Expert-Weighted Factors Enabling AGI

Forecasters overwhelmingly agree that the eight enablers are deeply entangled, making clean allocations difficult. As one writes: “They’re all super correlated… For instance, you don’t have compute without investment, and investment doesn’t matter that much on its own. It matters because it buys you compute and generates better algorithms, and you can get better data and so on.” Several invoke economic O-ring logic, with one concluding the listed factors “are all O-rings and therefore, in one sense, equally essential.” This entanglement drives many forecasters toward relatively uniform allocations; those who concentrate weight typically pick whichever factor or factors they regard as upstream of the others.

This is the enabler most frequently named as being singularly important, with many expressing the sentiment that AGI by 2040 “likely requires more than simply scaling current transformer systems,” and that genuine generality demands a new paradigm: “If AGI arrives by 2040, the story will be that someone cracked the architecture problem.” Few forecasters dismiss the importance of algorithmic improvements, but several view it as residing downstream of other enablers: “Important but partly a result of self-improvement and investment rather than an independent driver.”; “...crucial, but not the key ingredient as different architectures are often able to achieve similar results. Instead, a mix of data and sufficient compute are the key ingredients.”

Forecasters often treat these three as a single underlying enabler—“scaled physical compute”—because investment funds compute and infrastructure enables it: “Compute, investment, and infrastructure are tightly coupled, so I don’t know how to break it up between those.”; “‘Investment’ and ‘compute’ and ‘infrastructure’ are all overlapping.” Several who reference Richard Sutton’s Bitter Lesson [i.e., compute beats cleverness] concentrate weight here. Critics tend to argue that while “scaling compute has clearly driven progress so far,” that we may be bumping up against the limits of future progress given that “the compute, investment, and infrastructure enablers are (1) already well resourced with $700B in investment this year and (2) focused on the current paradigm of GenAI/LLMs,” and that going forward algorithmic breakthrough will likely “carry far more weight.”

Forecasters often combined these, with one writing, “real world interaction is just a way to generate the data,” and another adding, “I had a lot of trouble knowing what to count as ‘data’ versus ‘real-world interaction’ that accumulates data.” Forecasters allocating heavily here tend to emphasize that “the web is largely mined” and therefore “without continuous interaction with the world, models remain trapped within the distribution of human-generated text—which is precisely the model-collapse risk that makes purely text-based training a long-run dead end.”

Many forecasters view recursive self-improvement as a compounding meta-enabler. As one puts it, “once AI systems can meaningfully improve their own training pipelines, evaluation, architecture search, and tool-use, every other bottleneck (compute, data, architecture) becomes endogenous rather than exogenous.” Skeptics push back, with one arguing that self-improvement “is bottlenecked not by ideas, but by compute,” and another that it “comes after AGI, not before.”

Embodiment received consistently low allocations because the definition of AGI is restricted to non-physical occupations: “I do not see why embodiment would matter at all given the AGI definition focus on non-physical occupations.”; “Embodiment low because AGI definition excludes physical tasks.”

As they did when considering enablers of AGI, forecasters frequently note that the listed blockers overlap and interact. “All factors seem reasonable, and none seem irrelevant,” was a common sentiment.

Frequently named as a top blocker: “If AGI doesn't happen by 2040, the most likely reason is that current paradigms (transformers, autoregressive LLMs, reasoning RL on top of them) hit fundamental ceilings that scaling alone cannot overcome.”; “However much computing power, money and various types of data you have, if you don't have the architecture you won't reach AGI.” Few dispute that this blocker could play a key role, but many argue it is not likely to be the primary blocker: “I'm far less skeptical of technical barriers now. The industry seems to just scale through everything.”; “Our algorithms [are] good enough.”

Many tied this closely to algorithms, with one arguing that scaling limits are “somewhat downstream of the architectural problem, which would create the scaling law limit in the first place.” Several, however, expressed the sentiment that this blocker was independently crucial, with one respondent writing that if “returns to compute and data scaling diminish before AGI-level capabilities, no amount of additional resources will deliver a qualitative leap.”

Frequently cited as the most formidable non-technical hurdle, many emphasize the potential for backlash to AI—”job loss could very easily lead to social and political outrage”—and argue that the primary barrier to AGI “is increasingly not raw technical capability but the legal, regulatory, and societal environment in which Al is deployed.” But a sizable minority question the ultimate power of this blocker. One respondent writes, “Politics is too inept to stop anything I think, no matter what they do,” and another that “political, social, and funding barriers are less of a problem, as strong financial incentives and competition between organizations keep progress moving forward.” Echoing the latter sentiment, another respondent writes that political barriers “don’t typically prevent a sufficient technology from emerging” but instead “shift its location, timing, and deployment.”

Many highlight this factor as crucial if agentic persistence and, by extension, AGI is to be realized. As one respondent writes, “Memory is a serious blocker. Without the ability to hold and update what it has learned, no system can build on its own experience.” Another points out that “genuine continual learning without catastrophic forgetting, episodic memory, and persistent skill acquisition remain unsolved at frontier scale. Others see this issue as likely to be tractable: “learning and memory seems to be an obstacle that is currently being overcome (e.g. context windows increasing dramatically).”

While a few respondents treat data as the core barrier—”I continue with the mental model that it is all about the data, essentially, and everything else (algorithms, compute-scaling...) is ‘just’ an optimization”—others argue “we are likely to scale with synthetic data and virtual environments.” (Although one respondent warned that “synthetic data and self-play help but carry model-collapse risk.”) Some link better training data “directly to embodiment, where robotics becomes the bottleneck.”

Typically rates low because FRI’s AGI definition is non-physical: “Embodiment remains at 0% as a blocker. For this definition of AGI, physical interaction is not required.” A minority, however, argue physical grounding is necessary “even for cognitive tasks” with one writing that the failure of robotics to keep pace with language models is “the single most plausible technical reason AGI is delayed.”

Some respondents note infrastructure is downstream of investment, and a failure in one reflects a failure in the other. Both are rarely mentioned as primary blockers, with one respondent writing “capital is currently abundant,” and another that “we’ve not yet seen investment slow down so it’s hard to consider that a limiting factor at this point.” A few respondents, however, point to the potential for a geopolitical shock to quickly raise the salience of this blocker. As one writes, “It is certainly possible that some shock, such as Taiwan invasion, leads to a collapse of infrastructure and funding.”