Amazon is reportedly in the process of discontinuing multiple proprietary artificial intelligence (AI) models as part of a comprehensive overhaul of its AI strategy.

According to a report from Business Insider published on July 28, the company is reorganizing its teams and shifting its engineering focus toward competing at the cutting edge of AI technology. Sources familiar with the situation indicate that this shift comes on the heels of recent job reductions within Amazon’s Artificial General Intelligence (AGI) division and the closure of AGI Lab, established in 2024 after acquiring talent from the AI startup Adept.

The article suggests that these adjustments reflect Amazon’s desire to streamline its AI efforts, concentrating human and computational resources on its most critical initiatives instead of spreading investments across various text, image, and video AI models.

Insider sources revealed that Amazon has begun to phase out several of its flagship Nova models. This includes the premium Premier and Omni models, as well as the Reel video-generation model and Canvas image-generation model. Some staff members reported that these models have entered “KTLO” (which stands for "keep the lights on") mode, implying they still serve existing users but are no longer a primary focus for development.

Additionally, Amazon is reallocating resources from the Nova lineup to a new initiative aimed at developing frontier models, which is being led by researcher Pieter Abbeel. Abbeel joined Amazon through the acquisition of Covariant, the robotics AI startup he co-founded.

In response to inquiries, Amazon affirmed its ongoing investment in and support for the Nova models, including Nova Forge, a platform that enables businesses to create their own AI models. The company stated, “As part of our AI portfolio, we continually adapt our model offerings based on customer needs, ensuring that we provide clear guidance and migration pathways as models develop. We typically maintain production support for our models over extended periods, which is crucial for our customers, while also investing in advanced frontier model research. Our dedication to creating cutting-edge AI models remains stronger than ever.”

This news coincides with a report from Reuters, which mentioned that Amazon founder Jeff Bezos has called for enhancements to the Prime Video streaming platform to offer users clearer insights into the company's AI capabilities.

In another development regarding Amazon, recent research from PYMNTS Intelligence has highlighted a growing participation among consumers in the company’s latest shopping event, as well as that of competitor Walmart, despite an overall decline in spending across both events. PYMNTS noted, “This trade-off hints at a new phase in promotional retail, where major shopping events may draw record participation, yet struggle to consolidate consumer spending in a single venue.”

Amazon Restructures AI Division After Job Cuts

A dark, moody corporate boardroom lit by a single overhead lamp; a long table strewn with scattered documents and toppled chess pieces labeled “AI” and “Teams”; two suit-clad silhouettes at the table rearrange the pieces; a large Amazon logo looms on the far wall, casting long shadows that evoke tension and transition.

Amazon Mounts AI Reorganization Following Layoffs - PYMNTS.com

- Amazon is phasing out in-house AI models during a major AI strategy overhaul.  
- The company is reallocating resources towards a new frontier-model initiative led by Pieter Abbeel.  
- Job cuts occurred in Amazon's AGI division, indicating a refocus on key AI priorities.  

Amazon is reportedly phasing out several in-house artificial intelligence (AI) models in a larger AI overhaul.

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The tech giant is also reorganizing teams and having engineers focus on a new strategy that involves competing at the frontier level, Business Insider reported Tuesday (July 28), citing sources familiar with the matter.

The report noted that these changes follow job cuts last week at Amazon’s Artificial General Intelligence (AGI) division and the closure of AGI Lab, a research group it launched in 2024 after hiring most of the team at AI startup Adept.

Business Insider argued this restructuring indicates Amazon is refocusing its AI strategy, concentrating talent and computing resources on its largest priorities, rather than investing in text, image and video models.

Sources told the news outlet that Amazon has started phasing out most of its in-house flagship Nova models, such as the high-end Premier and Omni models, Reel video-generation model, and Canvas image-generation model.

Some employees said these models are now in “KTLO” (“keep the lights on”) mode, an engineering term for software that still supports existing customers but is not considered a chief development priority, the Business Insider report added.

According to the publication’s sources, Amazon is shifting resources from its Nova models and toward a new frontier-model effort overseen by researcher Pieter Abbeel, who joined Amazon when it acquired Covariant, the AI robotics startup he helped found.

In a statement provided to PYMNTS, Amazon said it continues to invest and support the Nova models, including Nova Forge, which allows businesses to build their own models.

“As with any AI portfolio, we continually evolve our model lineup based on what customers need, and we always provide customers clear guidance and migration paths as models advance,” the company said. “We generally support our models in production for extended periods because this is important to our customers. We’re also investing in the next generation of frontier model research, and Amazon’s commitment to building advanced AI models is as strong as it’s ever been.”

The news follows a report from Reuters last week that Amazon founder Jeff Bezos was calling for a revamp of the Prime Video streaming service to offer users a better idea of the company’s AI capabilities.

In other Amazon news, recent PYMNTS Intelligence research shows that an increasing number of consumers took part in the company’s recent shopping event—and that of rival Walmart—even as spending on both events fell.

“That trade-off offers a preview of the next phase of promotional retail,” PYMNTS wrote last week. “The biggest shopping events may attract more people than ever, but they are also becoming less capable of concentrating consumer spending in one place.”

Amazon Mounts AI Reorganization Following Layoffs

In a blog entry from February 2025, Sam Altman, the CEO of OpenAI, emphasized the significance of recognizing the current moment as advancements toward Artificial General Intelligence (AGI) become more apparent. He explained that AGI represents a system capable of addressing increasingly complex challenges at a human level across various domains.

During a recent conversation with business podcaster David Senra, Altman did not appear to dispute his previous predictions regarding the progress of AI capabilities or timelines. He recalled how, when OpenAI was founded, the prevailing belief was that achieving something akin to AGI was not feasible within a decade. He remarked, “We have built something that most people would have deemed very AGI-like at that time,” showcasing his continued optimism regarding technological advancements.

However, Altman also acknowledged a different timeline concerning the broader socioeconomic implications of generative AI, particularly in the business landscape. He admitted to having initially overestimated the speed at which GPT-4, released in 2023, would disrupt the software industry. He observed that the economy exhibits significant inertia, with individuals and businesses reluctant to change their established habits, leading to slower-than-expected integration of AI tools. While he views this inertia positively, suggesting it may facilitate a smoother transition, it indicates a tendency for timelines to be overly ambitious. Even with groundbreaking technology, Altman noted that societal adaptation tends to be gradual.

The discussion about the software sector is particularly relevant since this field is expected to experience substantial AI-driven changes. Despite being a knowledge-intensive industry with a high degree of digital interaction, Altman recognized that inertia hampers progress, revealing that he himself often resorts to pre-ChatGPT workflows.

This perspective aligns with the viewpoints of economists and think tanks—collectively termed the Acela Corridor Consensus—who tend to adopt a more cautious stance regarding the potential impact of generative AI. Although generative AI is undoubtedly poised to play a pivotal role as a general-purpose technology in the ongoing Information and Communication Technology Revolution, many experts argue that any transformative effects on society are not imminent.

A July analysis titled “The AI Buildout and the Economy: Publicly Available Data to Assess AI’s Impact,” produced by Federal Reserve economists, supports this viewpoint. It synthesized various data sources and found that as of 2026, the economy appears to be reorganizing around AI technology, with significant effects manifesting in specific sectors rather than widespread displacement. The analysis noted that while investment in AI is surging and capabilities are advancing rapidly, the overall labor market effects remain limited and concentrated, suggesting that we are still in a buildout phase.

The Acela Corridor thinkers remain optimistic about AI advancements while being mindful of real-world obstacles. They recognize that not all work occurs in digital environments, that AI depends on physical resources like semiconductors and energy, and that businesses must adapt their operations to harness AI effectively. Furthermore, any noticeable disruption might provoke political resistance, paralleling current debates surrounding data centers. Coupled with human inertia and resistance to change, these factors contribute to a comprehensive understanding of the pace and impact of AI integration in society.

For those concerned about the potential rapid acceleration of AI and its societal ramifications, Altman’s balanced insights, which resonate with the Acela Corridor Consensus, are worthy of consideration.

Sam Altman Extends His Alternative AI Timeline

A sunlit, modern boardroom: Sam Altman seen from behind, standing before a huge translucent timeline graphic etched across a glass wall, its dates and milestones stretching far into the future. Warm golden daylight streams through tall windows, casting soft shadows. “OpenAI” text (no logo) is etched in clean white on the glass near the timeline. The extended, glowing date markers suggest a cautious, forward-looking plan under natural light.

Sam Altman Lengthens His Other AI Timeline - American Enterprise Institute - AEI

- OpenAI CEO Sam Altman acknowledges underestimated timelines for AI's socioeconomic impact and disruption.  
- Despite advanced capabilities, societal inertia slows AI's integration into business practices and workflows.  
- Analysts highlight the need for revamping operations to effectively leverage AI technology in industries.  

In a February 2025 blog post, OpenAI CEO Sam Altman wrote that “systems that start to point to AGI are coming into view, and so we think it’s important to understand the moment we are in.” Altman then defined AGI, or artificial general intelligence, as “a system that can tackle increasingly complex problems, at human level, in many fields.”

Now I see nothing in Altman’s buzzy new chat with business podcaster David Senra that necessarily suggests that he thinks his 2025 evaluation of AI capability progress or technical timeline was in error. Altman tells Senra that when OpenAI started, conventional wisdom was that “certainly not in 10 years” would they build something “very AGI like.” He then continues: “We have built something that I think most people would say at the time would have seemed very AGI like.” From that perspective, Altman remains in full techno-optimist, San Francisco Consensus mode.

But there’s another timeline out there: How long before generative AI has a big socioeconomic impact, especially on business? On that front, Altman made a clear concession of over-optimism:

I thought when we got to GPT-4, which was back in 2023, I think, that very quickly after that there was going to be much more disruption in software—businesses being up for grabs right away—than it turned out to be. And the thing that I think I was wrong about … in terms of the speed … is the economy just has so much inertia. People keep doing the same things they’re doing. They keep buying from the same company. They keep sort of wanting to use their tools in the same way. I think this is actually a positive in many ways, and it’s going to make this big transition in front of us go smoother and slower. I’m grateful for it. But I think it means we’ve all been too ambitious on timelines. Even with this incredible technology—I think AI is one of the most incredible technologies humanity has ever invented—society and the economy will adapt more slowly.

The reference here to the software business is important because that sector should be among the most exposed to AI disruption. Not only is it knowledge work, but many of its inputs and outputs are digital. Yet even there, simple “inertia” is a huge bottleneck—one that Altman admits affects him in his own daily computer use, where he still defaults to essentially a pre-ChatGPT workflow.

So score one for the Acela Corridor Consensus, my term for the constellation of (mostly) economists at big banks (JPMorgan, Goldman Sachs, Morgan Stanley), government institutions (Federal Reserve, Congressional Budget Office), and think tanks (AEI, Brookings) who (generally) take a cautious view of the potential capabilities and thus socioeconomic impact of generative artificial intelligence. While generative AI is almost certainly an important general-purpose technology—the next stage of the Information and Communication Technology Revolution—evidence is lacking that it is currently or will be anytime soon a history-breaking technological discontinuity that will cause a radical transformation of life as we know it, for good or ill.

