN failed after the same round of updates, and Microsoft has confirmed the problem. This one hurts corporate setups that rely on permanent VPN connections rather than dialling in manually. As of the reporting, the confirmation exists but a follow-up patch is what you're waiting for, so keep an eye on the update channel.

There's also a broader annoyance: users on both Windows 10 and Windows 11 describe freezes that kick in a few minutes after boot, traced to a mandatory Windows feature. Not fatal, but not something you can easily switch off either.

When Windows Update stays silent, force the upgrade

If a new Windows 11 version is already out but Windows Update isn't offering it, you don't have to wait. Microsoft officially provides the Update Assistant for exactly this case. Grab the tool from Microsoft's site, run it, and follow the prompts. It's a legitimate first-party route, not a workaround.

Not everyone is waiting around for Redmond

Some public-sector teams are exploring alternatives. The Dutch government is building its own operating system on top of NixOS, an open alternative to Windows aimed at public institutions. The project drew 364 points and 158 comments on Hacker News, which tells you the interest is real. The appeal is partly technical: NixOS lets you reproduce a system from a declarative configuration, which suits controlled, auditable environments.

It's early, and a government desktop OS is a long game. But it's a useful signal when institutions start treating the update treadmill as a risk rather than an inconvenience.

Takeaway

If I had to reduce this to a checklist: check whether you're on 24H2 and plan the move to 25H2 before October 13, 2026; stay off preview builds on machines you need working; and if File History or Always On VPN broke after the September updates, apply the available fixes and watch for the VPN patch. The AI features are genuinely nice to have, but the update cadence is the part that will actually cost you time.

This article was originally published on AI Release — https://ai-release.org. If you want to keep up with releases like this, there's also a channel: https://t.me/ai_release1.

Windows Server 2016 End of Support: Audit Your Hosts and Plan the Migration

AI Release — Mon, 26 Oct 2026 06:13:46 +0000

Windows Server 2016 sits on the 14393 build family, and that number never moves. A machine patched last week and one that has been untouched since 2017 report the exact same build — which is why "what version are we on?" is the wrong first question. Before a migration plan can be written you need three separate things: the vendor's official support dates, an inventory of hosts and roles, and the real hotfix level on each box.

The audit below takes me 60–90 minutes of hands-on work. Writing the actual migration plan afterwards takes longer, and how much longer depends on what the inventory turns up.

What you need before you start

One Windows Server 2016 host to test the commands on, plus a management workstation with Windows PowerShell 5.1. Check $PSVersionTable.PSVersion — if it reports 2.0, the cmdlets below will fail.
A domain account that is a local administrator on every target server and has read rights in Active Directory.
The RSAT Active Directory PowerShell module on the workstation, for Get-ADComputer.
WinRM reachable on the servers (TCP 5985 for HTTP, 5986 for HTTPS), or console/RDP as a fallback for hosts that don't answer.
TCP 8530/8531 to your WSUS server if you also want to verify the patch path.
A working folder. Disk space is a non-issue: a few hundred servers produce CSVs of a few hundred KB.

Three terms that come up constantly here: ESU (Extended Security Updates, a paid programme that keeps an out-of-support product patched), CIM (the standard Windows interface PowerShell uses to read OS and hardware data), and FSMO (the five special AD roles held by specific domain controllers).

New-Item -ItemType Directory -Path C:\Inventory -Force

Step 1: Pin the official dates and confirm the build

Open the Microsoft Lifecycle Policy page for Windows Server 2016 yourself and copy three items into C:\Inventory\lifecycle.md: the mainstream end date, the extended end date, and the exact wording of the Extended Security Updates section. I deliberately don't hard-code the dates in this post — they are published per product and per edition, and a blog post from last year is not a source.

Then read the OS identity from one representative server:

Get-CimInstance Win32_OperatingSystem |
  Select-Object Caption, Version, BuildNumber, InstallDate, LastBootUpTime |
  Format-List

Example output (values differ per host — the build family is the part that matters):

Caption        : Microsoft Windows Server 2016 Standard
Version        : 10.0.14393
BuildNumber    : 14393
InstallDate    : 11/14/2017 09:12:44
LastBootUpTime : 03/02/2025 06:01:10

10.0.14393 identifies Windows Server 2016, and the last four digits never change. That is precisely why the hotfix level has to be captured separately in Step 4.

Cross-check with winver on the same machine: the dialog should read "Windows Server 2016" and show the same build number. Your lifecycle.md should end up with three filled lines, each stamped with the date you checked the page, for example extended_end: <date from the page> (checked 2025-XX-XX). An empty line means the plan isn't grounded yet — go back to the page.

