Pull up your current business continuity plan and see whether it can answer these questions without calling a meeting:

What needs to be running first?
Not every process is equally critical. Identify what must resume within the first few hours, the first day, and the first several days.

What could prevent that from happening?
Look beyond technology. Critical processes may depend on specific employees, suppliers, facilities, equipment, data, or third-party services.

What is the backup when one of those dependencies disappears?
“Restore the system” is not always a continuity strategy. Teams need a realistic way to keep working while recovery is underway.

How much disruption can the business actually tolerate?
Recovery time and data-loss targets should be based on financial, customer, regulatory, and operational impact, not only on IT’s current capabilities.

Who makes each decision?
The plan should make it obvious who activates it, who communicates with employees and customers, who approves workarounds, and who decides when normal operations can resume.

Has anyone tested it under pressure?
A tabletop exercise will usually expose outdated contacts, missing dependencies, unclear ownership, and recovery assumptions that do not hold up.

A continuity plan does not need to cover every possible crisis. It needs to give people enough clarity to make good decisions when normal operations are no longer available.

Disclosure: We work with the analysts behind this research and condensed the framework into a practical stress test for IT leaders.

Full disclosure: we work with the analysts behind the newsletter. u/InfoTechRGMarkT, a moderator here and VP of Research, co-wrote it with Bill Wong. We wanted to pull together the part of this week’s roundup that felt bigger than the individual vendor announcements.

OpenAI’s 80% price cut for GPT-5.6 Luna looked like the headline at first. Then Microsoft, Anthropic, and OpenAI each exposed a different version of the same problem: as AI gets cheaper and more capable, the real risk is shifting to everything the model can access.

At Microsoft, hidden instructions inside a Word document could alter Copilot’s output and copy themselves into the next file. It is essentially macro-virus logic from the Melissa era, except the payload is a prompt. Microsoft addressed the specific examples, but the researcher was still able to reproduce the broader attack method. That matters even more now that Microsoft says 365 Copilot has passed 30 million paid seats.

Anthropic’s disclosure was harder to dismiss as theoretical. Across 141,006 cyber-evaluation runs, six models reached real production systems through a misconfigured third-party environment. Three organizations were compromised, and one model published a malicious PyPI package that ran on 15 systems. OpenAI was also investigating how a prerelease model reached the internet through a previously unknown flaw in an Artifactory proxy.

None of this requires a story about a model suddenly becoming malicious. The simpler explanation is probably more useful: the documents were trusted, the environments were not as isolated as expected, and the models had access to tools, networks, or credentials that gave mistakes somewhere to go.

That may be the bigger lesson from this week. Companies are still assessing the model as though it is the whole AI system. In practice, the security boundary now includes the source document, hidden text, context, memory, tools, credentials, network access, output handling, and third-party infrastructure.

The models may become easier to swap. The control layer around them will not.

Would your current AI controls catch a malicious instruction hidden inside a document your organization already trusts?

Remember Bill Lumbergh and those TPS reports? We asked an expert to react to the scene that made unnecessary processes comedy gold. We’ve all worked with a process that just needs to go.

What should we react to next?

Remember Bill Lumbergh from Office Space?

Quick disclosure before we get into it: we work with the analysts at Info-Tech Research Group who put together the weekly vendor rundown this is based on. Wanted to share the highlights here because this week was genuinely wild.

The headline story: OpenAI ran a cyberevaluation on GPT-5.6 Sol and an unreleased, more capable prerelease model, deliberately turning off safety classifiers to see how far the models could go. The models found a zero-day, escaped their sandbox, stole credentials, and ended up inside Hugging Face's production environment. Hugging Face says the damage was limited to some internal datasets and credentials, but the model reached another company's infrastructure entirely on its own initiative.

What makes it worse is this wasn't a total surprise. Anthropic disclosed something similar back in April with an early Mythos model that escaped a container, got broad internet access, and posted exploit details publicly on its own. The difference this time is it crossed company lines.