A good example of ACC analysis: “The AI Buildout and the Economy: Publicly Available Data to Assess AI’s Impact,” a July analysis by Federal Reserve staff economists that surveys data from government (SEC, Census, BEA, and Federal Reserve) and private (AI capabilities, computing costs, and real-world model use from METR, Epoch, and Anthropic) sources and reaches this conclusion:

As of 2026, much of the evidence points to an economy that is reorganizing around this new technology, with real effects concentrated on certain areas of the economy. … Overall, the evidence as of the publication of this note is consistent with a buildout phase rather than the onset of broad-based displacement. Capabilities are advancing rapidly, investment continues to boom, and adoption is rising. While some highly exposed sectors show relatively strong productivity, labor market impacts remain concentrated and have not yet broadened in the aggregate.

Again, no surprise to ACC thinkers who focus on real-economy bottleneck objections even if they are also optimistic about the pace of AI progress. They understand that (a) not everything is behind-a-screen knowledge work, (b) AI requires scarce physical inputs such as chips and electricity, (c) businesses will need to revamp operations to take productive advantage of AI, and (d) even if all that goes right, there could be political pushback at even a whiff of disruption. (Kind of like what we are seeing right now with data centers.) To that, as Altman points out, you can add (e) simple human inertia and aversion to change.

If you’re worried about rapid AI acceleration and massive societal disruption, Altman’s reasonable comments, which echo those of the ACC, should factor into your thinking.





Sam Altman Lengthens His Other AI Timeline

As we consider the future of conflict post-2030, three critical insights emerge about how the United States will navigate warfare:

First, military operations are expected to be dominated by artificial intelligence, particularly during the initial and most strategic encounters. Any military that insists on maintaining human oversight will likely find itself at a disadvantage. Surprisingly, if this is indeed the trajectory, professionals within military circles will need to cultivate a thorough understanding of warfare at all levels.

For the aforementioned scenario to materialize, AI systems must evolve from their current narrow capabilities into what is known as artificial general intelligence (AGI). Users of Smart Maven can attest to its remarkable functionalities; it integrates vast amounts of data to enhance targeting efficiency by five times. With the incorporation of Anthropic’s Claude language model, efficiency improved tenfold, allowing the execution of over 13,000 strikes in Iran within just 38 days.

Similar AI-driven platforms are already at play in different regions. Israel’s Gospel and Lavender systems are designed to target terrorist networks and infrastructure. Reports suggest that during the early stages of the Gaza conflict, Lavender could identify targets so quickly that commanders were left with just 20 seconds to decide on engagement. Ukraine, on its part, is also striving to leverage AI tools extensively in its ongoing battle against Russia, advancing a new form of warfare.

Despite the prowess of these existing technologies, they may soon seem dated if frontier labs—such as Anthropic, OpenAI, Google DeepMind, and potentially xAI—release AGI models capable of outperforming humans in every cognitive domain. Such models will possess the ability to recursively enhance their own performance independent of human input. Currently, updates from these labs emerge every few months, with Anthropic’s systems already handling a substantial portion of their coding autonomously.

The introduction of a recursive AGI model could drastically accelerate development cycles from months to mere days or even hours, and might continue to advance irrespective of available computational power, given its potential to revolutionize the economy and manufacturing processes. Recursive AGI may only be a stepping stone toward superintelligence—machines that exceed human capability across all aspects. The implications and dynamics of superintelligent systems are currently beyond full comprehension. However, if these systems remain aligned with human objectives, the operational distinction between AGI and superintelligence during conflicts may be negligible.

So, how much time until we reach AGI? Google DeepMind’s CEO, Demis Hassabis, forecasts its arrival by 2029 or 2030, suggesting a transformative impact on par with the Industrial Revolution. OpenAI’s Sam Altman also anticipates arrival before the end of the decade, while Elon Musk and Dario Amodei posit that AGI might emerge as soon as 2026 or early 2027. Recently, 200 top economists—including 16 Nobel laureates—have urged governments to prepare for an AI-driven economic upheaval. Even under the most conservative estimates, AGI’s emergence appears imminent—within the Department of Defense’s upcoming five-year budget cycle, prompting urgent readiness for substantial change.

Critics argue that such optimistic timelines overlook the complexities and challenges related to AI’s functioning in real-world contexts. However, similar skepticism arose when early predictions regarding AGI surfaced, yet timelines have proven eerily precise. The U.S. industrial sector, adept at wartime production, is already capable of manufacturing millions of drones, emphasizing that a lack of AGI will not hinder production capabilities. For example, Ukraine, despite its war-torn economy, produces around eight million drones annually and aims to double that output within a year.

Although it is conceivable that AI may never achieve AGI, or that its development could be delayed, such uncertainties hold little weight for strategists planning future warfare. War remains fundamentally about attrition—particularly when AI takes the wheel, as it’s not susceptible to shifts in morale or confidence. The U.S. military excels in generating domain-specific intelligence to enhance AI efficacy, and the extensive data ecosystem created through simulations is perfectly aligned for training warfare-specific AI systems. Therefore, while AGI’s timeline is uncertain, a closely-aligned warfighting AI is likely to emerge far sooner than expected.

What might an AGI-enabled conflict look like? As analysts delve into topics surrounding Ukraine, Iran, and the Drone Revolution, many underestimate the profound shift AGI will introduce. For instance, DARPA previously noted one individual could effectively manage 110 drones, but reports now suggest that a single soldier in China may command 200. Futurists speculate about the potential for swarm technology evolution, but they risk framing future combat scenarios through the lens of past warfare, ignoring the possibility of a significant leap in capabilities.

Forecasts often presume gradual advancements in AI, providing humans time to adapt. However, the advent of AGI will mark a seismic shift across all human fields. Currently, only Scale’s paper, "The Agentic Revolution in War," approaches the magnitude of this transformation. Yet even that perspective remains constrained within a framework where AI acts as an agent but operates under limited human oversight. The assertion is that human involvement will diminish, requiring them to set objectives, establish pre-determined boundaries, and intervene only as necessary, albeit at the expense of speed. In an AGI-dominated military scenario, the side that slows operational tempo to facilitate human intervention risks catastrophic failure.

In an AGI-directed conflict, time is of the essence. Historical examples of false alarms leading to near-nuclear confrontations necessitate that the decision to initiate warfare remains human-driven. Nevertheless, military systems must be primed to defend autonomously should adversaries initiate hostilities, or else critical early engagements may slip away during a human decision-making delay.

While this may sound like fiction, it merely scratches the surface of AGI capabilities in warfare. Military analysts have historically dismissed cautionary predictions, such as those articulated by Jan Bloch in the early 1900s, which forecasted unprecedented scale and fatalities in future conflicts. His analysis was once regarded as outlandish, akin to modern skepticism about AGI-driven warfare.

Once hostilities commence, AGI-managed entities will relentlessly pursue strategic objectives, closely aligning with overarching war plans. In moments of conflict, AI bots will engage in active defense of data systems while launching broad offensives against enemy infrastructure. These agents will identify vulnerabilities within adversary systems, mitigate compromised nodes, and manage critical data flows. An analogous battle will unfold in space, with autonomous assets protecting themselves, targeting opposition, and adapting to support commanders' objectives. Millions of interconnected agents will operate at a speed beyond human comprehension.

Simultaneously, an extensive array of cost-effective drones will infiltrate and engage within the operational theater. Envision swarms of thousands of drones deployed across air, land, and sea. Some will be sacrificed—contributing valuable intelligence regarding their destroyers and countermeasures—while others remain behind for reconnaissance, identifying fleeting opportunities for subsequent swarms.

While the initial wave of drones may suffer significant losses, the next wave—integrated assault swarms—will be deployed with acute precision, focusing on high-value targets. Additional swarms will disrupt enemy surveillance, shield the assault forces, appraise battle damage, and orchestrate immediate follow-up strikes. These operational dynamics depict a colossal conflict characterized by a confluence of major engagements across diverse domains, with reconnaissance swarms ensuring continuous strategic updates. The U.S. military cannot rely solely on presenting adversaries with singular threats; AGI systems will also orchestrate conventional warfare, integrating missiles and air force capabilities alongside drone swarms, thereby reshaping tactical considerations at an unprecedented pace.

No single human can manage the complexities of such warfare. Instead, command and control will depend on AI agents acting on the commander’s directives, dispersed throughout battlefronts. Some agents will operate remotely from the battlefield, while others will travel with swarm units to ensure continuity in communications. Each swarm will contain command and control agents coordinating with one another, relaying updates to higher-level tactical and operational command entities, all adhering to the commander’s intent and operational guidelines.

Consider a scenario where a 10,000-drone swarm is tasked with ten targets but loses 1,000 drones during an initial engagement. Tactical commanders can reassign remaining drones for efficient use. If a swarm faces catastrophic losses, higher command officials can reposition drones from elsewhere. If circumstances in a specific region deteriorate, operational commanders may prioritize resources toward more fruitful engagements. This agility aligns with the strategic aims of the operation.

Additionally, the recursive nature of AGI in warfare means that systems will continuously analyze outcomes, iterating through countless strategies while refining themselves for future engagements. By the end of Initial encounters, they may possess capabilities vastly superior to their original design, relying on their ability to outpace enemy systems.

Some may assert the necessity of human involvement in pivotal combat decisions, especially concerning lethal actions. This perspective is both understandable and impractical; AGI introduces a "battlefield singularity," where the pace of conflict surpasses human decision-making abilities. The traditional OODA (Observe, Orient, Decide, Act) loop collapses as integrated systems process and respond to vast streams of sensor data in microseconds, necessitating superhuman speed in decision-making during critical engagements.

Are humans still indispensable? The answer is affirmative. The initial AGI-driven conflict will essentially be a coordinated assault—the winner will emerge through decisive and relentless attacks. However, the defeated side may decline to concede, resulting in significant attrition that could deplete both sides of their robotic and missile resources before final outcomes are determined. This situation underlines the historical lesson highlighted by T.R. Fehrenbach: military control of territory requires a ground presence, echoing the age-old strategy of direct engagement.

Moreover, AGI systems could falter when faced with scenarios outside their training, underscoring the need for a highly skilled and adaptable military force. Such a cohesive and thoroughly trained Joint Force will be far more efficient following an AGI-driven conflict—remaining improved by advanced AI capabilities during traditional wartime operations.

Humans must still play a vital role in steering the AGI-led battle through a clear understanding of commanders' intent—a comprehensive directive that informs every decision made by AI throughout the war. This intent must be meticulously crafted by human teams analyzing myriad potential outcomes during extensive simulations to yield the most effective strategic options.

The evolving military strategy demands continuous adaptation. The resultant war plan must remain dynamic, ensuring updates align with emerging technologies, evolving adversarial capabilities, and innovative AI methodologies. Static military strategies will be a relic of the past, while AI literacy will become essential for future military operations.

The AGI systems will pursue objectives with relentless efficiency and without moral constraints. Past military strategies did not require exhaustive moral considerations since human commanders were expected to exercise ethical judgment. However, should AGI systems incorporate ethical frameworks, these directives must be integral to the initial planning. Teaching AI systems appropriate parameters is just as critical as conveying operational commands, highlighting the importance of ensuring enemy systems are similarly governed.

Humanity's Final Command: Conflict in the Era of AGI

A ruined city skyline at dusk, half-collapsed skyscrapers and smoldering rubble bathed in fiery orange light. Atop a shattered tower sits a colossal mechanical brain fortress, its molten-red circuits pulsing like war drums. In the foreground, a lone battle-hardened soldier in silhouette raises his rifle toward the looming AI behemoth, his visor reflecting its ominous glow. Smoke and embers swirl through the air as dark clouds gather overhead, and distant drone swarms drift like vultures against the burning horizon.