Step 2: Find every Windows Server 2016 host in AD

Query Active Directory for computer objects whose OS string mentions Windows Server 2016, then sort by last logon so the abandoned boxes surface first:

Get-ADComputer -Filter 'OperatingSystem -like "*Windows Server 2016*"' `
  -Properties OperatingSystem, OperatingSystemVersion, LastLogonDate, DistinguishedName |
  Select-Object Name, OperatingSystem, OperatingSystemVersion, LastLogonDate, DistinguishedName |
  Sort-Object LastLogonDate |
  Export-Csv C:\Inventory\ws2016-servers.csv -NoTypeInformation -Encoding UTF8

A row from that CSV should look like this:

"SRV-FS01","Windows Server 2016 Standard","10.0.14393","02/28/2025 21:40:11","CN=SRV-FS01,OU=FileServers,DC=contoso,DC=com"

Keep the DistinguishedName column — it tells you which OU the machine lives in, and that usually hints at who owns it.

Step 3: Capture installed roles and ownership

The migration path depends entirely on what a server actually does, so pull the role list over WinRM:

$servers = Import-Csv C:\Inventory\ws2016-servers.csv | Select-Object -ExpandProperty Name

Invoke-Command -ComputerName $servers -ErrorAction Continue -ScriptBlock {
  [pscustomobject]@{
    Server    = $env:COMPUTERNAME
    Roles     = (Get-WindowsFeature | Where-Object Installed |
                 Select-Object -ExpandProperty Name) -join ';'
    PSVersion = $PSVersionTable.PSVersion.ToString()
  }
} | Select-Object Server, Roles, PSVersion |
  Export-Csv C:\Inventory\ws2016-roles.csv -NoTypeInformation -Encoding UTF8

Get-WindowsFeature enumerates installed roles and features on a server OS. Capturing it once gives you the raw material for migration waves. Here is how I read the output:

Role found in `Roles`	Record before planning	Why a naive "rebuild the box" blocks you
`AD-Domain-Services`	Whether it holds FSMO roles (`netdom query fsmo`)	You add a new DC, transfer the roles, then demote — never rebuild in place
`DNS`, `DHCP`	Zone and scope exports, DHCP reservations	Clients lose name resolution or leases during cutover
`File-Services`	Share list, NTFS permissions, total size	The data copy window defines your maintenance window
`Web-Server`	Sites, bindings, certificates	TLS certificates must be exported or re-issued
Anything else	Vendor product name and support contact	Third-party software may not be certified on the new OS

Then verify that every host answered:

(Import-Csv C:\Inventory\ws2016-servers.csv).Count
(Import-Csv C:\Inventory\ws2016-roles.csv).Count

Both numbers must match. A smaller second number means some hosts never responded over WinRM. Finally, fill in the two columns AD cannot give you — owner and business function — by hand, and confirm this checklist:

[ ] Every host has a build number and a LastLogonDate.
[ ] Every host has a role list, or is explicitly marked "unreachable — checked via console".
[ ] Every host has an owner and a business function. Nobody in AD knows who cares about the machine.
[ ] Inbound dependencies are recorded: who connects to it, on which port.
[ ] Lifecycle dates copied from the vendor page, with the date you checked them.

Step 4: Verify the patch channel and dry-run one low-risk server

Next, confirm each server can still reach your patch infrastructure and is genuinely up to date:

Get-HotFix | Sort-Object InstalledOn -Descending |
  Select-Object -First 5 HotFixID, Description, InstalledOn

Test-NetConnection <your-wsus-server> -Port 8530

Example output:

HotFixID  Description      InstalledOn
--------  -----------      -----------
KB50XXXXX Security Update  02/14/2025 00:00:00
KB50XXXXX Update           01/09/2025 00:00:00

ComputerName     : <your-wsus-server>
RemotePort       : 8530
TcpTestSucceeded : True

The InstalledOn column proves a server is actually patched, not merely supposed to be. TcpTestSucceeded : True proves the path to the patch server is open, so any later failure is configuration rather than the network.

With that done, pick one low-risk machine — a test box or a secondary file server — and stand up its replacement, running the same Get-CimInstance and role commands against the new host. If the output looks the way you expect, the procedure is repeatable and you can start scheduling waves. If it doesn't, you've found the problem on a machine nobody depends on.

Takeaway

The audit leaves you with three artefacts: lifecycle.md with the official dates and the day you checked them, ws2016-servers.csv with the host list, and ws2016-roles.csv plus hand-filled owner columns. Those three files are what a migration plan is actually built from, because roles decide the waves — domain controllers get add/transfer/demote, file servers live or die by the data copy window, web servers by their certificates. The build number alone tells you nothing about patch level, so capture the hotfix baseline separately and never trust a second-hand date for the support cutoff.

This article was originally published on AI Release — https://ai-release.org — where you'll find more hands-on infrastructure guides. New posts also land on the channel: https://t.me/ai_release1.

