From AI to SI: Models That Can Manage Real Projects

If your understanding of artificial intelligence comes from trying ChatGPT in the GPT-3.5 era, it is time for another look.

The familiar experience was a conversation: ask a question, get an answer, refine the prompt. Useful, but you still supplied the context, moved information between tools, and decided what should happen next.

Today's capable models, connected to the right tools and project environment, can take on much more of that coordination. They can help plan work, carry out steps, delegate tasks, and bring results back for review. At Abundance, we have had models work on their own on projects for more than 60 hours at a time.

That experience is central to why we use super intelligence, or SI, in our language. We are building around systems that can carry meaningful responsibility for work over time. If you are searching for AI automation for your business, this is the change worth understanding.

From GPT-3.5 to today's models: a much larger working space

One measurable change is the context window: the working space available to a model for a step of the project. Think of it as a desk. A larger desk gives it room to work with more material at once. OpenAI explains context windows here.

Input tokens, output tokens, and context in plain English

Tokens are small pieces of information a model processes. For text, a token might be a word, part of a word, or punctuation. Token counts are not the same as word counts.

• Input tokens are what you give the model to work with: your instructions, project files, earlier conversation, and information returned by its tools.
• Output tokens are what the model generates: a response, a plan, code, or a request to use a tool. Reasoning models can also use output tokens for internal reasoning, so the output budget is not all readable text.
• The context window is the shared working budget. Input and generated output need to fit within that space; the maximum output allowance is a separate ceiling. OpenAI explains reasoning-token accounting.

For example, asking a model to review a project brief uses input tokens. Its proposed plan uses output tokens. When it reads a file through a tool, that returned information becomes input for its next step. Over a long project, this cycle repeats many times.

Compare these documented examples from the GPT-3.5 family and today's models. The GPT-3.5 rows are different variants, rather than a single specification for every GPT-3.5 release.

The listed input and output capacities share the context budget; you cannot fill the entire window with input and then add the maximum output on top. These are documented API limits as of October 1, 2026, and the usable budget also includes the system instructions and other context the application supplies.

That million-token window is roughly 256 times the 4,096-token example, and about 64 times the later 16K version. The maximum output allowance has grown 32 times, from 4,096 to 128,000 tokens. A system has more room to examine the project and more room to generate substantial work in a response.

For project work, the practical opportunity is to provide much more of the brief, relevant files, requirements, and previous decisions together. You can give the system a fuller view of how a change fits the surrounding work.

Capacity still needs to be used well. A bigger context window creates room for information; selecting the right information and checking the result determine how useful that room becomes.

Models can act as managers of scoped work

Project management involves breaking an objective into tasks, deciding what depends on what, coordinating execution, and checking progress. With suitable tools and instructions, modern agents can take on parts of that role.

OpenAI's multi-agent documentation describes systems that delegate work to subagents and coordinate their results. That gives a model a way to organize specialist work around a larger objective. See the multi-agent documentation.

Consider a website project. A coordinating agent could assign research on the existing site, organize implementation tasks, request checks on the resulting pages, and assemble findings for the next decision. People define the objective and review the outcome, while the system handles more of the movement between steps.

The management role becomes useful when it reduces the amount of attention people spend keeping a project moving. You can spend more time deciding what the business needs and less time repeatedly explaining the same task or carrying information from one tool to another.

What is an MCP server?

MCP stands for Model Context Protocol. It is a standard way for an AI application to connect with outside tools and information. An MCP server is a software program that makes particular tools or resources available through that connection. It can run on your computer or remotely. The MCP architecture documentation explains these connections.

Think of it as an adapter between the system doing the work and the systems your business already uses. A customer-management tool, for example, could have an MCP server offering actions such as finding a customer record or creating a task.

The agent requests an action, the connected software performs it within its configured permissions, and the result comes back to the agent. That can remove the manual step where someone copies information out of one application and pastes it into another.

An MCP connection does not automatically give a model access to everything. The available tools, authentication, and permissions determine what it can read or change. That makes the connection useful for a defined job, such as looking up project information while leaving unrelated records outside its access.