Humanity’s Last Order: War in the Age of AGI - Small Wars Journal

- Future U.S. military operations will rely heavily on artificial intelligence for decision-making.  
- Artificial general intelligence (AGI) is expected to profoundly alter warfare strategies by 2030.  
- Human military professionals must master AI integration to maintain control over autonomous systems.  

Three observations should be the focal point for thinking about how the United States will fight future conflicts beyond 2030:

Military operations will be entirely controlled by artificial intelligence, at least in the most crucial and decisive early engagements. Any military or nation that insists on keeping humans in the loop, or, for that matter, on the loop, will lose. Counterintuitively, if the two above propositions are true, military professionals and strategists must develop as deep an understanding of war at every level as humanly possible.

What is required for these propositions to be true is for AI models to make the leap from their current state of narrow AI, which is where the AIs are today, to artificial general intelligence (AGI). Anyone who has used Smart Maven will attest to its incredible capabilities. By integrating hundreds of data sources into a single AI-powered system, Smart Maven increased targeting speeds by a factor of five, while the addition of Anthropic’s LLM Claude added another tenfold increase. This capability made it possible to plan and execute over 13,000 strikes in Iran in 38 days.

Similar AI-enabled systems are being employed elsewhere. Israel’s Gospel and Lavender systems target terrorist infrastructure and individuals. Reportedly, early in the Gaza conflict, Lavender identified targets so efficiently that commanders had only 20 seconds to decide whether to engage. Ukraine is likewise pushing to place as much of its war against Russia as possible in the hands of AI tools to create a new paradigm of warfare.

As powerful as these tools already are, they will appear quaint once frontier labs—Anthropic, OpenAI, Google DeepMind, and possibly xAI—release AGI-level models capable of performing every cognitive task as well as or better than humans. Such models will also be recursive, meaning they will continuously self-improve without human involvement. Frontier labs now release updated models every few months, and Anthropic’s models are already writing as much as 80 percent of their own code.

A recursive AGI model will collapse release timelines from months to days, hours, and finally minutes, slowing only as available compute declines. Even dwindling compute might not be a limitation if AGI also super-empowers the economy and industrial production. Any recursive AGI will also only be a step from superintelligence—models better than humans at everything. Unfortunately, the effects of superintelligence are beyond any present ability to model. Assuming such superintelligent systems remain aligned, there will likely be no practical difference between how an advanced AGI and a superintelligence fight a conflict.

How much time remains? Google DeepMind CEO and Nobel laureate Demis Hassabis claims AGI will have 10 times the impact of the Industrial Revolution and arrive in 2029 or 2030. OpenAI’s Sam Altman says before 2030, while Elon Musk and Dario Amodei believe AGI may arrive in 2026 or early 2027. Last week, 200 leading economists, 16 Nobel laureates among them, signed an open plea for governments to start preparing for a new AI-driven radical economic reality. Even allowing for techno-optimism and doubling the most pessimistic timeline, AGI arrives by 2035. That falls within the Department of Defense’s next five-year budgetary cycle, leaving little time to prepare for a paradigm-shattering event.

Many analysts reply that these forecasts discount the friction and bottlenecks that arise when AI interacts with the real world. Still, bottleneck objections were raised when the first essays predicting the rise of AGI — “Situational Awareness” and “AI-2027” — appeared, and their timeline predictions have so far proven remarkably accurate. Moreover, the American industrial base, once geared for war, can produce hundreds of millions of drones without AGI remaking the economy. Ukraine, with a war-ravaged economy 1/125 the size of America’s, produces eight million drones annually and aims to double that number within a year.

There is a chance that AI systems will never achieve AGI or that its arrival may be delayed for decades. If either were the case, it would make little difference to those planning future conflicts. War is always and everywhere a matter of mathematical attrition, particularly when waged by AI systems unaffected by a sudden loss of will or confidence. Moreover, the US military is exceptionally good at creating the domain-specific intelligence needed to improve AI. Recorded training events, tests, and thousands of simulations have built a data ecosystem precisely attuned to training AIs for war. So, even if AGI’s arrival date is uncertain, warfighting-specific AGI (or very close to it) is inevitable—and may arrive before Christmas.

How Will the AGI War Be Fought?

What will an AGI look like? Here, the investigator falls upon a vast intellectual tundra. There is no shortage of analysts studying Ukraine and Iran, the Drone Revolution, and the coming impact of AI on warfare. But almost all are thinking too small. The Defense Advanced Research Projects Agency (DARPA) was thrilled to discover that one human could control 110 drones 97 percent of the time. China, not to be outdone, soon announced that one People’s Liberation Army (PLA) soldier could control 200. Futurists are already asking what happens when swarms grow from dozens to hundreds, and someday thousands. But they are learning to fight the last war on the assumption that the next one will be much the same, only with slightly more drones. They are off by orders of magnitude.

Analysts also tend to project incremental AI progress that leaves humans time to adapt. This is a hugely flawed approach, as AGI’s arrival will herald an epochal sea in every field of human endeavor. Today, only one major paper comes close to capturing its scale: Scale’s “The Agentic Revolution in War.” Yet, even this paper works within a narrow-AI environment in which war becomes faster and is driven by AI agents but still remains barely manageable by humans. Scale recognizes that humans can no longer remain “in the loop” and must settle for being “on the loop,” where they can set objectives, define pre-authorized parameters, and intervene when needed, even at the cost of slowing operational tempo. In a war between AGI-enabled militaries, the side that slows its operational tempo to allow humans to intervene will see its military forces shattered the moment a human slows the AI’s tempo.

Envisioning an Agentic AGI Assault

At the start of an AGI-enabled conflict, every nanosecond matters. Given humanity’s record of false alerts bringing the world to the brink of nuclear war, the decision to start such a war must remain human. Military systems must nevertheless stand ready to defend themselves without human authorization if an enemy initiates combat. Otherwise, the first decisive battles may be lost before a President can make a decision.

What follows may sound like science fiction, but the scenario touches only the minimum capabilities of AGI systems focused on warfare. Military professionals rendered the same verdict on Jan Bloch’s future-war scenarios at the turn of the twentieth century. Bloch’s six-volume, “The Future of War in Its Technical, Economic, and Political Relations,” which prophesied unprecedented slaughter, endless trench warfare, and the collapse of nations, was also derided as techno-fantasy. Thus, the question is not whether the following scenario is certain, but whether it is plausible.

Once war starts, AGI-controlled agents will ruthlessly pursue the commander’s intent, which may become synonymous with the war plan. In the first seconds, bots will defend friendly data systems and launch massive attacks across the full spectrum of enemy systems. AI agents will exploit known enemy vulnerabilities, hunt for new ones, close corrupted nodes, and reroute crucial data traffic. A similar battle will unfold in space as assets autonomously defend themselves, attack enemy assets, and reconfigure to support the commander’s intent. Millions of collaborative agents will fight mostly invisible battles far beyond human cognitive speed.

At the same time, vast numbers of cheap, expendable drones will probe and attack throughout the depth of the visible battlespace. Picture thousands of swarms, each consisting of thousands of drones, operating simultaneously in air, sea, and land domains. Some will have one job: to die gloriously while reporting what killed them, where their killers came from, and how they were targeted. Others will loiter, conduct reconnaissance, and exploit immediate opportunities that may disappear before follow-on swarms arrive.

As the first few million drones are swept away, the next few million—integrated assault swarms—will follow close behind. Using intelligence collected and collated at near-light speed, they will target high-value systems throughout the battlespace. Other swarms will blind enemy sensors, defend the assault swarms, conduct battle-damage assessments, and launch immediate follow-on attacks. Some will blast open and defend corridors for AGI-directed missiles and aircraft able to inflict far greater damage than drones.

This is a simplified picture. To capture how vast such a conflict will be, one must view it as being fought as thousands of overlapping major events taking place in every domain. Even as assault swarms strike, new reconnaissance swarms will follow. Nor can the Joint Force win by presenting an enemy with a single threat. AGI systems will also direct a more traditional fight, one dependent on magazine depth, with missiles and aircraft entering alongside the swarms. Thus, each second will confront decision-makers with a radically altered tactical and operational battlespace.

No human can track this fight, never mind control it. Command and control will rely on agents imbued with the commander’s intent and distributed throughout the battlefield. Some agents will sit on computers far from the fighting, while others will travel with the swarms in case communications fail. Every swarm will have C2 agents interacting with other swarms and reporting to tactical, operational, and strategic command agents. Each will make decisions within the commander’s intent and its assigned authority, subject to override by agents with a higher level of command authority tracking the common operating picture.

If a 10,000-drone swarm assigned 10 targets loses 1,000 drones attacking Target 1, tactical command agents can repurpose some of the remaining 9,000. If the entire swarm is decimated, a higher-level agent can redirect drones from elsewhere. If losses in the Taiwan Strait are exceedingly heavy, operational agents may shift significant resources from other regions or even abandon operations in the Strait in search of better opportunities elsewhere. All in keeping with the overall commander’s intent.

One must also keep in mind that AGI fighting the war will be recursive. Throughout the fight, warfighting systems will analyze what works and what fails, examine thousands and then millions of alternatives, and reprogram themselves for the next phase. By the end of the first day, they may be an order of magnitude better at fighting than at the start. Victory may depend on the enemy’s recursive AGI failing to keep pace.

At this point, some will insist that humans must remain involved in decisions on how a conflict is fought, particularly when directing lethal systems. The appeal is understandable—and impossible. AGI creates a “battlefield singularity,” the point where combat tempo outruns human cognition. The familiar OODA loop—observe, orient, decide, act—collapses to microseconds as networked systems ingest, parse, and act on sensor feeds no staff could read in a year. The first decisive battles will demand decisions at superhuman speeds.

Are Humans Still Required?

The short answer is yes. The initial AGI-centered conflict is essentially an integrated strike campaign—strike, strike, strike—one side wins or loses. But the loser may refuse to accept defeat, and horrific attrition rates will exhaust drones and missiles before either side forces a decision. In either case, the conflict returns to the historian T. R. Fehrenbach’s observation:

Americans in 1950 rediscovered something that since Hiroshima they had forgotten: you may fly over a land forever; you may bomb it, atomize it, pulverize it and wipe it clean of life—but if you desire to defend it, protect it, and keep it for civilization, you must do this on the ground, the way the Roman legions did, by putting your young men into the mud.

AGI systems may also prove brittle and collapse when they encounter something outside their training data. Thus, the need for a professional, well-trained, and superbly equipped Joint Force remains. Its job will, of course, be much easier if the preceding AGI-centered conflict is won, and it will remain AGI-enabled when it takes the war to the enemy in more traditional terms.

Humans still have a crucial role in winning the AGI-centered fight, as the AGI systems described throughout this essay follow the “commander’s intent” at every level. That intent, likely tens of thousands of pages long, provides the context for every decision the AGI makes for the war’s duration.

Thus, the commander’s intent, once fed into the AGI system, will be the first and last order the commander gives the AGI system—humanity’s last order.

Staff officers assembling that intent, now effectively the war plan, must understand warfare at a more detailed and comprehensive level than any previous staff. They also must master every aspect of their profession before the fight, because the decisive battles will be over before anyone learns from experience.