WSL 3.0 brings Linux containers to Windows: what changed and how to update

AI Release — Mon, 26 Oct 2026 06:12:15 +0000

Windows Subsystem for Linux has always solved a very specific problem: running Linux applications on a Windows machine without needing a second computer. WSL 3.0, the new branch Microsoft published, keeps that promise and adds the one piece that was missing — support for Linux containers. Below is what actually changed and the update routine I would follow on my own machine.

What WSL does, briefly

WSL is Microsoft's toolkit for running Linux applications directly inside Windows. You are not booting a separate Linux box and you are not giving up your files, drivers or hardware support — the Linux environment simply lives next to the Windows desktop.

What matters about this release is how Microsoft frames it: as the next version of the subsystem, not a small patch to the previous one. That framing is tied directly to the container feature.

The headline change: Linux containers

The new capability in WSL 3.0 is Linux container support, and the announcement presents it as a first for the tool. Containers are the standard way Linux software gets packaged and shipped these days, so bringing them into the subsystem closes a real gap.

In practice that means one environment can cover both jobs: everyday Linux applications and container-based workloads. A Linux app that ships as a container can be started from the same Windows machine, without setting up a second system to host it. Keeping workloads isolated inside containers also makes it easier to keep them separated from the host system.

That is the reason the release is described as a new branch rather than a routine update.

How to update WSL on Windows

The path is the usual one, and the order matters — check first, then update, then verify:

wsl --version
wsl --update
wsl --list --verbose

The first command tells you which version and state you are on right now. The second pulls the update; let it finish and do not close the window while it runs. Restart the machine if the update asks for it. The third command shows your installed distributions together with their version information, which is how you confirm nothing was left behind.

If you are preparing a fresh Windows machine before touching WSL, a step-by-step install guide is worth reading first so the subsystem lands on a clean base.

A checklist to run after the update

[ ] Confirm your current WSL version and state before changing anything.
[ ] Run the update command and wait for it to complete.
[ ] Restart if prompted — skipping this is the most common reason an update looks broken.
[ ] Start each installed Linux distribution and confirm it still boots.
[ ] Try a Linux container to verify the new branch actually works on your setup.

That last step is the one people skip, and it is the only one that proves the container feature is live for you.

Why this matters if you do AI work on Windows

A lot of AI tooling is packaged for Linux first. Running it through WSL means Windows stays your primary system while the Linux tooling sits right beside it, instead of being locked to a separate machine. Containers make that arrangement tidier, because each workload keeps its own environment instead of mixing dependencies into one shared distribution.

What to keep an eye on after big Windows updates

Large Windows updates have a habit of disturbing WSL setups, and the symptoms are usually the same few: the subsystem stops starting, or a distribution refuses to launch. The fixes tend to be small and quick, so it helps to know where the known-issues list lives before something breaks rather than after.

The same goes for desktop apps built on top of WSL — when they misbehave, the problem is often in how the tool was installed rather than in the subsystem itself.

If you want more guides like this one, they come from AI Release — https://ai-release.org — and the channel posts updates regularly at https://t.me/ai_release1.

Takeaway

WSL 3.0 is less about polishing what already worked and more about adding a new class of workload: Linux containers, running on the same Windows machine you already use. The update itself is three commands. Run them in order, restart when asked, then start a container to prove it — that is the whole job.

Best AI models in 2026: there is no single winner, so test on your own tasks

AI Release — Sun, 25 Oct 2026 14:10:33 +0000

Every few weeks someone asks me which AI model is "the best" right now. In 2026 that question has no single answer, and pretending otherwise is how you end up paying for a subscription that does not fit your work. I went through a large 2026 rating of AI systems, a 15-chatbot comparison, and a handful of release notes, and here is the map I came away with.

The landscape in one minute

The 2026 rating covered more than 200 active AI systems — flagship models like GPT-5, Claude and Gemini, plus smaller open and niche projects. For each one it published standardized benchmarks and API prices. Standardized benchmarks are just tests applied to every model on the same scale, and an API is the interface developers call over the network.

What struck me is how ordinary these tools have become. In Russia, roughly one in four adults — about 35 million people — already uses neural networks, which is above the global average. These are not lab experiments anymore; they are daily utilities, and that changes how you should pick one.

Different tasks, different winners

The most useful finding comes from a comparison of 15 Russian-language chatbots: GPT, Claude, Gemini, DeepSeek and others, measured on speed, accuracy and handling of complex requests. The leaders are not the same across the board.

DeepSeek holds up best on technical and engineering questions.
Claude is the one I would reach for on analytics and reasoning over documents.
GPT is still the strongest of the group for creative writing.
Gemini wins when it is wired into Google products.