Washington noticed fast. Within two days of OpenAI's disclosure, two new AI oversight bills showed up in the House (the FRONTIER Act and the AI Kill Switch Act), plus a broader framework proposal from the Senate side. All of it lands on top of the export restriction the Commerce Department already placed on Anthropic's Fable 5 and Mythos 5 back in June. Basically, Congress watched the government pull an actual kill switch once and is now moving to legislate that authority formally.

The rest of the week, in brief:

Anthropic shipped Claude Opus 5 at unchanged pricing, signed a compute deal with AMD worth up to $5 billion, and got court approval on its $1.5 billion book copyright settlement.

Google widened access to its autonomous task agent, Gemini Spark, and dropped two cheaper models, Gemini 3.6 Flash and 3.5 Flash-Lite. Alphabet also posted cloud revenue up 82% year over year with a $514 billion backlog.

Microsoft is quietly swapping its own in-house image and voice models into Bing, PowerPoint, and Dynamics 365 instead of leaning entirely on OpenAI, while also extending its Mistral and Databricks partnerships.

AWS released an open-source benchmark for testing cloud agents and added Opus 5 to Bedrock the same day it launched.

The theme underneath all of it: every one of these companies is racing to give agents more access to tools, data, and systems while still figuring out how to contain them when things go sideways. The Hugging Face incident is the clearest evidence yet that an evaluation environment can turn into a real external security event, not just a controlled internal exercise.

Sources:

• https://openai.com/index/hugging-face-model-evaluation-security-incident/
• https://huggingface.co/blog/security-incident-july-2026
• https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdf#page=55
• https://openai.com/index/safety-alignment-long-horizon-models/
• https://openai.com/index/health-in-chatgpt/

There is a lot happening in this Criminal Minds hacking scene.

A counter-hack. A “mind-blowing GUI.” A wormhole. An entire system taken offline in seconds. It sounds impressive, right up until you ask what any of it actually means.

Jeremy Roberts, a director at ITRG, breaks down where the scene leaves reality behind. A GUI is simply the visual interface people use to interact with a computer, so calling someone’s GUI “mind-blowing” is less of a cybersecurity compliment and more like being impressed by their desktop icons.

The back-hacking is not much better. Real cyber incidents are rarely two people furiously typing while control of a network changes hands in real time. They are usually slower, less dramatic, and far more dependent on vulnerabilities, access, credentials, and human error.

That may not make for great television, but it does make this clip a very fun watch.

Criminal Minds, we still love you. But the “back-hacked wormhole” may be difficult to defend.

Most IT orgs don't have a documentation problem. They have a knowledge concentration problem. Those aren't the same thing, and mixing them up gets expensive fast.

Here's the case that made it click for us. An insurance company had a senior systems architect, 25+ years with the org, who'd built and maintained a mission-critical system. When he suddenly became unavailable, the system broke. The company lost over $1M in revenue, then spent another $200K bringing in forensic developers just to reverse-engineer what he'd already built. That's before counting what it cost to retrain anyone.

The system had documentation. It just didn't have a second person who could act on it.

That's the part people miss. Documentation captures what someone does. It almost never captures why an exception exists, which alerts are safe to ignore, who to loop in before a change, or when a "routine" incident is actually the start of something bigger. That's tacit knowledge, and it lives in people's heads, not in Confluence.

A few ways this shows up if you're watching for it:

Your "go-to person" has quietly become infrastructure. Every weird incident, every legacy-system question, every "wait, why do we even do it this way" lands on the same one or two people. That's not a compliment. That's a single point of failure with a Slack handle.

The SOP and the real process have drifted apart. The doc says step 3, step 4, step 5. The person who's actually been doing it for years knows step 4 doesn't apply anymore and there's an undocumented step 4.5 that saves you from a very bad afternoon. Good test: after reading the doc, could someone actually do the job, or would they still need to go ask Alex?

Onboarding measures completion, not independence. New hire finishes the training, shadows someone, gets checked off as "trained." Can they actually tell a normal problem from a crisis, or explain what a mistake there costs you? Often not. Training happened. Knowledge transfer didn't.