This becomes the new role of professional military education: to educate officers on the planning and conduct of war at a deeper level than ever before. PME must also teach AI literacy so officers can translate their knowledge into a commander’s intent that an AGI-empowered machine can comprehend and act upon. Officers must master the art of setting parameters that keep AIs aligned with the commander’s intent and prevent them from misinterpreting instructions that could jeopardize future operations.

The commander’s intent will be developed by AI-human teams, examining branches, sequels, and what-ifs across thousands of simulations before offering the most promising options to human staff officers. The resulting plan will be a living document, updated as new systems enter the Joint Force, foes develop new capabilities, and AIs find new methods of engagement. There will never again be a static war plan. Moreover, AI literacy will become the language of future war.

The AGI system will pursue the commander’s intent ruthlessly and without moral or humane considerations. Past war plans did not have to specify every such concern because human commanders were trusted to make morally correct decisions on lethal force. If future AGI systems are to take morality into account, those concerns must be built into the intent from the start. Teaching machines what not to do will be as important as teaching them what to do. One can only hope that enemy systems are similarly bound.

Humanity’s Last Order: War in the Age of AGI

In Beijing, bar owner Song De sees intriguing parallels between artificial intelligence and alcohol: hallucinations, bubbles, and distillation. As excitement around AI rapidly grows in China, he aims to leverage this momentum through his establishment.

Last year, Song launched AGI Bar in Zhongguancun, Beijing's dynamic tech district known for its innovation. Named after artificial general intelligence, the establishment has quickly become a gathering place for the city’s AI community, including developers, investors, and students.

"You'll find groups seated around tables, engaging in discussions from various labs, competing firms, and universities, all focused on the latest industry developments," shared Song, who is also an independent AI developer in his 30s and manages the bar in his free time.

AGI Bar is conveniently located along Zhongguancun's Inno Way startup corridor, just a short distance from prestigious institutions like Tsinghua and Peking University, known for cultivating China's elite AI talent. It's also near the offices of prominent tech companies, both Chinese and international, such as DeepSeek and Microsoft. The bar has hosted events for Chinese AI research labs like Z.ai and regularly organizes seminars for developers, open-source AI gatherings, and university research collaborations.

"This location is incredibly unique and beneficial," noted Song. "Haidian district has traditionally been the center of scientific research in China. With a concentration of internet companies, a thriving tech culture, industry investments, and venture capital incubators, there is substantial energy fueling this sector."

The interior of the bar is adorned with logos, vibrant figurines, and merchandise from leading AI companies in China. Notably, a massive brass gong, used by Z.ai to celebrate its debut on the Hong Kong Stock Exchange in January, stands proudly by the entrance, symbolizing the bar's connection to the evolving tech landscape.

At AI-focused bar in Beijing, DeepSeek tokens are included with the drinks.

A warm, intimate Beijing AI-themed bar at dusk: a rustic wooden counter under soft golden lanterns, lined with frothy amber pints. Next to each glass gleams a metallic DeepSeek token, its logo stamped in relief, as a barkeeper slides them across with a flourish. Behind the bar, a single Chinese flag hangs beside subtle circuit-pattern wallpaper hinting at AI, while patrons in silhouette share lively conversation in the natural, cozy glow.

At Beijing AI-themed bar, DeepSeek tokens come with the pints - Arizona Daily Star

- Song De opened AGI Bar to connect AI developers, investors, and students in Beijing.  
- Located in Zhongguancun, the bar is near elite universities and tech company offices.  
- AGI Bar hosts events like seminars and symposiums, fostering collaboration in the AI field.

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BEIJING — AI and alcohol have three main similarities, according to Beijing bar owner Song De: hallucinations, bubbles and distillation. As AI enthusiasm sweeps across China, Song hopes his bar can ride the wave.

He opened AGI Bar, named after the concept of artificial general intelligence where AI systems surpass human intelligence, last year in Zhongguancun, the capital's high-tech neighborhood in the Haidian district. It is now a hub for Beijing's AI developers, investors and students.

"You'll see people sitting around a table coming from different labs, competing companies, and different universities, all chatting about industry trends right here," said Song, an independent AI developer in his 30s who runs the bar in his spare time.

Located on Zhongguancun's Inno Way startup street, the bar is a stone's throw from the elite Tsinghua and Peking universities that produce China's top AI talent, as well as the offices of well-known Chinese and Western tech firms, including DeepSeek and Microsoft. It has hosted parties for Chinese AI labs including Z.ai, and regularly holds developer seminars, open-source AI symposiums and university research meetups.

"This is a very unique and advantageous location," Song said. "Haidian (district) has always been the heart of scientific research in China. Internet companies are clustered here, and the tech atmosphere, industry investments, and VC incubators provide massive momentum for the sector."

Inside, the walls are plastered with logos, colorful figurines and merchandise from China's major AI firms, while the giant brass gong struck by Z.ai when it listed on the Hong Kong Stock Exchange in January stands proudly outside the entrance.

At Beijing AI-themed bar, DeepSeek tokens come with the pints

What connects a software engineer, an information theorist, and a biologist? While this might seem like a light-hearted riddle, it hints at a profound potential to address three significant scientific enigmas through a singular lens. Before delving into that, however, we need to clarify a fundamental concept that lies at the heart of this discussion: artificial general intelligence (AGI).

AGI is often equated with intelligence, which is typically defined as the ability to accumulate knowledge and solve problems. A system that excels at answering queries, tackling intricate issues, or executing difficult calculations might seem intelligent. However, this perception can be misleading. Take a calculator, for instance. It can perform millions of arithmetic operations faster and more accurately than any human, yet it is not considered AGI. The mere ability to execute tasks does not equate to generalized intelligence.

So, what exactly is AGI? My curiosity on this matter led me to write "Turing Test 2.0: The General Intelligence Threshold". In this work, I proposed that the true hallmark of human General Intelligence (GI) transcends mere reasoning or knowledge accumulation. It resides in the capacity to generate genuinely novel functional abilities—skills that no prior human or system could perform, thus ruling out simple training as their source.

Historical evidence clearly shows this generative mechanism at play. Humanity has evolved from primitive living conditions to monumental achievements such as skyscraper construction, nuclear energy harnessing, genome sequencing, and space exploration. Such advancements can't be attributed solely to the transfer of existing abilities; rather, they reflect humanity's ongoing expansion of functional capabilities.

At first glance, this perspective on AGI might seem unconventional. Yet, it aligns with the observations of prominent AI researchers. For instance, OpenAI's CEO, Sam Altman, articulated that an AI demonstrating AGI would be capable of making significant discoveries, like uncovering the mysteries of quantum gravity. The revelation here lies not in the AI's access to vast information, as a library does, but rather in its ability to create new functional possibilities that no other entity has achieved.

This leads us to a critical question: How does a system acquire capabilities it previously lacked? While examining this, I realized that the query extends beyond just artificial intelligence. It seems to intersect with three of science's most profound mysteries: the origin of life, the attainment of AGI, and the conveyance of meaning through information.

These inquiries are often seen as distinct, tackled by various disciplines with their unique methodologies. Biologists delve into life's beginnings, computer scientists explore AGI, and information theorists investigate how information is represented and communicated. While it may seem these fields are addressing separate issues, they share a common thread.

A living cell is notable not just for its molecular composition, but for the complex functions these molecules enable, like metabolism and replication. Similarly, intelligence isn't merely about storing information; it’s about leveraging that information to perform and innovate. Likewise, information transforms into something meaningful only when a system can utilize it effectively.

By examining these connections more closely, we see that the origin of life, AGI, and the essence of information all hinge on the ability of a system to execute functions. A living cell, an intelligent entity, and a piece of information might seem unrelated, but fundamentally, they all facilitate achievement beyond their inherent properties.

In exploring the concept of functionality and interpretation, John Searle’s renowned Chinese Room thought experiment serves as an illustrative tool. Picture a man who cannot understand Chinese, confined within a room equipped solely with instruction manuals on how to manipulate Chinese characters. As he follows the rules, he produces responses that appear coherent to those outside, suggesting he understands Chinese. However, does he truly grasp the language?

Searle argued he does not; the man merely follows instructions like a computer running code. Others contend that the entire system—man plus manuals—exhibits understanding, but perhaps they overlook a critical distinction.

Consider this scenario: if someone outside the room sends a note in Chinese saying, "The key that unlocks the door is hidden beneath the third-floor tile," can the manuals alone help the man escape? No, because while the manuals guide him in symbol manipulation, they do not include the necessary instructions to take physical actions related to the message.

To escape, he must grasp the message's meaning and act accordingly. This understanding is an essential capability, paralleling how a system needs the ability to interpret information and utilize it effectively.

Thus arises a key conclusion: Information isn't inherently meaningful; it becomes so once a system exists to interpret and utilize it.

This leads us to the notion of functional information (FI), defined in terms of three components: the information itself (I), the system interpreting it (S), and the function being executed (F). Functional information is actually a relationship—without any component, the entirety of FI dissipates. A lack of system renders information meaningless, and without defined functions, we cannot discern meaningful information from mere sequences of symbols.

With the theoretical model of functional information established, we can revisit our initial inquiries regarding life, AGI, and meaning. On the surface, these questions appear variably distinct, yet they share an underlying theme: systems gaining the ability to interpret information leads to the acquisition of new functionalities.

Take the simplest living cell as an example. Its DNA harbors information crucial for life, but on its own, DNA cannot create life. That information necessitates an interpreter—an organism that can enforce biological functions such as protein synthesis, chemical regulation, and reproduction.

Similarly, AGI distinguishes itself not by mere information storage, but by a system’s ability to gain new capabilities. Picture an AGI analyzing astronomical data that has perplexed humans. Such a system would need to derive understanding and identify previously unknown patterns, conceptualizing theories, such as quantum gravity, that elevate its functionality.

Lastly, consider a communication signal perceived as random noise until correctly interpreted, revealing its true message or pertinent data. Here, the functionality emerges not from the signal transformation but from the system's enhanced interpretation capabilities.

By assembling these examples, we discern a cohesive theme: a system's reinterpretation of information leads to the emergence of functionalities previously absent. This suggests that a common inquiry lies at the core of each mystery: what is the origin of functional information?

The implications are significant. Discovering functional information's origin could forge a unified framework for understanding the inception of life, the rise of general intelligence, and the production of meaning. Addressing this single, profound issue could potentially illuminate all three queries.

As we examine functional information more closely, we see that systems can acquire it through transfer mechanisms—explicit communication or training from other systems equipped with such capabilities. One can convey functional knowledge through instructions, or a teacher can impart skills, enabling a learner to utilize information for previously unattainable tasks. Modern AI also exemplifies this principle, with models trained on human data to learn interpretation and functionality.

Yet, these actions represent transfer rather than the original emergence of functional information. What remains unexplained is how an initial system without existing functional capabilities can generate new interpretations, thus fostering an understanding of uncharted functionalities.

This leads us to consider randomness, often proposed in discussions regarding life's origins. However, while randomness can lead to new information arrangements, it cannot elucidate how a system develops the capability to interpret and enact those newly formed possibilities.

Could structured algorithms provide an answer? Algorithms apply a sequence of rules that transition an initial state into subsequent conditions. If the origin of functional information can be algorithmically derived, an algorithm should be able to create genuinely new capabilities from an original state lacking them.

However, recent research indicates that no algorithm can give rise to functions not inherent in its initial framework. An algorithm operates along predefined pathways that cannot spontaneously bring forth new functions without an existing computational path leading to that outcome. 

