For years, artificial general intelligence has existed somewhere over the horizon. Researchers have argued about how to define it, technology companies have predicted when it might arrive, skeptics have questioned whether it is even possible, optimists have promised abundance, and pessimists have warned of catastrophe. Through all of it, AGI remained comfortably distant. Artificial intelligence could write an essay, generate an image, pass professional examinations, write sophisticated software, interpret medical images or defeat increasingly difficult benchmarks, and we could continue arguing that none of it constituted general intelligence. There was always another limitation to point toward, another benchmark it couldn't pass, another kind of reasoning it couldn't reliably perform, or another example of something a child could understand that the world's most sophisticated AI somehow could not.

That argument remains valid. GPT-6 Astra is not obviously artificial general intelligence. Neither are the latest systems from Anthropic, Google, xAI or their competitors. Today's systems remain uneven. They make mistakes. They can require tools, scaffolding and human supervision. They don't demonstrate every form of intelligence humans possess, and researchers still don't even agree on a scientific definition that would allow everyone to point at a system and declare that the AGI threshold has objectively been crossed.

So this is not an announcement that AGI has arrived. It is a narrower and, we believe, increasingly defensible argument: we have entered the AGI foothills.

The Mountain Is Not the Foothills

The distinction matters because foothills are not mountains. They are the transitional terrain where the flat ground begins to rise. When approaching a mountain range, there isn't necessarily a sign announcing where the plains end and the mountains begin. The landscape gradually changes. Elevation increases. Rivers behave differently. Vegetation shifts. The horizon begins closing around you. At some point, even though the summit remains far away, it becomes difficult to continue pretending that you are standing on the same terrain you occupied fifty miles earlier.

That may be approximately where artificial intelligence stands in September 2026. For most of the modern AI era, individual breakthroughs could reasonably be considered narrow accomplishments. Deep Blue could defeat a chess champion but couldn't write an email. AlphaGo could dominate one of humanity's most sophisticated games but couldn't drive a car. Early large language models could generate astonishingly convincing prose but struggled with reasoning, mathematics, persistent memory and interaction with the physical or digital world. Each achievement occupied its own technological island.

Those islands are increasingly connecting. Frontier AI systems can now reason across unfamiliar problems, write and debug sophisticated software, interpret images and video, process enormous quantities of information, browse the internet, operate computers, navigate software interfaces, conduct research, solve increasingly difficult mathematical and scientific problems and pursue objectives across longer sequences of actions. None of those abilities individually establishes AGI. What matters is that capabilities that once appeared separately are increasingly appearing inside the same general-purpose systems.

The signal is not one benchmark. It is convergence.

Astra Is a Signal, Not the Finish Line

OpenAI's release of GPT-6 Astra on September 3, 2026, makes that convergence particularly difficult to ignore. Astra should not automatically be called AGI simply because its capabilities are remarkable or because some people inside OpenAI are willing to entertain the term. Benchmark performance, no matter how impressive, cannot settle a philosophical and scientific question that researchers haven't even agreed how to define.

But neither should Astra be treated as merely another incremental chatbot upgrade. OpenAI reports that Astra reaches 99.9 percent on ARC-AGI-3, a benchmark specifically designed to test adaptation to unfamiliar environments, while demonstrating substantial advances in computer use, software engineering, scientific reasoning, professional work and cybersecurity. The precise benchmark numbers will eventually be surpassed, as benchmark numbers always are. The more important development is the expanding range of activities that a single model can perform.

Astra can increasingly act rather than simply answer. It can navigate computers, operate software, conduct online research, manipulate professional tools, build and test applications, troubleshoot problems and execute multistep workflows. That distinction is enormous. For much of the generative AI era, artificial intelligence primarily produced information. A human asked a question and the machine returned an answer. Even when that answer was extraordinary, the machine generally remained on one side of the screen waiting for another instruction.

Agentic systems begin crossing that boundary. An AI capable of explaining how to conduct scientific research is useful. An AI capable of participating in the research process is something different. An AI capable of explaining software engineering is useful. An AI capable of entering a development environment, modifying software, running tests, observing failures, diagnosing them and trying again begins functioning as a participant in the engineering process.

Artificial intelligence started the generative era with an extraordinarily capable voice. It is increasingly acquiring hands.