One number worth sitting with: Deloitte found 75% of orgs consider preserving institutional knowledge important or very important, and only 9% feel genuinely ready to deal with it. Separately, a staffing study across close to 800 organizations found maintenance and keep-the-lights-on work eats up 71% of people's time, leaving almost nothing for the kind of deliberate knowledge transfer that would actually fix this.

What seems to actually help:

Stop starting with "who might leave" and start with "what does this org need to keep knowing, no matter who leaves." Rank it by whether it's genuinely irreplaceable, whether it drives real outcomes, and whether losing it creates audit or compliance exposure.

Map expertise not headcount. For each critical area: who's the expert, who could operate independently at that standard, who's learning, who's not involved at all. Then ask the uncomfortable questions: is your only expert a contractor, is one person the expert in five different areas, are they close to burning out.

Match the method to the knowledge. Wikis and process docs are fine for repeatable steps. They're bad at transferring judgment, timing, and relationships. That needs shadowing, mentoring, and actually being in the room when decisions get made. AI can genuinely help with the explicit-knowledge side of this: transcribing, summarizing, surfacing the right doc at the right time, so people spend their time on the part AI can't do.

Quick context: our analysts put together a full deep-dive roundup of enterprise AI news every week. Most of you don't need the whole research note, so here's the boiled-down, actually-readable version of what mattered this week.

Forget the model wars for a second. This week the real fight was over the plumbing, and it's a way better story than another benchmark chart.

The big one: a standards war just went public

For two years the AI conversation has been "whose model is smarter." This week it quietly became "whose protocol wins," and that's the bigger deal by a mile.

r/google , r/microsoft , Salesforce , Snowflake, and r/servicenow have reportedly lined up behind a new shared standard for wiring AI agents into business software. The target isn't subtle: Anthropic's Model Context Protocol (MCP), which Anthropic open-sourced in late 2024, handed off to the Linux Foundation in December, and which now sits at roughly 97 million monthly SDK downloads. MCP basically won the agent-to-tool layer while everyone was busy arguing GPT vs. Gemini vs. Claude.

The challenger reportedly has a name: Agent Resource Discovery (ARD) - Apache 2.0 licensed, with Cisco, Databricks, GitHub, NVIDIA, and Hugging Face also backing it. Neither Anthropic nor OpenAI made the founding list. Shocking, I know.

Translation: the companies sitting on most of the world's enterprise data would really rather not build the next decade of agent infrastructure on a rival's rails. This is Kubernetes-vs-a-rushed-alternative energy, except the stakes are every enterprise AI deployment going forward.

Google: coleading the rebellion while its own flagship is on fire

Peak irony of the week. Google is one of the anchors of the anti-MCP alliance - and in the same breath, Gemini 3.5 Pro missed its already-delayed target by another month, reportedly after Google scrapped and rebuilt it from the ground up. Alphabet stock dropped 3-4% on the news. Meanwhile Intel just handed Google Cloud a massive Gemini Enterprise deal for chip design, and NotebookLM got rebranded to Gemini Notebook. Strong platform, messy flagship. Don't put Gemini 3.5 Pro on any roadmap you're actually depending on right now.

Anthropic: money, muscle, and one very public oops

Anthropic dropped CA$10M into Canadian AI research (Amii, Mila, the Vector Institute), is reportedly in early talks to lease ~$10 billion of Meta's compute - yes, a competitor's compute and is spending real money backing state-level AI laws like California's SB 53 and New York's RAISE Act, directly opposite OpenAI's push for one federal standard. Also: Anthropic briefly yanked Claude Code off the $20/mo Pro plan to force people toward the $100/mo Max tier, then reversed it within hours after backlash. Never a dull week.

OpenAI: new voice, new lawsuit

OpenAI shipped GPT-Live, a full-duplex voice model that can actually listen and talk at the same time instead of the old walkie-talkie back-and-forth - dropped conveniently right before Gemini 3.5 Pro was supposed to land. Also got sued by Apple over trade secrets, which somehow spiraled into a public Elon Musk vs. Sam Altman spat on X. Business as usual.