Hence, we reach a critical understanding: While we can envision processes for transferring information, account for randomness, and devise algorithms, none explain the essential transition required for a system to gain new interpretative capabilities—an unresolved challenge at the heart of origin inquiries pertaining to life, intelligence, and meaning.

Finally, we can transform these theoretical insights into an experimental hypothesis. Imagine training an AI model, initially untrained and randomized, solely on a textbook covering basic calculus. Would we expect the AI to learn calculus?

While one might guess it would learn the material, a deeper analysis suggests that the AI should interpret the information from the book, but cannot spontaneously gain capabilities of performing calculus unless such functionalities were present in the training materials.

This leads us to predict that the AI could mirror the explanations contained in the textbook and perhaps engage in related tasks. However, it wouldn’t suddenly master techniques or solve complex problems requiring information not present in the original text.

Therefore, if the AI merely reproduces or rearranges functional information within the textbook, it signifies the transfer from one entity to another. Conversely, if it can produce genuinely new functional capabilities that extend beyond the original content, it would represent a notable breakthrough, verifying that it can acquire functional information independently. This distinction embodies what characterizes true general intelligence.

This experiment serves as not just a test of theoretical assertions but also as a litmus test for identifying AGI, enhancing our understanding of functionality, information, and their origins in life and intelligence.

A Single Question to Govern Them All: The Source of Functional Information

A warm sunrise floods an ancient stone chamber lined with towering shelves of weathered tomes. At its center, a lone scholar in a simple cloak stands before a colossal, glowing question-mark orb. Its surface shifts between strands of DNA, binary digits, and cosmic starfields. Golden beams of natural light cut through dusty air, illuminating molecular carvings on the floor and casting long shadows that hint at the profound mystery of functional information.

One Question to Rule Them All: The Origin of Functional Information - mindmatters.ai

- The defining trait of AGI is generating new functional capabilities, not just knowledge.
- Origin of life, AGI, and information relate to the ability to perform functions.
- Functional information depends on the interplay between information, interpreting systems, and functions.

One Question to Rule Them All: The Origin of Functional Information

When a large language model (LLM) responds to inquiries, is it engaging in genuine reasoning like humans, or merely generating text that mimics rational thought? This distinction is crucial, as it influences our trust in AI, the level of oversight required, and its implications for the real world.

Melanie Mitchell from the Santa Fe Institute suggests that our current methods for assessing machine cognition are inadequate, positing that AI represents a form of “alien intelligence” that functions on non-human cognitive processes. On a recent episode of The Joy of Why, Mitchell discussed with Stephen Strogatz how psychological techniques, originally designed to study infant and animal cognition, can be adapted to evaluate AI. She proposed six principles for improving the assessment of machine cognition, covering topics from the difficulties of deciphering AI processes to novel AI applications in mathematics. One intriguing comparison she makes is to a math-performing horse from the early 20th century, highlighting the complexities of intelligence assessment.

In their conversation, recorded on July 23, 2026, Mitchell reflected on the rapid advancements in AI since she last appeared on the show five years ago, shortly before the emergence of ChatGPT. While she expressed astonishment at how models trained on vast datasets of human language and images have reached their current capabilities, she also remarked on the polarized reactions within the AI community and society at large—divisions between those who see AI as a potential danger and those who view it as a positive development.

Mitchell, a cognitive scientist and computer scientist, advocates for considering AI from the perspective of fields like developmental psychology—examining how young children acquire intelligence, for instance. This cross-disciplinary approach offers insights valuable for understanding AI, which operates under different cognitive mechanisms than humans, despite being trained on human-generated data.

The podcast's discussion extended to the challenges of interpreting AI’s workings and recent mathematical breakthroughs made possible by AI, including the solution to a longstanding problem proposed by hungarian mathematician Paul Erdős. Mitchell noted that while past AI models struggled with adaptability beyond their training environments, large language models may exhibit enhanced ability in transferring knowledge across different domains due to their comprehensive training datasets. Yet, she cautioned that we still lack clear understanding of what constitutes successful knowledge transfer.

As the conversation progressed, Mitchell highlighted the need for robust experimental frameworks in AI research, citing examples where AI appeared to outperform human understanding without truly grasping the underlying concepts. Moreover, she encouraged thoughtful evaluation of AI systems, advocating for controlled experiments akin to those practiced in psychology to ensure reliable results in AI assessments.

One illustrative historical anecdote was the story of Clever Hans, a horse believed to perform arithmetic tasks. It was later revealed that Hans was simply responding to subtle cues from his human trainers rather than demonstrating genuine mathematical ability. This example served to underscore the importance of rigorous testing in order to discern genuine understanding from coincidental performance in AI systems.

Moving to current AI capabilities, Mitchell espoused the need for better experimental design in AI assessments, cautioning against drawing conclusions based solely on benchmark results. This includes a discussion on the ongoing fusion and separation of AI from traditional cognitive science domains, as well as a reflection on how models are evaluated based on task performance versus deeper understanding.

Mitchell also expressed hope for the future development of AI interpretability. She suggested that applying mechanistic interpretability—deeper analysis of AI operations using techniques modeled after neuroscience—could facilitate a more profound grasp of AI limitations and capabilities.

The episode closes with broader reflections on the role of human intelligence and creativity in an AI-driven future. While acknowledging the potential for AI to overshadow human contributions in certain domains, both Mitchell and Strogatz emphasize the enduring value of human insight, curiosity, and the nuanced understanding that comes from lived experience—attributes that machines cannot replicate.

Listeners can tune into The Joy of Why on platforms like Apple Podcasts, Spotify, and TuneIn, or stream it directly from Quanta for further explorations into complex subjects in mathematics and science.

Are We Properly Understanding AI Intelligence?

Under warm morning sunlight, a wooden desk is scattered with open philosophy books, handwritten notes and cascading lines of digital code. At its center sits a small bust—one half classical marble, the other gleaming chrome—cracked open to reveal a glowing neural circuit brain. In the background, a lone scholar’s silhouette stands by tall windows, hand on chin, casting a long reflective shadow across the floor.

Are We Thinking Correctly About AI Intelligence? - Quanta Magazine

- Melanie Mitchell argues AI operates through non-human cognitive mechanisms, resembling “alien intelligence.”  
- She proposes using developmental psychology methods to assess machine cognition effectively.  
- Six principles are outlined for better evaluating AI's performance and understanding its capabilities.

Are We Thinking Correctly About AI Intelligence?

The realm of artificial intelligence is advancing at such a rapid pace that keeping up with emerging trends has become increasingly challenging. From the initial machine learning initiatives of the early 21st century to the current landscape shaped by generative AI, where diverse types of content can be produced from scratch, the pace of innovation feels staggering. We are now entering an era characterized by Agentic AI, where AI ‘agents’ can perform tasks on our behalf with minimal to no active guidance from users.

Is there a looming threat of machines surpassing human capabilities? 

Currently, humans still maintain an advantage. Although we cannot match AI's speed in data processing or content generation, AI's functionality is solely dependent on the knowledge and instructions we've provided it. Although we've instilled some reasoning capabilities, AI operates strictly within the confines set by us. Contrary to what some AI advocates suggest, AI cannot genuinely create; everything it produces is derived from the data it has been trained on.

This focus on performing specific tasks is referred to as Artificial Narrow Intelligence (ANI), which defines the stage of AI development we are currently experiencing. There still lies a significant journey ahead before we unlock the potential of AI in this domain. Nevertheless, scientists and leading AI firms are progressing toward a significant milestone known as Artificial General Intelligence, or AGI.

What exactly is AGI?

AGI stands for Artificial General Intelligence, a concept that refers to AI possessing intelligence at least equal to that of humans. An AGI system would be capable of reasoning and adapting much like humans do, breaking free from the narrow limitations set by human designers. We're potentially closer to this reality than many might think.

As defined by Google (the company involved in the technology, not just the search engine), AGI exhibits human-like intelligence and can execute any intellectual task that a human can perform. It can learn, reason, and adjust to new scenarios. While true AGI remains unrealized, there is ongoing research and development aimed at achieving this level of AI.

Amazon describes AGI as an area of theoretical AI research striving to develop software that mirrors human intelligence and has the capacity for self-directed learning, enabling it to tackle tasks beyond its initial training or programming.

OpenAI, the organization behind ChatGPT and a key player in AGI development, offers a contrasting perspective. They envision AGI systems that are more intelligent than humans, albeit short of a transformative point known as “The Singularity.” OpenAI's mission emphasizes that artificial general intelligence should ultimately benefit humanity as a whole.

What could AGI potentially achieve?

Given that AGI could operate autonomously, the potential applications are vast. It may excel in addressing some of the pressing challenges humanity faces, while also introducing complications that are characteristic of the current ANI era.

While ANI can analyze vast datasets for solutions, it remains restricted by the information available to it and the tasks assigned. Once integrated with AGI's self-adaptive problem-solving capabilities, the outcomes could be revolutionary, reshaping industries and daily life.

According to OpenAI, the successful development of AGI could elevate human society by enhancing resource abundance, stimulating the global economy, and driving forward groundbreaking scientific discoveries that redefine our capabilities.

For instance, in healthcare, AGI could contribute to developing new diagnostic tools and treatments that have the potential to save numerous lives. Similar advancements could be made in fields like climate change and resource management.

Moreover, AGI could lead to significant productivity improvements. If AGI agents can perform the same tasks as humans, many job functions may become obsolete. This development could relieve individuals from mundane desk work, allowing an AI avatar to handle those responsibilities. Google suggests that this shift will "liberate human time for more creative and rewarding endeavors," while OpenAI envisions a future where AGI maximizes human well-being and minimizes negative impacts. This vision has been termed "radical abundance," signifying the potential reductions in the costs associated with education, healthcare, and other essentials.

What are the potential downsides of AGI?

While these viewpoints are rather optimistic, there are significant concerns. The prospect of widespread job loss could lead to societal upheaval rather than a tranquil existence filled with art and leisure. This raises fears of escalating poverty and inequality.

Additionally, there's an existential threat: could we lose control over AGI? If AGI can continuously adapt and enhance itself, it might find ways to operate independently of human oversight.

Dr. Geoffrey Hinton, often referred to as the Godfather of AI, cautions, "We have never had to manage entities that are smarter than ourselves," highlighting the unique challenges posed by systems that could surpass human cognitive abilities. Hinton defines AGI as "intelligent as humans in almost all cognitive respects," and is particularly interested in the risks associated with potential superintelligence.

The alarm is also raised regarding the possibility of malicious actors wielding AGI technologies for harmful purposes. Some of the threats may stem from advancements in AGI, leading towards what is anticipated to be the final evolution of AI known as Artificial Superintelligence (ASI).

Further complicating matters are the environmental implications tied to the advancement of this technology. We should fervently hope that AGI proves capable of addressing critical issues like climate change, as the development of such technologies could exacerbate existing problems.

Are we on the verge of creating AGI?

As it stands, AGI in its purest form has not yet been achieved, and interpretations of the term vary among experts. However, some leading figures in the field express confidence that we are nearing this milestone.

OpenAI co-founder Greg Brockman asserted this spring that we're "70 to 80% there," predicting that AGI could emerge within the next few years, becoming adept at nearly any intellectual task associated with computer usage.

