u/Sassy_Allen

Real agentic organizations will outcompete traditional companies. Not because they add more AI tools to the same SaaS stack, but because they will run on a different software foundation.
They will operate with a live model of themselves, allowing them to move faster and make better decisions. They will see where execution is blocked, surface decisions before they become escalations, and connect customers, projects, budgets, risks, workflows, and outcomes in a way that today’s dashboards cannot.
You already know this. That is why AI transformation programs are everywhere.
But the story I hear from friends in executive and consulting roles is almost always the same: the ambition is clear, the implementation is not. Companies have run the workshops, hired the consultants, selected vendors and startups, and launched pilots. Yet the organization does not feel fundamentally different.
That is the gap most AI conversations avoid. The problem is not that companies have failed to adopt AI. According to McKinsey's 2025 State of AI survey, 88% of organizations now use AI in at least one business function, but nearly two-thirds have not yet begun scaling AI across the enterprise (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai).
The problem is that AI is being added to organizations that were not built for agents.
The company is not queryable
Most companies are still steered through fragments: a dashboard from finance, a CRM export from sales, a project update from operations, a risk register from legal, and a slide deck before the board meeting. None of these views is wrong. But none of them is the company.
The actual company lives in the relationships between them. Which customer depends on which project? Which project depends on which vendor? Which vendor depends on which approval? Which approval affects which budget? Which budget affects which strategic decision? That operating context is rarely available in one place.
Humans compensate for this with hierarchy. Information moves upward, decisions move downward, and middle layers translate, filter, summarize, and escalate. That structure is slow, and it hides important signals until they become obvious enough to enter a meeting or a report.
Agentic organizations will work differently. They will not only let executives ask better questions. They will let the organization surface the questions that matter. The organization starts to talk back.
Agentic organizations need a World Model
The missing layer is not another chatbot. It is a live World Model of the organization: a connected operating view of customers, projects, workflows, decisions, budgets, risks, dependencies, commitments, and outcomes.
Many enterprises are already investing in this direction through platforms such as Databricks, Snowflake, and internal data lakehouse programs. That work matters. Databricks describes the lakehouse as an architecture that combines the scale of data lakes with the management features of data warehouses for business intelligence, machine learning, data science, and analytics. Snowflake similarly positions its platform around unified data, AI, applications, governance, and enterprise controls.
But centralizing data is not the same as making the organization agentic. A lakehouse can make information easier to store, govern, analyze, and use for models. A World Model must go further: it has to represent the live operating context of the company and keep updating as work happens.
A dashboard shows selected metrics at a point in time. A data platform gives teams access to large amounts of structured and unstructured information. A World Model gives agents a view of how the organization actually runs: the relationships between people, projects, decisions, budgets, workflows, risks, and commitments.
That distinction matters because the highest value decisions are often not obvious at the top. They appear first as weak signals inside the operating system of the company. A blocked approval, a slipping partner milestone, a budget dependency, a customer risk hidden in support tickets, or a market signal that has not yet reached strategy can all matter before they become visible to leadership.
In today's model, those signals travel through people, systems, pipelines, and reports until someone notices the pattern. In an agentic organization, agents can identify those patterns directly and bring decision points to the right people earlier.
That is not just automation. It is a different organizational model: less hierarchy for information flow, more direct access to operating context, and faster escalation of decisions that matter.
The SaaS stack was not designed for this
The current enterprise software model works against this. Every SaaS product brings its own data model, permissions, interface, and roadmap. Over time, the company becomes a patchwork of tools connected by integrations, exports, meetings, and manual interpretation.
That model worked when humans were the main integrators. It does not work well when agents are expected to operate across the company. An agent that can only see one system can optimize one workflow, while an agent that understands the relationships between systems can help improve the operating model.
This is why simply adding AI to existing software will not be enough. It may produce better summaries, faster drafts, and useful automations, but it will not make the company agentic.