The other tools that make sustained SI work possible

A capable model needs a working environment around it. Here are the main pieces in everyday language.

Agents: the workers

An agent combines a model with instructions and tools so it can take steps toward an objective. It reads the task, chooses an action, sees the result, and decides what to do next. That repeated cycle lets it work through a project.

The harness: the workspace and operating system

A harness is the software that runs the agent. It supplies its workspace, connects tools, tracks the session, and handles things such as stopping, resuming, or asking for a decision. The model supplies the reasoning; the harness gives it a place and a process for doing the work.

Skills: the playbooks

Skills are reusable instructions and supporting resources for a particular kind of work. A website-review skill might explain what to inspect, which checks to run, and how to present the findings. They give the agent an established method to follow.

Retrieval and project memory: the filing cabinet

Retrieval finds relevant information and brings it into the model's current working context. Project records preserve decisions, requirements, and completed work between steps. A useful system can look up the information it needs without carrying every historical file in every request.

Browser and command-line tools: ways to act

Browser tools let an agent inspect and interact with web pages. Command-line tools let it use software through typed commands, such as running a build or checking a file. These connections let it test what happened after an action and use that result in its next decision.

Orchestration and checks: coordination and quality control

Orchestration organizes work across agents and tools. Checks test whether the result meets the goal: whether a feature works, a page loads, or a calculation agrees with its source. Together, they give a coordinating model a way to assign work and inspect what comes back.

What running for more than 60 hours changes

At Abundance, we have had models run on their own for more than 60 hours at a time on projects. That is a different operating experience from waiting for a single chat response.

A run of that length moves through many steps. Larger context windows help, and the surrounding system also needs ways to preserve the project state as work continues. For example, OpenAI documents context compaction, which reduces the active context while carrying forward state needed for subsequent steps. Read about compaction for long-running work.

The opportunity is sustained progress while people devote their attention elsewhere. A well-scoped system can keep working toward an objective between human reviews, with completed work and unresolved decisions ready when the team returns.

Duration becomes valuable when it produces useful work. We care about what was completed, what checks passed, and which decisions still require a person. Those are the measures that connect a long run to time and resources recovered by the business.

More than seven billion tokens in a day

We have also had a day with more than seven billion tokens processed.

To put that figure in context, tokens are units of model processing. The million-token context window above describes capacity for an individual step. The seven-billion-token figure describes our processing volume over a day. Input and output token counts help explain that volume: reading project material and tool results contributes input, while generating work and reasoning contributes output. Reused context can be counted again across requests, so total tokens do not mean the same number of unique words or finished deliverables.

That scale changes how we think about allocating work. There is room to explore alternatives, assign separate tasks, and check results across an extended project workflow. The useful output still has to justify the processing effort: a working feature, a completed investigation, or a deliverable that makes the next decision easier.

For a business owner, the important question is what that capability lets your team accomplish with the people and time available.

Why we are moving from AI to SI

Calling these systems artificial intelligence can leave people picturing the chatbot they tried years ago. The intelligence is machine-built, but the work it can contribute to is real: projects, software, research, and operating decisions.

SI reflects the role we are designing for: capable systems that help manage and execute substantial work, freeing people to focus on direction, judgment, and relationships.

The wider naming shift is happening too. A September 29, 2026, executive order directs federal agencies to use SI in place of AI in specified official materials. Separately, superintelligence has a stricter research meaning involving broad capabilities beyond those of humans. Our practical description of project work is not a claim that every model meets that scientific threshold. Read the executive order and the research definition.

Give the system a project worth taking on

Start with a defined objective, access to the relevant information, and a clear description of a successful result. Identify where the system can act independently and where a person needs to decide or review.

That might mean carrying a software feature through implementation and checks, investigating a recurring operational problem, or coordinating a research project into a usable deliverable. Choose work whose completion would give your team meaningful time back.

At Abundance, we build the software, workflows, MCP connections, and tools that make that kind of delegation possible. Explore our SI workflows and agents, or bring a project to a Project Review.