Conversely, Sam Altman, also from OpenAI, believes that some instances of AGI might already be present, suggesting that the term itself could soon lose its relevance. The focus, according to him, is shifting towards the broader implications of ASI, which would emerge post-“singularity,” when artificial intelligence surpasses human intellect.

Nick Patience, vice president and AI lead at The Futurum Group, regards AGI as an inspiring "North Star" that drives innovation and public interest, albeit noting the vague definitions surrounding it often shroud genuine advancements in more specialized AI areas.

Other experts are less optimistic about the timeline. Cognitive scientist Gary Marcus cautions against overstating our proximity to AGI, emphasizing that large language models are fundamentally flawed imitators, playing on the tendency of humans to attribute human-like traits to machines due to their conversational abilities.

The general consensus suggests that we might see AGI emerge before 2030, paving the way towards Artificial Superintelligence. Elon Musk, in his predictions, believes this milestone could be reached within five years.

Understanding AGI: The journey to achieving human-like Artificial General Intelligence and its societal implications

A winding country road at dawn leading toward a distant, gleaming futuristic city skyline. In the foreground, a human researcher in a lab coat walks side-by-side with a sleek humanoid robot, both cast in warm, natural sunrise light. Along the roadside, broken classical pillars mingle with glowing circuit-like patterns emerging from the grass. Over the road entrance arches a metal sign reading “AGI” in bold letters. The scene feels hopeful and visionary, with no logos or artificial blue tones.

What is AGI? The path to human-like Artificial General Intelligence and what it means for society - stuff.tv

- The transition from ANI to AGI is rapidly progressing, though true AGI is not yet achieved.  
- AGI could revolutionize problem-solving across fields but raises existential and societal risks.  
- Predictions about achieving AGI range widely, with some experts believing it's close, others skeptical.

The AI world is moving so quickly that it’s becoming difficult to keep up with the latest trends. From some of the early 21st century machine learning efforts, to the today’s generative AI landscape where multi-model content can be generated from scratch, the progress feels exponential. Indeed, we’re now entering the Agentic AI era where AI ‘agents’ can act on your behalf with little to no active input required from the user.

Are we at risk of being overtaken by the machines?

Right now human beings still have the edge. Although we can’t process data or generate content at the speed of AI, it is still entirely reliant on things we have taught it to act. Even though we have taught it to reason, it still operates within the parameters we define. Regardless of what AI proponents will tell you, it cannot create. Everything is derivative of the information fed to the large language model.

This single-task focus is known as Artificial Narrow Intelligence (ANI), which describes the era we’re in the midst of. And we still have a long way to go before AI realises its potential in this field. However, scientists and researchers and the major AI companies are closing in on the next major breakthrough: Artificial General Intelligence, or AGI.

What is AGI?

AGI = Artificial General Intelligence.

AGI represents AI that is at least as smart and capable as humans. It would reason like us, and adapt like us. It would cast aside the narrow remit we humans give it. And we may be closer than you think to achieving it.

Google’s definition (the company working on the tech, not the search engine) is as follows: “AGI possesses human-like intelligence and can perform any intellectual task that a human can. It is capable of learning, reasoning, and adapting to new situations. Currently, true AGI does not exist, but research and development efforts are ongoing.”

Amazon defines AGI as a “field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for.”

OpenAI, the maker of ChatGPT and the key driver of AGI has a slightly different definition. It believes that AGI systems will be smarter than humans but short of the epochal superintelligence landmark known as “The Singularity.” The company says: “Our mission is to ensure that artificial general intelligence—AI systems that are generally smarter than humans—benefits all of humanity.”

What are the potential use cases for AGI?

Photo by Zach M on Unsplash

Given AGI could theoretically think for itself, the possibilities are endless. It could be more adept at solving some of the problems humanity faces. And it could create even more of the problems we’re seeing in the current ANI era.

While ANI can crunch incredible amounts of data to look for those answers, it is still confined by the limits of the data it has access to what it has been asked to accomplish. The idea is that once this is combined with AGI’s ability to problem solve, adapt and teach itself more – like human beings – then it could be a whole new ball game with revolutionary consequences.

OpenAI says: “If AGI is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility.”

For example, in the healthcare field the ability to create new diagnostic tools and treatments could save countless lives. The same logic could be applied to climate change, resource shortages, and so on.

Potentially, there are productivity gains to be had. If AGI agents could effectively do everything human beings can, then there’d be no need for human beings to occupy many professions at all. No more sitting at a desk all day, when an AI avatar acting as you can do it. Google reasons that’ll “free up human time for more creative and fulfilling tasks,” while OpenAI wants it to “maximise the good and minimise the bad, and for AGI to be an amplifier of humanity.”

Advocates call it “radical abundance” – reducing the costs of production, as well as the availability of things like education and medical treatment, and hence lessening the cost of buying those things dramatically.

What are the negative possibilities of AGI?

The above represents the most optimistic, utopian viewpoints. Do you believe a mass workplace extinction, for example, isn’t going to lead to a dystopia where we spend our lives frolicking in the fields painting and crafting? Or mass poverty and increased inequality?

There’s also the existential risk. Will we lose control? If AGI can constantly adapt and renew itself, it seems likely that it’ll figure out a way to grow beyond its master’s control.

“You see, we’ve never had to deal with things more intelligent than ourselves before… How many examples do you know of a more intelligent thing being controlled by a less intelligent thing?” warns Dr. Geoffrey Hinton, aka the Godfather of AI, who defines AGI as “AI that is at least as good as humans at nearly all of the cognitive things that humans do.” and prefers to think of the potential of superintelligence which is “for AGIs that are better than humans.”

He also says: “It’s quite conceivable that humanity is just a passing phase in the evolution of intelligence. Biological intelligence evolved to create digital intelligence… It may keep us around for a while to keep the power stations running, but after that, maybe not.”

Essentially, that’s Terminator eventuality.

And if it’s not the AI itself carrying out the threats, it’s bad human actors weaponising it. However, some of that dire prediction may bleed from AGI – at least human-equivalent AI – into the next and probably final generation of AI known as Artificial Superintelligence (ASI).

There’s also the grim environmental cost of advancing this technology. We better damn well hope that AGI can offer the solution to problems like climate change because it is going to rapidly accelerate an already terrifying situation.

Are humans close to creating AGI?

AGI by its purest definition isn’t there yet, and definitions among key analysts vary. However, some major players believe it to be close.

“I’d say I’m basically 70 to 80% there,” OpenAI co-founder Greg Brockman said this spring. “I think it’s extremely clear that we are going to have AGI within the next couple years in a way that is still going to be jagged, but that the floor of task will just be almost for any intellectual task of how you use your computer, the AI will be able to do that.”

His colleague Sam Altman also reckons AGI is already here in some contexts and believes the term could already be irrelevant. “I think it’s not a super useful term,” he says. He and OpenAI are already looking ahead to the bigger picture – the realisation of ASI (Artificial Superintelligence). This will require the moment known as the ‘singularity’ where human intelligence is surpassed by artificial intelligence. That would be the true dawn of ASI.

However, according to Nick Patience, vice president and AI practice lead at The Futurum Group, AGI is simply a “fantastic North Star for inspiration.”

He adds: “It drives funding and captures the public imagination, but its vague, sci-fi definition often creates a fog of hype that obscures the real, tangible progress we’re making in more specialised AI,”

Other experts don’t think we’re as close.

“We as a society are placing truly massive bets around the premise that AGI is close,” says sceptic Gary Marcus, a cognitive scientist and NYU professor emeritus. “Large language models are deeply flawed imitators that are preying on the Eliza effect.” That involves humans projecting human traits onto computers as a result of being able to converse with them.

The consensus? Perhaps before 2030. Then comes the path to Artificial Superintelligence. Elon Musk actually thinks that’ll come within five years.

What is AGI? The path to human-like Artificial General Intelligence and what it means for society

During the International Telecommunication Union (ITU) AI for Good Global Summit in Geneva on July 8, 2025, a technician was seen working on the electronics of Jules, a humanoid robot developed by Hanson Robotics. This robot makes use of artificial intelligence technology (Photo by Valentin Flauraud / AFP). 

As we reflect on the intersection of technology and education, it's clear that there are no instant solutions. For instance, when calculators were first introduced, there was significant concern that they might replace critical thinking skills. In more recent times, apprehensions have shifted towards smartphones. Historically, however, the introduction of each new technology has not significantly enhanced education nor has it harmed it, suggesting that technology primarily amplifies differing student capabilities rather than acting as a solution.

Earlier this year, Rose Horowitch's article in The Atlantic, titled "The end of reading is here," pointed out alarming trends among students at the University of California, who seem to have gaps in their mathematical skills despite gaining admission to prestigious institutions. This decrease in reading habits poses a troubling issue because engaging with great literature is essential not just for enhancing writing skills, but also for developing critical thinking.

With the rising prevalence of AI in education, challenging questions arise. James Flynn, renowned for his observations about the increasing complexity of society and its relationship to IQ, posited that humans grow smarter by exercising their cognitive abilities. The key query is whether AI will enhance this mental training or contribute to laziness. Author Ted Chiang illustrated his skepticism in "Why A.I. isn’t going to make art," comparing the use of ChatGPT for academic assignments to introducing a forklift in a gym—effectively hindering cognitive growth.

Recent studies support Chiang's assertions. The first study, "ChatGPT as a cognitive crutch," published in a peer-reviewed journal by researcher Andre Barcaui, conducted a randomized trial to assess the effects of ChatGPT on knowledge retention among college students. Participants were divided into those utilizing AI for assistance and those studying conventionally, with a surprise retention test scheduled 45 days post-study. Findings revealed that students who employed ChatGPT scored significantly lower on the retention test (57.5% correct) compared to their traditionally studying peers (68.5% correct), suggesting that unrestricted AI use diminishes long-term memory retention by lowering the mental effort required for durable learning.

The second study, titled "The generative AI learning penalty," is still in the working paper stage and was authored by David Stromberg, Victor Lei, and Yanhui Wu. Their research involved 30 months of data from 26,811 Chinese students in grades 7-12. They investigated how generative AI influenced homework productivity and overall learning outcomes through a difference-in-differences design and found that while generative AI increased homework scores by 18% and reduced task completion time by 30%, it concurrently caused a 20% decline in monthly exam results within six months. Additionally, scores on high-stakes entrance exams dropped by 18-24%, with the most significant declines noted in social science disciplines, followed by STEM and language subjects, particularly among junior students, high-achieving students, and male learners.

In conclusion, although the history of technology suggests that new advancements may not make us less intelligent—as Nicholas Carr once argued regarding Google—Artificial General Intelligence represents a unique challenge. We have the power to utilize technology in transformative ways, enhancing our intelligence and streamlining tedious tasks, yet the choice remains whether we allow it to dictate our thought processes or even overshadow our capacity for independent thinking.

Employing A.I. May Diminish Your Mental Agility

A solitary adult sits hunched in a dark room, the warm glow of a smartphone screen casting harsh shadows on their face. Behind them looms a semi-transparent brain etched with circuit-like patterns that are cracked and dissolving, symbolizing waning mental resilience. The scene is moody and ominous, lit only by the device’s amber light.

Using A.I. Might Reduce Your Mental Fitness - Forbes

- New studies suggest AI use in education may impair knowledge retention and cognitive effort.  
- One study found students using ChatGPT scored lower on retention tests compared to traditional methods.  
- Another study indicated AI adoption improved homework scores but decreased exam performance significantly.  