The World Model cannot become another lock-in layer
If the World Model becomes the operating memory of the company, it cannot simply become another vendor-owned layer. This is the part many AI transformation programs underestimate.
Data platforms are useful. They help companies centralize information, govern access, and prepare data for analytics and models. But they can also create new dependencies around metadata, permissions, APIs, workflows, pricing, and operational expertise.
For sensitive or regulated workflows, jurisdiction matters too. US-based or US-controlled providers may be subject to legal access regimes such as the CLOUD Act, where foreign governments can have access to your data and operations.
That does not mean every enterprise workload must avoid these platforms. It means agentic organizations need to treat sovereignty as an architecture decision, not as a procurement checkbox.
The more valuable the World Model becomes, the more important it is that companies understand who controls it, where it runs, how it can be changed, and whether it can survive beyond one vendor relationship.
Motoko changes what an app contributes
This is where Motoko, a new frontier language built for the age of agents, becomes important, not because executives need to care about programming languages, but because they need to care about what software contributes back to the organization.
In the current SaaS model, every new tool often becomes another place where context gets trapped. A team gets a useful workflow, but the organization gets another data island. For an agentic organization, that is the wrong direction.
The benefit of Motoko-based apps, once the query flag is enabled, is that custom software can be built around a specific workflow while still contributing context back into the organization. If an AI agent creates a custom app for a team, that app does not have to become another isolated tool in the stack. It can become part of the organization's shared operating context.
A procurement app can contribute vendor, approval, and budget context. A partner portal can contribute commitments, milestones, and risks. A project tracker can contribute execution status and dependencies. A decision system can contribute who decided what, when, and why.
Each app becomes more than a workflow interface. It becomes a source of context for the company. The program is the database. This is the breakthrough: AI-built software does not just help people complete tasks. It helps the organization build a live model of itself.
The competitive advantage is speed and intelligence
The value is that the company becomes more legible to itself. In the old model, leaders depend on reporting lines, meetings, dashboards, and escalations. In the new model, the organization can identify its own decision points.
It can show where execution is blocked, highlight which commitments are at risk, connect operational signals to strategic choices, and give teams the software they need without waiting for the next vendor roadmap or transformation program. That changes competition.
One company waits for the next update meeting. Another company sees the constraint as it forms, creates the tool it needs, and adjusts the workflow before the issue becomes visible in quarterly reporting. That difference compounds.
This is why agentic organizations will outcompete traditional companies. Not because they have more AI, but because they can see and adapt faster.
Infrastructure becomes the foundation
If AI-built apps become part of the organization's memory, they cannot be treated as disposable prototypes. They need to stay available, keep their data, remain verifiable, resist silent modification, and run without locking the company into one vendor's application layer. Sovereignty and security matter!
The Internet Computer is the sovereign frontier cloud for agentic organizations: a network where apps can run end-to-end in a tamperproof, always-on environment with no need for a security team. It is also the only infrastructure where agents can safely build and operate without the risk of data loss (a common problem of agents building on legacy IT infrastructure)
Agentic organizations will not be built on prompts alone. They need apps, context, governance, and a cloud layer where AI-built software can become durable operating infrastructure. More information about the infrastructure, that was built wih a massive 10 Year R&D investment: https://internetcomputer.org
How to start
The practical path is now much shorter. Teams can build such apps today with all major AI tools like Claude Code, OpenAI's Codex, Perplexity by using skills.internetcomputer.org, a curated database of agent-readable skills maintained by DFINITY Foundation that gives AI tools the up to date superpowers to built on the Internet Computer.
You can also build directly with caffeine.ai, an Internet Computer native app builder that absratcs all of the complexity away from users. For some teams, the right first step is a small internal app that solves one real workflow and contributes useful context back into the organization.
For organizations dealing with sensitive data, regulated workflows, or sovereignty requirements, the more interesting path is a private cloud engine: a dedicated environment for tamperproof, always-on apps and agentic workflows: https://opencloud.org