Astra also became OpenAI's first model to reach the company's Critical cybersecurity capability threshold. According to OpenAI, with appropriate tools and access, the system can discover previously unknown vulnerabilities and develop ways of exploiting them across well-protected systems without requiring a human to guide every individual step. That does not establish general intelligence, but it does represent the kind of autonomous problem-solving capability that would have sounded much closer to science fiction than product development only a few years ago.

Intelligence Is Beginning to Help Build Intelligence

Perhaps the strongest signal that we have entered the AGI foothills isn't Astra at all. It is what is beginning to happen inside the laboratories developing systems like Astra.

For most of AI history, the development loop was straightforward. Humans researched artificial intelligence, humans wrote the software, humans designed experiments, humans analyzed the results, and humans used what they learned to construct better artificial intelligence. Computers were essential tools in that process, of course, but the intellectual work of advancing AI remained overwhelmingly human.

That boundary is beginning to blur.

Anthropic has publicly reported that more than 80 percent of the code merged into its codebase was authored by Claude as of May 2026. Its engineers are producing dramatically more code than they were before AI coding agents became deeply integrated into their workflows. More consequentially, AI systems are beginning to participate in portions of the experimental research process itself. They can generate hypotheses, write experimental code, run experiments, analyze results, coordinate parallel investigations and iterate toward research objectives.

Humans remain deeply involved. They determine much of the research agenda, construct important evaluations, make consequential decisions and remain responsible for training infrastructure, architecture, safety and deployment. There is no compelling public evidence that an artificial intelligence can independently design a superior successor, build it, activate it and allow that successor to repeat the process indefinitely.

That would be recursive self-improvement in its strongest sense, and we are not claiming that has happened.

Something less dramatic but potentially just as historically important may already be underway: AI-assisted recursive improvement.

The loop now looks different. Humans build AI. AI helps humans build better AI. Better AI makes the combined human-AI research system more productive. That increasingly productive system builds still better AI, which can then contribute more substantially to the following development cycle. Humans remain inside the loop, but the loop itself begins to tighten.

Full autonomous recursive self-improvement is therefore not necessary for technological acceleration. Intelligence doesn't have to remove humans from the process before intelligence begins accelerating the production of more intelligence.

Why Everything Suddenly Feels Faster

One of the most noticeable characteristics of the current AI landscape is simply how fast everything feels. Capabilities that appeared experimental become products surprisingly quickly. Agents become more reliable. Context windows expand. Computer use improves. Coding performance advances. Scientific capabilities emerge. Models begin operating tools that previous generations could merely discuss.

Release cadence alone cannot demonstrate recursive improvement. AI laboratories develop multiple models simultaneously. Training infrastructure improves. Post-training techniques become more sophisticated. Products can be released independently of entirely new foundation-model training runs. A shorter interval between two public model releases does not mean the second model somehow built itself.

But the effect of AI on AI research cannot simply be dismissed either. Imagine that one generation of AI makes a laboratory's researchers substantially more productive. Those researchers can conduct more experiments, test more hypotheses, write more software, analyze more results and investigate ideas that previously would have consumed too much human time. The resulting model then becomes another research instrument available to those same researchers. If that model produces another productivity increase, the following development cycle changes again.

At some point, the distinction between "humans improving AI" and "AI improving AI" becomes less clean than either phrase suggests. The actual research unit increasingly becomes a combination of human and machine intelligence, with each contributing capabilities the other lacks.

No runaway intelligence explosion is required for the development curve to begin bending upward.

That may be where we are now.

AGI May Be an Ecosystem Before It Is a Machine

There is another possibility worth considering. Perhaps our traditional mental model of AGI is wrong.

For decades, discussions of artificial general intelligence have often imagined a machine. One system becomes sufficiently intelligent, a threshold is crossed, and AGI exists. The moment resembles a finish line because that makes the concept easier to understand.

Modern artificial intelligence increasingly looks less like a solitary machine and more like an ecosystem. A frontier model can be connected to persistent memory, browsers, computers, software tools, databases, communication systems, specialized agents and enormous computational infrastructure. Its effective capability is therefore not simply whatever intelligence can be measured inside the neural network in isolation. It is what the entire system can accomplish when those components operate together.

Human intelligence already works this way. A modern scientist's effective cognitive capability includes computers, scientific literature, laboratories, colleagues, databases and instruments. Removing those tools doesn't make the scientist unintelligent, but it dramatically reduces what that intelligence can accomplish.