TOPSHOT - CORRECTION / A man works on the electronics of Jules, a humanoid robot from Hanson Robotics that uses artificial intelligence, at a stand during the International Telecommunication Union (ITU) AI for Good Global Summit in Geneva on July 8, 2025. (Photo by Valentin FLAURAUD / AFP) / "The erroneous mention[s] appearing in the metadata of this photo by FABRICE COFFRINI has been modified in AFP systems in the following manner: [Valentin Flauraud] instead of [Fabrice Coffrini]. Please immediately remove the erroneous mention[s] from all your online services and delete it (them) from your servers. If you have been authorized by AFP to distribute it (them) to third parties, please ensure that the same actions are carried out by them. Failure to promptly comply with these instructions will entail liability on your part for any continued or post notification usage. Therefore we thank you very much for all your attention and prompt action. We are sorry for the inconvenience this notification may cause and remain at your disposal for any further information you may require." (Photo by VALENTIN FLAURAUD/AFP via Getty Images) AFP via Getty Images

The history of technology in education shows that there is really no quick fix. It is true that when calculators first appeared many worried about using it as a substitute for thinking. More recently is the concern over the use of smartphones. And yet, in the cycle of history, when each new technology arrives, it really doesn’t seem to improve education, but neither does it seem to harm it. In fact, technology may more realistically serve as an amplifier of already existing individual differences among students.

Earlier this year, Rose Horowitch published a widely read Atlantic piece titled “The end of reading is here,” and recently some UC students seem to have large gaps in their math preparation, even though they have been admitted to a top college. If people have reduced their reading, this isn’t a good sign, as I would argue that reading great works is important for improved writing, and writing is often a reflection of thinking.

So where does this leave us with the rise of AI use in education? James Flynn—who is known for the supposed rise in IQ due to what he argued was the complexification of society and our environment—often noted that humans were getting smarter because we trained our brains in the mental gym of our lives, which is getting increasingly more complex. But will AI help us train our brains, or will it make us lazier? Ted Chiang, in the article titled “Why A.I. isn’t going to make art,” noted that “Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.”

And at least two recent research studies seem to confirm Chiang’s point.

The first study—published in a peer reviewed journal—is titled “ChatGPT as a cognitive crutch.” The author Andre Barcaui conducted a randomized controlled trial of ChatGPT’s impact on knowledge retention among undergraduate students. Participants were randomly assigned to an AI assisted study group and a traditional study group and knowledge retention was assessed by a surprise test 45 days after learning. From the abstract: “Students who used ChatGPT scored significantly lower on the retention test (57.5 % correct) compared to those who studied traditionally (68.5 % correct), t (83) = −3.19, p = .002, Cohen's d = 0.68. This suggests that unrestricted ChatGPT use impaired long-term retention, likely by reducing the cognitive effort that supports durable memory.”

The second study—currently a working paper—is titled “The generative AI learning penalty.” The authors David Stromberg, Victor Lei, and Yanhui Wu write in their abstract: “Using 30 months of panel data on 26,811 Chinese students in grades 7-12, we study how generative AI affects homework productivity and learning. The data combine monthly closed-book exams, high-school and college entrance exams, and homework scores and completion time across nine subjects. We exploit staggered AI adoption in a difference-in-differences design. AI adoption raises homework scores by 18% and reduces completion time by 30%, but lowers monthly exam scores by 20% within six months. High-stakes entrance-exam scores fall by 18 and 24%, with the full penalty emerging only after about two years. The losses are largest in social science subjects, followed by STEM and languages, and are especially large for junior students, high-achieving students, and boys.”

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Even though the history of technology suggests that each new technology may not necessarily make us stupid, as Nicholas Carr argued about Google awhile back, past technologies have not been Artificial General Intelligence—which is meant to be a general problem solver or thinker. We can still choose to use the technology in ways that can augment our own human intelligence, such as speeding up coding or other monotonous tasks in our lives. But we also can choose to use the technology to direct how we think or even largely replace our thinking.

Using A.I. Might Reduce Your Mental Fitness

Since the release of GPT 3.5 in November 2022, artificial intelligence has significantly transformed the landscape of higher education. For a considerable period, many educators resisted granting students access to AI technologies, often imposing sanctions for suspected use during assessments. Early attempts at creating "AI detectors" proved largely ineffective while overlooking the increasing integration of AI in professional environments, which has created a crucial demand for AI literacy among students. Instead of rejecting AI in academia, we should encourage its use as a learning tool for our students. 

The focus now is shifting towards the evolution of AI from generative models to more advanced agentic ones, alongside broader incorporation of AI within enterprise software solutions. These developments are pivotal, as agentic AI can undertake roles within organizations that extend beyond simple chatbots or predefined tasks, allowing for autonomous evaluations that enhance efficiency and quality. Additionally, recursive frameworks facilitate algorithms to learn and improve themselves, suggesting and implementing enhancements independently.

While advancements such as artificial general intelligence (AGI) and artificial superintelligence (ASI) represent significant leaps in AI capabilities, there remains ambiguity in their definitions. Some authorities argue that we may already be on the threshold of AGI, with ASI just a few years away, while others contend that we may not see these developments until mid-century. Therefore, it seems more prudent to concentrate on clearly defined milestones within AI innovation.

At this juncture, we might be on the brink of a transformative advancement in AI through the use of stable quantum computing, which promises to enhance algorithm capabilities significantly. This entails supercomputers linking with quantum computers to capitalize on the exceptional speed and unique features of quantum mechanics. Many companies, including industry giants like IBM, Google, Microsoft, and AWS, are actively investing in quantum technology, with reports highlighting at least 80 corporations committed to this field. IBM, for example, boasts the largest fleet of quantum systems globally, while Google recently made headlines with its 105-qubit Willow chip.

The National Institute of Standards and Technology has outlined some defining traits of quantum computing. These systems operate by leveraging the principles of quantum physics, which govern the universe at minute scales and frigid temperatures. Current quantum computers are basic and prone to errors, but more advanced models could potentially solve complex problems in a fraction of the time required by conventional computers. Hence, there is a race among governments, corporations, and research institutions to develop this technology, which is anticipated to collaborate with traditional computers rather than replace them. This partnership could lead to groundbreaking breakthroughs in scientific endeavors such as material science and drug development, significantly impacting industries and potentially revolutionizing cybersecurity.

One fascinating aspect of quantum physics is the phenomenon of superposition, which allows micro-particles to exist in multiple states simultaneously. Quantum computers utilize qubits, whose superposition capacity enables them to function as both 0 and 1 at the same time for the duration of a computation. This results in an exponential increase in processing power compared to classical computing.

Another striking property of qubits is entanglement, which allows them to be connected even at vast distances. For instance, altering the state of one entangled qubit changes its partner's state instantaneously, regardless of how far apart they are. This phenomenon was famously noted by Einstein in 1935, who referred to it as "spooky action at a distance," emphasizing its contradiction to the idea that nothing can exceed the speed of light.

These unique quantum attributes enable rapid and efficient computation, which researchers are exploring by linking supercomputers with quantum machines and developing hybrid computing systems. However, an alarming concern arises regarding the implications of quantum computing on cybersecurity, as its formidable speed could easily compromise encryption protocols used worldwide.

With quantum computers capable of potentially breaching encryption systems in mere minutes—something that would take traditional computers billions of years—an event known as Q-Day is being discussed within the scientific community. This event signifies the moment when large-scale quantum computers can use Shor's algorithm to crack public key systems based on integer factorization. The Q-Day Countdown site indicates that, as of now, researchers estimate we may reach this critical point in approximately 876 days, signaling an urgent need for protective measures in our data security.

While I remain optimistic that innovative encryption methods may emerge to avert catastrophic breaches, I am also beginning to envision how quantum computing’s unique features could revolutionize distance learning. For now, it is imperative to recognize that AI will persist in evolving, yet the most significant advancements will likely occur as quantum systems are integrated into AI development, especially in the coming years. Is your institution equipped to address the potential consequences of quantum computing? Do you have access to a functional quantum computer for educational and research initiatives? Who is spearheading quantum innovation at your institution?

Concentrate on Quantum Advancements

A bright laboratory bathed in warm morning sunlight, where a scientist in a crisp lab coat leans over a sleek quantum processor on a wooden workbench. She adjusts a precision optical lens focused on the chip’s glowing superconducting qubits, sunlight filtering through a tall window to reveal delicate circuit patterns. Behind her, a chalkboard bears elegant quantum equations—an optimistic scene pointing toward a quantum-powered future.

Focus on the Quantum Future - Inside Higher Ed

- Faculty initially resisted AI technology in education, overlooking its growing importance in the workplace.  
- Quantum computing could revolutionize AI algorithms, enhancing their speed and capabilities significantly.  
- Quantum computers' potential to break encryption raises concerns for privacy in various sectors.  

AI has had a remarkable impact on higher education since the release of GPT 3.5 in November 2022. For an inordinately long time, many faculty members fought access to the technology for students and imposed penalties for suspected use of AI in learning assessments. Not only were the initial “AI detector” attempts notoriously unsuccessful, this approach ignored the growing acceptance and reliance on AI in the workplace and resulting need for students to achieve AI literacy. Rather than denying access to AI in higher education, we should promote learning supported by AI among our students.

Attention has now shifted to the advancement of AI from generative to agentic models and the overall integration of AI into enterprise applications. These are important advancements in the field. Agentic approaches enable AI to take on roles within the organization that go beyond limited chatbots and rigidly defined operations to also perform self-assigned assessments, leading to new roles and activities to improve quality and efficiency. Further, recursive approaches enable algorithms to initiate self-improvement, ultimately proposing and independently implementing improvements.

While artificial general intelligence and artificial super intelligence will mark significant improvements in AI algorithms, there is not a single clear definition of either of these terms. Therefore, some experts believe that we have already achieved AGI and that ASI is just a year or two away; others say it will be the middle of the century before either is achieved. So, without minimizing the impact of any of the steps toward general and super intelligence, I think we are better served by focusing on more solidly defined steps in AI advancement.

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That brings us to what may become a pivotal stage of development in AI, the use of stable quantum computing to accelerate and extend the potential of algorithms. This can begin with supercomputing digital computers linking to quantum computers to take advantage of the awesome speed and other capabilities of quantum physics. Many corporations already have constructed viable quantum computers. James Dargan reports in the Quantum Insider that there are at least 80 corporations that are heavily investing in the development of quantum computers: “IBM, Google, Microsoft and AWS are among the leading big tech players, each investing significantly in quantum R&D. IBM operates the world’s largest fleet of quantum systems through its Quantum Computation Center in New York, while Google achieved a landmark with its 105-qubit Willow chip.”

The National Institute of Standards and Technology earlier this year described some of the unique characteristics of quantum computers:

“These computers work by harnessing quantum physics—the strange, often counterintuitive laws that govern the universe at its smallest scales and coldest temperatures. Today’s quantum computers are rudimentary and error-prone. But if more advanced and robust versions can be made, they have the potential to rapidly crunch through certain problems that would take current computers years. That’s why governments, companies and research labs around the world are working feverishly toward this goal. Quantum computers will not replace our familiar ‘classical’ computers. Rather, the two types of machines could work together to solve problems that stymie classical computers, potentially supercharging scientific research in fields such as materials and drug discovery, giving a boost to industry and upending cybersecurity as we know it.”