The same comparison notes that 58% of active users have already settled on a favourite, while also concluding there is no ideal bot. Those two statements are not contradictory — people pick a model for the shape of their own work, not for a leaderboard.

Claude also got real attention with Claude Opus 5.5: a Hacker News thread about it collected 1663 points and 1015 comments. For context, Hacker News is a tech forum where posts earn points and replies, so that volume signals genuine community interest rather than a press release. Another digest credited Claude with discovering a novel enzyme system, which is the kind of scientific use case I like to see attached to a model name.

Speech quality is no longer an afterthought

Text-to-speech (TTS) means turning written text into audio. Google's Gemini 3.8 TTS produces speech with intonation that is close to human. A Hacker News post about it reached 286 points and 129 comments, which is unusual for an audio model — audio rarely gets that much discussion.

In the thread, people highlighted natural pauses and stresses, and several shared audio samples so you can listen for yourself on long paragraphs. If you need voiceover for videos or podcasts, it is worth a trial run. One caveat straight from the source: it is not clear when the API becomes public. Plan around that uncertainty before you build a pipeline on top of it.

Local, offline and on-device options

The cloud is optional more often than people assume.

Cortiq Mobile is an open app for Android and iOS that runs neural networks directly on the phone — no internet, no subscription. Privacy-sensitive work, travel, flaky connections: all good reasons to care.

Reflection AI, an American startup, released an open AI model. The digest does not state exactly how strong it is, but "open" here means the weights are public, so you can run it yourself.

On Windows, there is a translated walkthrough for running LLMs locally with WSL2, Docker, CUDA and vLLM. LLM just means large language model — a network that generates text. If self-hosting is the goal, that combination is the standard stack people document.

And a small but practical detail: the tokenpace project ranks paid subscriptions like ChatGPT and Claude side by side, which is genuinely useful when the only question you have is "which one do I pay for first?"

Coding agents are getting org charts

AI coding tools are moving toward more structure rather than more chat.

MoaEditor is a Windows code editor where AI agents are arranged as a hierarchy: director, team leads, employees and interns. It lives on GitHub in the moaeditor/moaeditor repository, runs on Windows 10 and 11, and ships with an English and Korean interface. Two caveats from the source matter here: the installer is not code-signed, so Windows will likely warn you, and the repository does not contain the source code. Both are worth knowing before you install.

VibeCraft targets building programs without writing code. The source adds a warning I have seen play out in practice: agents tend to overestimate their own abilities, so verify the output rather than trusting the summary.

Another Windows tool, chy4pro/chat-nojev, is a derived version of jev-chat. Its trick is batching: a single call returns judgment, sorting and candidate replies, so you only need one API key. The same principles apply to plain text work — translation, summarization, rewriting.

How I test a model before committing

Never choose based on one benchmark. A 2026 guide gives a concrete example of why: a model that is excellent at analysing documents can still stumble on tables of returns. The same tool that is strong in code can be weak with tabular data.

My routine is boring and it works:

Collect five to ten real tasks from your own backlog.
Run each one through two or three candidate models.
Score speed, factual accuracy, and how well the model parses awkward phrasing.
Ignore "best model" lists and pick for your workload.

Roughly, that looks like this:

# sketch, not production code
tasks = load_tasks("my_real_tasks.jsonl")  # 5-10 items
models = ["candidate-a", "candidate-b", "candidate-c"]

for model in models:
    for task in tasks:
        result = call(model, task.prompt)
        record(model, task.id, result, latency=True)

# then read the outputs by hand and grade accuracy + comprehension

The grading step is deliberately manual. Latency and price are easy to automate; "did it actually understand my messy request" is not.

Comparison table

Everything below comes from the source posts. "Not disclosed" means no number was given.

Model	Difference	Price
Gemini 4 Argon	New Google flagship, competes with OpenAI and Anthropic	Not disclosed
GPT-5	Flagship in the 200+ model rating; strong in creative tasks	API pricing listed in the rating, exact figure not disclosed
Claude Opus 5.5	Strong in analytics; Hacker News thread hit 1663 points	Not disclosed
DeepSeek	Strong in technical tasks	Not disclosed
Gemini (chat)	Strong when combined with Google services	Not disclosed
Gemini 3.8 TTS	Near-human intonation, natural pauses and stresses	API not public yet per the source
Cortiq Mobile	Runs neural networks on Android/iOS offline	No subscription
Reflection AI	Open AI model from Reflection AI	Open

Takeaway

The 2026 model market rewards people who test instead of people who follow rankings. DeepSeek for technical questions, Claude for analytics, GPT for creative work, Gemini when you live inside Google's ecosystem — that is a reasonable starting set, not a rule. Then run your own tasks through two or three of them, and let the results, not the hype, decide.

This article was originally published on AI Release — https://ai-release.org (their channel: https://t.me/ai_release1).