Fabio explains that the NASDAQ listed company which reached out to Dfinity to invest in ICP is in the medical industry. I wonder who that could be.

https://www.theregister.com/systems/2026/05/16/europe-built-sovereign-clouds-to-escape-us-control-then-forgot-about-the-processors/5237735

Nearly every solopreneur, startup, SME, enterprise or government department, will want to become an agentic organization (or "cybernetic org").

They will be vastly smarter, faster and more efficient.

Internet Computer Protocol (ICP) helps enable this brave future. But how? 🧵

An agentic org's outer layer is employees, who orchestrate AI, and use custom SaaS that the AI creates for them on-demand.

Caffeine, Claude Code and Perplexity will create SaaS on-demand. Caffeine Snorkel (coming) will auto-migrate legacy systems. 🧵

SaaS apps built using Motoko contain a data graph.

(Technical tip: the roots are the software code's global variables, as orthogonal persistence stores data inside code abstractions using persistent memory).

Coming demo: that graph is browsable and queryable by trusted AI 🧵

The data graphs inside the SaaS apps running on an org's cloud engines combine to form a *global* data graph.

That global data graph is the World Model that sits at the core of an agentic organization. 🧵

On-demand SaaS will include sovereign versions of messaging, online docs, HR, accounts, source code repos, etc.

The World Model shall provide the AI powering an agentic org with access to the totality of its data in *real-time* in the form of a uniform data graph it can query 🧵

The real-time "total" context provided by the World Model enables AI to serve at the true frontier of its potential.

AI can perform brilliant analyses for employees. AI can perform actions in pursuit of objectives. AI can adapt its behavior in real-time based on results. 🧵

Agentic organizations will be vastly faster, smarter, and more efficient, despite burning large numbers of AI tokens.

Organizations that successfully transform themselves into agentic organizations, will be able to easily out-compete rivals that lag behind. 🧵

The largest business transformation event in history has already begun, but yesterday's tech stacks weren't built to support agentic organizations.

By design, the Internet Computer and ICP cloud engines provide the ultimate platform supporting this new era. 🧵

— Hackproof infra/cloud from a network enables AI to build/update SaaS without security teams on-hand.

— Always-on apps and cloud sheds systems admin tasks (more automation).

— Sovereignty provides freedom from lock-in.

— Digital assets enable future commerce

🧵

— Motoko enables AI to build better, faster, at less cost, and without accidentally losing app data during updates, and... most importantly... enables the production of a World Model to unleash AI.

New demos coming soon. One last thing 🧵

DFINITY is transforming itself into an agentic organization, replacing its legacy SaaS with on-demand SaaS that's running on cloud engines 💪

This is making us better and more efficient, and enabling us to drive development by dog-fooding.

Excited to report more soon.

https://chainspect.app/chain/icp?range-tc=quarter&type-tc=avgTxFee

This is the new Revenue model for $ICP Cloud Engines:

➡️For Customers🏦

Governments & Institutions will pay $ to DFINITY
DFINITY buys $ICP (buying pressure📈)
ICP converted to cycles 🔁
Cycles are used to make sure the Engine is running ⚡️

➡️For Node Providers🌐:

Node providers don’t receive guaranteed payments, or receive smaller ones, and must compete for their nodes to be chosen by end users for inclusion into their engines.

In this model, 80% of the ICP revenues generated by a cloud engine (calculated as the number of ICP tokens that would have to be transformed to create the cycles powering the engine), would be returned to node providers, while 20% would be immediately burned🔥

Thus engines would always be deflationary rather than inflationary, and node providers would operate more like traditional businesses to maximize their profits.

“Yes, nations can run entirely on Cloud Engines and the Internet Computer if they chose to.

Its a very straight forward process: Define the goals (private vs public), ensure national data centers can provide supporting nodes, click deploy ;-)

We can support along the entire process.”

ICP to the moon

"Dear ICP community, the Internet Computer has now been running strong for 5 years

Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from (opencloud website)

Main points:

— Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network but are selected and configured by the cloud engine's owner.

— The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud.

— Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI.

(The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.)

— Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others.

— New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries.

— Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory).

— An expanding database of skills at (Internet Computer website) shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on (caffeine ai website) can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free.

— Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further...

— You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still...

— Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom.

— Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost.

— Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures).

— For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently.

— Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too.

Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030.

After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology.

In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that.

Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments.

It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines.

Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world.

Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models.

We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday

I'll be back with more news soon!!"

I had to remove the links to the websites in his post because Reddit's filters were blocking me from posting it.

The demo video is on X and I can't seem to post it separately from X.

https://chainspect.app/chain/icp?range-tc=quarter&type-tc=txns