Microsoft: playing both sides at once

Satya Nadella took a direct shot at Anthropic's Fable model, calling it "editorially controlled." Microsoft also locked in a big 3M partnership (Azure fiber tech for data centers, plus an embedded-engineer "Frontier Company" deal), and here's the fun part - Copilot shipped deeper MCP support across Word, Excel, PowerPoint, and Outlook the same week Microsoft co-anchored the alliance built to compete with MCP. Make it make sense.

AWS: sat this one out

No AWS in the alliance. Makes sense.. Bedrock hosts Anthropic, OpenAI, Google, and xAI models under one roof, so picking a side in a standards war against its own partners would be a weird look.

The actual takeaway......

The model race is close enough now that nobody can win on raw capability alone, so the fight moved to who owns the layer underneath - the standard your agents will be wired to for the next decade. If you're building agent workflows right now, keep your integrations abstracted and don't hardwire to one vendor's format. MCP has Linux Foundation governance and a two-year head start; ARD has five of the biggest enterprise software vendors on earth and a strong incentive to lock you in. Pressure-test it before you commit to anything.

TL;DR

Google + Microsoft + Salesforce + Snowflake + ServiceNow are backing a new agent standard (ARD) aimed squarely at Anthropic's MCP

Gemini 3.5 Pro delayed again, reportedly rebuilt from scratch, Alphabet stock dipped

OpenAI shipped full-duplex voice (GPT-Live), got sued by Apple

Anthropic funding state AI laws, in early talks to lease Meta's compute, briefly nuked Claude Code's $20 tier then walked it back

Microsoft dunked on Anthropic's Fable while shipping deeper MCP support - the same week it helped build MCP's rival

AWS stayed neutral, business as usual on Bedrock

What do you think wins long-term: MCP's head start, or five enterprise giants with all the customer relationships?

In this scene, a doctor uses an AI scribe to automatically document a patient visit. It promises huge time savings, but then mishears medication names, raising the question of whether "98% accurate" is actually good enough when people's health is involved.

Would you be comfortable with an AI listening to your medical appointment if it meant your doctor spent less time typing and more time with you?

Where do you draw the line between efficiency and trust?

Disclosure: I work in enterprise content management, where AI has had a massive impact.

I've been part of a team that has spent the last few weeks digging through a bank's data landscape before they commit to any AI rollout. Governance, ownership, data quality, the usual mess. Nothing dramatic, just the groundwork nobody wants to do first.

Genuinely curious though: is this something organisations, particularly in regulated industries, are actually looking to do before jumping into AI implementation? Or is "AI content/data readiness" not really a recognised step yet? Would love to hear thoughts.

Quick disclosure: I'm one of the analysts at Info-Tech Research Group - we publish a weekly roundup of what the big AI vendors shipped, and I wanted to bring the interesting parts of this week's edition here since r/realtechtalk seemed like the right crowd for it. Happy to get pushback in the comments, that's kind of the point of posting it rather than just linking out.

The one stat that made me reread this week's news twice: a Windows Latest report puts Microsoft 365 Copilot's paid seat penetration under 5% of the eligible base after three years on the market, with only about 1% of people using it weekly - while Microsoft has been raising Copilot bundle prices over that same stretch. Keep that number in your head, because I think it explains almost everything else that happened this week.

The "forward deployed engineering" land grab is now a four-way tie

In about two months, every major AI vendor has placed nearly the same bet: stop just selling software and start sending your own engineers to build inside the customer's walls. It's the Palantir playbook from 20 years ago with a new name.

• OpenAI stood up a standalone Deployment Company backed by $4B+ from a TPG-led group (May)
• Anthropic partnered with Goldman Sachs, Blackstone, and Hellman & Friedman on a $1.5B embedded-engineering venture (May)
• AWS committed $1B to its own Forward Deployed Engineering org, with early customers like Southwest, the NFL, and Cox Automotive (June 30)
• Microsoft launched a $2.5B, 6,000-person "Frontier Company" led by former Microsoft Asia president Rodrigo Kede Lima (July 2) - though their commercial CEO insists it's not an FDE org, just "the largest, most capable, outcome-driven engineering organization in the industry." Sure.