Artificial intelligence may be developing along a similar path. General machine intelligence could emerge not as a solitary digital mind but as a networked cognitive system capable of perceiving, reasoning, remembering, acting and interacting with tools. If that happens, debates about whether one particular foundation model technically qualifies as AGI could become increasingly detached from the societal reality surrounding it.

We could find ourselves living in an effectively AGI-shaped environment before researchers agree that any individual model deserves the label.

The Foothills Test

If AGI isn't likely to arrive with a flashing sign, we need a better way to recognize the approach. Rather than relying on one benchmark or one company's announcement, we should look for multiple independent signals moving in the same direction.

Are AI systems becoming broadly competent across previously separate intellectual domains? Yes. Are they increasingly capable of acting rather than merely answering? Yes. Are they maintaining objectives across longer sequences of work? Yes. Are they becoming better at navigating unfamiliar digital environments? Increasingly. Are they participating meaningfully in scientific and engineering workflows? Yes. Are they helping build subsequent generations of artificial intelligence? Yes. Are their capabilities becoming economically consequential outside controlled demonstrations? Increasingly.

Then comes the crucial question: are they robustly human-level across essentially all cognitive domains, independently capable of pursuing arbitrary intellectual objectives and consistently reliable when confronting genuinely unfamiliar circumstances?

No.

That final answer matters enormously. It is why we should resist declaring that AGI has arrived.

But all the preceding answers matter too.

They are why continuing to describe AGI exclusively as something beyond the distant horizon is becoming increasingly difficult.

There May Never Be an AGI Day

History rarely provides clean boundaries while people are living through them. The Industrial Revolution didn't begin on a particular Tuesday morning. The internet didn't suddenly become socially transformative at 2:37 on some afternoon. Smartphones didn't become fundamental infrastructure for modern civilization on the day one particular device crossed a benchmark.

Transformations accumulate.

Eventually people look backward and realize that the environment changed.

AGI may follow the same pattern. Perhaps future historians will identify a particular system as the first true artificial general intelligence. Perhaps they will choose GPT-6 Astra. Perhaps Astra will look astonishingly primitive compared with systems released eighteen months from now. Perhaps the entire concept of identifying a "first AGI" will eventually seem quaint because general machine intelligence emerged gradually across models, agents, tools, infrastructure and human-AI systems.

We don't know.

That uncertainty is precisely why the foothills metaphor is useful. It doesn't pretend that the destination has been reached. It simply recognizes that the journey may have entered a qualitatively different phase.

The Terrain Has Changed

There is an understandable temptation in discussions about artificial intelligence to choose between two extremes. Either AGI is imminent and civilization changes tomorrow, or today's systems are merely statistical machines and nothing fundamentally important has happened.

Reality increasingly appears to occupy the uncomfortable territory between those positions.

Current artificial intelligence remains flawed, inconsistent and dependent upon human-created infrastructure. It is also capable of things that would have sounded extraordinary only a few years ago. Those statements are not contradictory. Both can be true simultaneously.

The more useful question may therefore no longer be simply, "Has AGI arrived?"

Instead, ask something slightly different:

What would the world immediately preceding AGI look like?

We would probably expect increasingly general systems capable of operating across intellectual domains that once required separate specialized models. We would expect growing autonomy and longer action horizons. We would expect AI to become deeply integrated into scientific research and software engineering. We would expect models to operate computers and tools rather than merely describe how humans should operate them. We would expect AI systems to participate increasingly in the development of their successors. We would expect the traditional boundaries between chatbot, programmer, researcher, analyst and agent to become increasingly difficult to maintain. And eventually, we might expect progress itself to begin accelerating as increasingly capable artificial intelligence becomes one of the tools used to create the next generation.

That hypothetical description of the world immediately preceding AGI is beginning to sound remarkably familiar.

So Conjugo is putting down a marker.

As of September 2026, we believe humanity has entered the AGI foothills.

We are not declaring AGI. We are not declaring an intelligence explosion. We are not claiming that autonomous recursive self-improvement has begun.

We are saying something more modest and, perhaps, more consequential.

We can still see plenty of mountain above us. We don't know how steep the climb becomes from here. Progress could accelerate, stall, encounter fundamental limitations or follow a path nobody currently anticipates. We don't know where future historians will ultimately draw the boundary between advanced artificial intelligence and artificial general intelligence.

But we no longer appear to be standing on a distant plain wondering whether the mountains are real.

The ground beneath us has begun to rise.

The terrain has changed.