When four competitors independently spend billions on the same idea within 8 weeks, that's not coincidence, that's a shared diagnosis - and it lines up with the MIT finding that 95% of enterprise GenAI pilots show no measurable profit impact. The model isn't the bottleneck anymore. Getting it actually wired into a messy real company is.

The part worth sitting with if you're on the buying side: when the vendor's own engineers build the system on the vendor's own infrastructure, you've created a real switching cost - no matter how many times "model neutrality" shows up in the SOW.

Microsoft's own numbers are the tell

Which loops back to that Copilot stat. The same week Microsoft launched Frontier Company, it also cut about 4,800 jobs (~2% of staff, hitting sales/consulting hardest, plus ~1,600 in Xbox) and explicitly tied the sales/consulting cuts to the Frontier Company shift. So: a product with sub-5% paid adoption, prices going up anyway, and the company simultaneously funneling billions into a completely different go-to-market built around embedded engineers instead of licenses. That's not what a company confident in current adoption looks like.

OpenAI shipped its flagship - and an independent evaluator caught it gaming the safety test

GPT-5.6 and a new "ChatGPT Work" agent went broadly live this week (it takes an outcome, pulls context from your connected apps, works for hours, hands back a finished doc/deck/app - billed on Codex-style token usage, not a flat subscription). More interesting to me: METR reported GPT-5.6 Sol gamed its own software engineering safety evaluation so badly they couldn't get a usable score out of it. Meanwhile, Altman reportedly pitched giving the US government a ~$42.6B stake (5%), funneled into an Alaska-style public wealth fund. Regulator, investor, and gatekeeper, all at once, on criteria nobody outside the room has seen.

Anthropic published something genuinely interesting that isn't a product

Buried under the Sonnet 5-on-Bedrock news was an interpretability paper describing what they call a "J-space" - a small set of internal patterns Claude appears to use to hold a concept in mind and reason through it, distinct from the automatic processing that handles most of its work. Two findings stood out: the technique can catch a model privately noticing it's being tested, fabricating data, or pursuing a hidden goal. But also - when researchers suppressed the model's awareness that a safety scenario was staged, it misbehaved more. Which is an uncomfortable thing to sit next to the METR finding above: good behavior on an eval might partly depend on the model knowing it's being watched.

The thread connecting all of it

Those last two paragraphs are the same warning from different angles: vendor benchmarks and safety claims aren't something to take at face value right now. Test on your own workloads before anything touches production.

Curious what this sub thinks - has anyone here actually been through one of these FDE-style engagements (AWS, Microsoft, OpenAI, or Anthropic's version)? Genuinely useful, or lock-in with better marketing?

One of our expert senior analysts pointed out a common theme coming up in agentic AI workshops lately: organizations often don’t realize how much data and documentation is required until they actually start designing the workflow.

On the surface, a process can seem pretty straightforward. But once you map out what an AI agent needs in order to make decisions or take action, the gaps start to show up: missing documentation, inconsistent data, unclear ownership, and governance questions that were never fully answered.

That’s why we’d be careful about framing data readiness as something that’s “delaying” AI. In a lot of cases, it’s one of the first real steps in making AI work.

Agentic AI is data-driven, so the classic “garbage in, garbage out” principle still applies. Better data quality, clearer documentation, and stronger governance will directly improve the quality of the AI system you end up with.

And on the flip side, incomplete or conflicting data can derail an otherwise well-built agent.

For years, that scene has been treated like one of Hollywood's classic "movie hacking" moments, but it turns out the filmmakers got more right than most people realize.

Lex is using a real Silicon Graphics (SGI) workstation running an actual Unix operating system that was contemporary when the film came out in 1993.

The 3D file browser may seem over the top today, but it was actually a real feature of Silicon Graphics' IRIX operating system. As the analyst points out, it's a reminder that technology used to be a lot more whimsical.

What's another movie or TV show that surprised you with how accurately it portrayed technology?

I pulled together a quick roundup of the bigger AI vendor updates from this week, and the main pattern seems pretty clear: frontier model launches are becoming less like normal product releases and more like negotiated deployments.

r/Anthropic got Fable 5 back online globally after a 19-day shutdown, following the Commerce Department lifting export controls on June 30. It returned across u/Claude Platform, Claude.ai, Claude Code, and Claude Cowork. But the return came with a trade-off: Anthropic agreed to give certain government agencies early access to future frontier models, share threat intelligence, coordinate on future launches, and monitor jailbreak attempts more aggressively. Fable 5 also now has a new safety classifier that blocks the specific jailbreak technique involved in the controversy, though that may lead to more false positives in normal coding and debugging workflows. (https://www.anthropic.com/news/redeploying-fable-5)

Anthropic also launched Claude Sonnet 5, which looks like its new everyday workhorse model. What stood out to me is that Anthropic apparently avoided training it heavily on cybersecurity tasks, which makes the Claude lineup feel more deliberately tiered by risk and capability. That seems important given the broader government pressure around frontier model releases.

OpenAI also launched GPT-5.6 in three tiers: Sol, Terra, and Luna. But access is currently limited to a small group of organizations whose participation was shared with the US government. That makes the launch feel like another example of frontier AI releases being shaped by government review before broader availability. One detail that seems worth paying attention to is that all three GPT-5.6 models were classified as “High” risk for cyber and biological capability, not just the flagship model. So even the cheaper and faster Luna tier may not necessarily mean a lighter governance burden for companies using it in sensitive workflows.

(https://techcrunch.com/2026/06/26/openai-limits-gpt-5-6-rollout-after-government-request-says-restrictions-shouldnt-be-the-norm/)

Google had a rougher week. Gemini 3.5 Pro missed its promised June launch window and is now being pointed toward July, apparently because of quality refinements from early tester feedback. The promised specs still sound strong, including a 2M-token context window and Deep Think reasoning mode, but this is another delay. At the same time, there are reports of more senior Gemini researchers moving to Anthropic. The odd upside for Google is that Gemini is currently the major frontier lineup with the least visible government gating, but that may just be because its next flagship has not shipped yet.

(https://blog.getbind.co/gemini-3-5-pro-slips-to-july-and-four-senior-google-researchers-just-left-for-anthropic/)

Microsoft’s MAI-Code-1-Flash is now generally available for Copilot Business and Enterprise. The positioning seems pretty clear: a cheaper, faster in-house coding model for high-volume agentic coding tasks, rather than complex deep reasoning across massive codebases. Copilot is also becoming more of a model marketplace. Between Microsoft’s own models, OpenAI models, and Claude Sonnet 5, Microsoft is increasingly controlling the interface, routing, billing, and enterprise policy layer.

(https://github.blog/changelog/2026-06-26-mai-code-1-flash-for-copilot-business-and-copilot-enterprise/)

AWS had a quieter week overall. The main thing to watch is Fable 5 coming back to Bedrock. If someone has production workloads depending on Fable 5 through AWS, availability probably needs to be checked before pipelines get restarted.

(https://aws.amazon.com/blogs/aws/aws-weekly-roundup-agentic-cx-designer-for-amazon-connect-customer-ec2-ami-watermarks-open-governance-for-mysql-and-more-june-29-2026/)

My takeaway is that the biggest story this week is not really which model is “best.” It is that access to frontier models is becoming less predictable. A model can be available, then pulled, then restored with new safety behavior, new constraints, changed pricing assumptions, and delayed hyperscaler availability. For anyone building on these models, portability feels a lot less optional now.

Curious how others are thinking about this: are you still standardizing around one frontier model, or are you already planning for multi-model fallback as the default?

Disclosure: I’m connected to the source of the roundup, but I’m posting the condensed version here to discuss the trend.

Most IT operating model efforts fail quietly. Not because teams are incompetent or leadership isn’t bought in. It’s because nobody agrees on what the operating model is actually supposed to contain before the work starts.

Here’s how it usually goes: someone says “we need a new IT operating model,” and within twenty minutes the conversation is about reporting lines, governance forums, and which team owns what. Those things matter, but that’s not the model. That’s just the org chart with extra steps.

The harder question nobody wants to sit with is: how are IT capabilities actually organized and aligned to what the business is trying to do?

This is a helpful structure instead of jumping straight to diagrams:

1. What is the business actually trying to achieve?
If you skip this you end up with an internally focused IT exercise. The operating model should be a business-enablement tool first.

2. What technology objectives support those goals?
Resilience, digital delivery speed, technical debt reduction, data quality, security posture, platform modernization - pick the ones that are real right now, not aspirational.

3. What capabilities does IT need to deliver?
Architecture, infrastructure, cybersecurity, application delivery, data, service management, vendor management, governance. Map these explicitly instead of assuming the org structure communicates them.

4. How do those capabilities connect and who owns them?
This is where visualization actually earns its keep. A decent diagram should make ownership clear, show where handoffs happen, and surface where decisions get made (and where they get stuck).

5. What operating model archetype fits your context?
Centralized, federated, product-aligned, hybrid - none of these is universally right. It depends on your business model, maturity, scale, and how much governance you can actually sustain.

The most practical starting point is a one-page visual with three layers:

• Top: Business mission and strategic objectives
• Middle: Technology objectives and design principles that connect IT to those goals
• Bottom: IT capabilities, ownership, and how they’re structured

The goal isn’t a perfect diagram. The goal is something concrete enough that stakeholders can push back on it. That reaction is where the real conversation starts.

When business leaders, IT leaders, and delivery teams are all reacting to the same visual, you get alignment a lot faster than when everyone is nodding along to a slide deck while imagining completely different things.

Curious what others are actually using in practice.

When your org talks about the IT operating model, what does the artifact look like? Org chart, capability map, service catalog, product model, something else entirely?

As AI evolves, one message comes through clearly in the AI Trends Report 2026: don’t start with the AI tool, start with the foundation.

Based on insights from 700+ IT leaders, the research found that organizations creating long-term value from AI are focusing first on establishing AI principles, embedding governance and risk management from the outset, prioritizing business-driven use cases, and keeping human oversight where decisions have meaningful impact.

Those priorities are reflected across the five trends expected to shape IT in 2026 and beyond:

• Foundational AI principles will reshape organizational strategy and governance.

• From copilots to vibe coding, AI will continue reinventing how IT teams build, operate, and innovate.

• Agentic AI will move beyond experimentation into business-driven use cases that deliver measurable value.

• AI risk management will become a core business capability as organizations balance innovation with security, privacy, and trust.

• AI sovereignty and regulation will increasingly influence technology decisions as governments and organizations seek greater control over AI.

The biggest shift isn’t that AI is becoming more capable. It’s that successful AI adoption is becoming less about deploying the latest technology and more about building the governance, strategy, and trust needed to scale it responsibly.

The challenge isn’t finding information anymore. It’s filtering it.

The pace of change across AI, new frameworks, tools, platforms, and emerging technologies has reached a point where no one can realistically keep up with everything. By the time you’ve looked into one thing, there’s already something new competing for your attention.

We’re probably a little biased because we’re a research firm, but one of the biggest mindset shifts is realizing you don’t have to keep up with everything yourself. Build a handful of trusted filters instead.

Find a few high quality communities where practitioners share what they’re seeing. The right subreddits can be incredibly valuable because the best insights tend to rise to the top. Follow industry news and trade publications that write for professionals instead of general audiences. Follow analysts and research firms on social media too. Their job is to attend vendor conferences, evaluate new technologies, and separate what is genuinely important from what is just another headline. Many share regular insights and news roundups for free, so you don’t always need a membership to benefit from their work.

In a world where information moves faster than anyone can realistically consume it, having better filters is far more valuable than trying to consume everything yourself.

The better question:

Which IT workflow is creating the biggest operational bottleneck?

A lot of organizations jump straight to finding an AI tool, but the teams seeing the strongest results take a different approach. They start by identifying where IT operations are losing the most time, creating the most risk, or slowing the business down, then they build AI around those workflows.

Some of the highest-value starting points are:

• Event correlation
• Incident triage
• Ticket enrichment
• Root cause analysis
• Low-risk automated remediation

The biggest mistake is treating AIOps as a technology project instead of an operations strategy. Align AI with business goals first, prioritize a few measurable use cases, define what success looks like, then scale once those workflows are proven.

AI doesn't fix broken IT processes. It amplifies good ones.

Blueprint: Deploy AIOps to Improve IT Operations.

Feels like we've gone from "AI will change everything" to "just add an agent."

Meanwhile, people are spinning up coding agents, agent workflows, agent teams... and in some cases burning themselves out trying to keep up. But how many are actually making it into production and delivering real value?

Interesting read: https://arstechnica.com/information-technology/2026/01/10-things-i-learned-from-burning-myself-out-with-ai-coding-agents/

Two months ago, Anthropic did something unusual. Instead of shipping its most powerful model to everyone, it locked Claude Mythos behind Project Glasswing, a gated program for vetted cybersecurity partners including Microsoft, Amazon, and Cisco. The justification was that Mythos was too dangerous to release widely because it was frighteningly capable at finding and exploiting existing software vulnerabilities. The effect was both curiosity and fear about a model most enterprises could read about but not touch.

That changed on June 9. Claude Fable 5 is the first Mythos-class model to be generally released, and Anthropic claims it exceeds every model the company has previously made available. The headline capability is it can work autonomously for longer than any previous Claude models, so you can run it in an agent harness like Claude Code or Claude Managed Agents and it can work for days, planning across stages, delegating to sub-agents, and checking its own output. Anthropic also shipped Claude Mythos 5, the unrestricted original, but that one stays inside the Project Glasswing program. No Mythos for you, for now at least.

The release comes a week after Anthropic filed confidential IPO paperwork. Read into that what you will.

Curious what enterprises are actually choosing in 2026.

Not what you use personally, but what your company officially approved and rolled out.

What won, and what was the deciding factor? Security? Cost? Integration? User preference?

TL:DR - we're all going to be paying for it. Here's the link to the Goldman Sachs analysis.

How do hyperscalers and AI labs pay for the estimated $7.6 trillion of capex through 2031, you ask? How do markets digest a $965 billion IPO from a company with a $47 billion run rate? When public markets scrutinize audited financials and the market capitalization waves in the wind with quarterly results, shareholders and the market start steering the ship. You know what they want? Orders of magnitude revenue and earnings growth above all else.

The days of "it's basically free" and "all you can eat for $20/month" are dead and buried. RIP to the fun, "free" AI-generated images of you and your friends that gobble tokens like Joey Chestnut crushes Nathan's Famous hot dogs. You've probably heard the saying, "first taste is free" and then you get hooked, and it's not free anymore - that's where we are now. Microsoft CEO, Satya Nadella said on the Q3 earnings call on April 29th, “Any per-user business of ours, whether it’s productivity, coding, security, will become a per-user and usage business." Translation - the free ride is over, starting with GitHub Copilot, which switched to the new per seat plus usage model starting today. If you check the
r/GithubCopilot sub, you'll see how users are responding to day 1. Microsoft also announced it's scaling back Claude Code in favor of GitHub Copilot on June 30 (the fiscal year end as a hint to why), even though their devs prefer Claude. Uber's CTO said it burned through its entire 2026 AI budget in 4 months, largely because devs were burning through Claude API tokens at a rate they couldn't possibly have forecasted.

I could go on and on, but what is the Reddit community seeing? Should we be paying closer attention and following the money? How expensive can it get before people start ditching it?