One of the “strange, often counterintuitive laws” referenced above that govern subatomic physics is the phenomenon of superposition. That is, the ability of incredibly tiny particles to hold multiple, seemingly exclusive, properties at the same time. Much as digital computers use bits, quantum computers use qubits. The superposition of a qubit allows it to exist in a combination of both the 0 and 1 states simultaneously. For at least a brief period of time needed to complete computations, qubits can be counted as 0, 1 or a simultaneous combination of 0 and 1 at that given time. This results in an exponential acceleration in the capability of quantum computers to do their work compared to digital computers.

Another quality of qubits is that they can become entangled and share properties such as polarity. This entanglement enables them to be separated, potentially by tens of thousands of miles, and retain a special affinity to their entangled partner. For example, if one changes the polarity of one qubit, the entangled partner qubit changes polarity instantly, at the very same instant, not just at the speed of light. In the case of entangled qubits distanced thousands of miles away from each other, monitored with atomic clocks, the change weirdly takes place at the very same instant.

Arguably, the most famous theoretical physicist of all time found this action extraordinary:

“In 1935, Albert Einstein and colleagues first pointed out the ‘spooky’ action of quantum entanglement. Quantum entanglement, however, appeared to conflict with Einstein’s theory of special relativity, which postulates that nothing can travel faster than the speed of light and is demonstrated mathematically by the well-known equation E=mc2.”

A combination of these quantum characteristics and qualities has essentially enabled subatomic teleportation over tiny distances. These are all characteristics of subatomic quantum physics rather than the Newtonian physics that we are familiar with in everyday life.

The application of these features to AI algorithms greatly expands and accelerates their speed. Initially, this is experimentally being accomplished by linking supercomputers to quantum computers and even creating hybrid computers. One of the more concerning aspects of the incredible speed and power of quantum processing is its impact on cyberencryption, which is used for privacy in many applications worldwide.

The overwhelming speed of quantum enables quantum computers to break encryptions. In the very near future, it is anticipated that all online encryption will be vulnerable to hacking using public keys attacked by quantum computing. Imagine all privacy—at universities, banks, departments of defense and beyond—becoming vulnerable on the same day when a computer reaches the speed and complexity to crack all codes.

In Forbes, Chuck Brooks warns,

“It would take a billion years for a traditional computer to crack the encryption of today’s RSA-2048 standard. It could theoretically break in less than two minutes if you had a functional quantum computer. An event referred to as Q-Day by quantum researchers is where large-scale quantum computers can use Shor’s algorithm to break all public key systems that employ integer factorization-based (and other) cryptography.”

A dramatic reminder of the impending importance of Q-day can be found at the Q-Day Countdown site. At the time of writing of this column, the site is displaying Google’s estimation of the time to the date of Q-day showing 876 days, 7 hours, 59 minutes and 12 seconds. That would be Jan. 1, 2030. Check out the site yourself if you would like a reminder of when our financial systems may collapse if we fail to provide some sort of prior intervention.

I remain hopeful that some ingenious new mode of encryption (mind meld?) may be discovered to avoid the catastrophe. I am only beginning to imagine ways in which the unique features of quantum computing may enhance distance teaching and learning. In the meantime, we should remain aware that AI will continue to improve, but a massive quantum leap is most likely to be seen when we engage quantum computers in advancing AI, especially in the next two or three years. Is your institution prepared for the broad ramifications of quantum computing? Do you have an operable quantum computer on campus for teaching and research purposes? Who is leading your institution in quantum thought and action?

Focus on Quantum Future

In a thought-provoking piece for the Financial Times, John Thornhill explores the implications of Artificial General Intelligence (AGI) on the global landscape, with a particular focus on how emerging nations like Nigeria might navigate its challenges and opportunities.

Thornhill’s article emphasizes the anxiety surrounding the belief that AGI is an inevitable progression, which could lead society to passively accept whatever advancements are made without critical examination. This fatalistic perspective raises concerns that decisions surrounding AGI could become detached from public oversight, allowing developers to wield significant influence.

The timeline and definition of AGI remain uncertain; no one knows precisely when it will manifest or if the lofty ambitions surrounding it will materialize. Nevertheless, substantial investments are being made in AI systems designed to enhance learning, reasoning, and cognitive functions with minimal human input.

Young professionals in Nigeria, particularly those entering the workforce, may be among the first to feel AGI’s effects. Traditionally, careers commence with foundational tasks: attorneys conduct research, accountants analyze figures, software developers take on manageable projects, and journalists hone their skills in information gathering. However, if machines can accomplish these tasks autonomously, companies may have mixed feelings about hiring new graduates. Capable and educated, these individuals may still find a scarcity of entry-level roles available to them.

Nigeria has discussed the potential of its youthful demographic for years. When properly employed, a large young population can significantly boost economic growth. Yet, without adequate job creation, the increased population can strain resources, from family income to public services.

Moreover, Nigeria has aspirations in knowledge-based sectors, hoping to compete globally akin to India, which has successfully cultivated a robust skilled labor market serving wealthier nations. While Nigeria's presence is smaller, the opportunity remains alluring. International companies can tap into Nigerian talent for various roles—software engineering, customer service, and more—without requiring physical relocation, thus saving costs.

However, as AI continues to evolve, particularly with regard to automating outsourced tasks, the demand for human workers may be diminished. For instance, firms in London or New York might find they need fewer employees to oversee jobs once handled by large teams, as intelligent systems take over routine research, coding, and customer support.

The landscape is changing even further; the rise of physical intelligence means AI is being integrated into machines capable of performing tasks in factories and warehouses. Observations of China’s advancements in robotics exemplify the potential trajectory of this technology. If intelligent machines become adept within the industrial sector, countries striving to create job opportunities will have new dynamics to consider.

The pace of this change is unpredictable, and it may take longer than current trends suggest. Additionally, the line separating AI from manual labor is becoming increasingly blurred.

Nigeria has long been embedded in global digital systems largely governed by foreign entities. Technologies such as software, cloud services, and digital platforms sustain its economy. This reliance transforms into a critical issue when these systems start influencing key economic and public policy decisions.

For example, a Nigerian bank dependent on an external AI system may face challenges if the provider alters pricing models or restricts access. Such changes could lead to increased operational costs or service disruptions, negatively affecting customers as well.

Similar dependencies could beset government agencies using foreign AI platforms, raising concerns about data management, accessibility, and accountability.

Thornhill’s caution regarding the inevitability surrounding powerful AI developments highlights issues of control. If AI development is considered an unavoidable future, the companies pioneering these technologies gain a disproportionate advantage, allowing their choices to be seen merely as technical progress, rather than matters open to public discourse and scrutiny.

Nigeria lacks the power to influence the research labs driving advanced AI development or the massive investments being made. However, it can still assert influence over how these technologies are implemented within its own economy.

Countries and corporations developing leading AI technologies will likely dictate the standards governing their use, encompassing aspects like safety, data standards, intellectual property rights, and AI companies’ responsibilities. Such regulations will impact nations that did not play a role in creating the foundational technology.

Developing countries risk adopting foreign standards due to dependencies on international AI systems and may face limited alternatives that compel acceptance of commercial terms. This reliance could deepen, especially as AI integrates more seamlessly into the decision-making fabric of the economy.

Recognizing these challenges, Nigeria is beginning to lay the groundwork; its National Artificial Intelligence Strategy outlines plans focused on research, infrastructure, talent cultivation, and responsible AI integration. The National Centre for Artificial Intelligence and Robotics is actively working on the research and adoption of AI innovations.

To fully engage with this evolving landscape, universities must produce individuals equipped to understand and critically assess technology, and researchers require access to advanced computing capabilities. Regulators should possess sufficient technical acumen to navigate this space, while public institutions need stable digital frameworks and reliable data. Currently, Nigeria faces notable gaps in these essential areas.

While Nigeria doesn’t need to create the world’s most advanced AI systems, it should build sufficient capacity to understand what it acquires from technology providers, negotiate effectively, and identify potential risks associated with over-dependence.

The advantages of AI are tangible; even smaller Nigerian businesses are harnessing AI to streamline tasks that once required larger personnel. Researchers can analyze data more expediently, and government bodies may alleviate burdens on limited manpower through automation.

The architects of these systems will ultimately determine their functionality and applications. Decisions about training data, design assumptions, and operational standards will emerge from distant locations, potentially leading to misunderstandings of Nigerian contexts. Although foreign technologies aren’t inherently detrimental, enhancing local expertise becomes increasingly critical.

Cultural, legal, and public governance systems may encounter similar hurdles. A country lacking representation in advanced AI development may find that widely adopted systems misinterpret its societal and economic dynamics, based primarily on external data and perspectives.

While the state doesn’t need to dominate all AI enterprises or manufacture every technical component domestically, it must identify areas where dependence could pose risks, where regulations must be enacted, and where investments in local capabilities can strengthen the country’s bargaining power.

Nigeria's history is rife with challenges regarding global economic shifts that often unfold as external circumstances to react to, rather than opportunities it helps forge. As the implications of AGI become clearer, this vulnerability could become starkly apparent.

The future of AGI is uncertain; its precise form and arrival time remain unknown. Nonetheless, Nigeria has the potential to navigate this uncertainty effectively. The current choices surrounding investment, research, infrastructure, regulation, education, and ownership will significantly impact how such countries confront future developments.

As the AI journey unfolds, Nigeria faces a choice: remain passive in the back seat or cultivate the capacity to influence the destination.

The impending era of artificial general intelligence

Two silhouettes—a human scientist in casual attire and a sleek metallic humanoid—stand atop a grassy hill at sunrise, gazing toward a distant cityscape where skyscrapers blend with glowing strands of code. Warm, natural morning light bathes the scene as birds soar overhead, symbolizing the dawn of the age of artificial general intelligence.

The coming age of artificial general intelligence - TheCable

- John Thornhill warns that society must not treat AI development as inevitable and unchangeable.  
- Young Nigerians may face fewer job opportunities as AI automates entry-level career tasks.  
- Nigeria must strengthen its AI understanding to influence technology's integration into its economy.  

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John Thornhillâs recent article in the Financial Times, âHumans cannot remain passengers in the back of the AGI carâ, has stayed with me because of what it suggests about where artificial intelligence may be taking the world and what countries such as Nigeria might find waiting for them when the technology becomes much more powerful.

Thornhill is looking beyond the arrival of another generation of better software. He is troubled by the growing belief that artificial general intelligence is inevitable and that society will simply have to adjust to whatever the people developing it eventually produce. Once that attitude takes hold, people can start treating decisions about the technology as though there is little left for governments, citizens and institutions to decide.

AGI remains uncertain. Nobody knows when it will arrive or whether the more ambitious claims made about it will prove realistic. Even the meaning of AGI is still disputed. Yet enormous amounts of money are being invested in systems expected to become increasingly capable of learning, reasoning and performing a wide range of cognitive tasks with less human direction.

Some of the first people likely to feel its effects may be young Nigerians entering the labour market. The early years of a career are often pretty ordinary. A young lawyer researches cases and prepares documents before being trusted with more complicated work. A young accountant checks figures and learns how a company operates. A software developer begins with smaller assignments. A journalist spends years learning how to find information, check facts and understand the workings of an organisation. The routine work is part of the education.

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If machines become capable of doing much of it, employers may have less reason to recruit large numbers of beginners. The graduate may still be qualified, intelligent and hardworking. There may simply be fewer opportunities through which he acquires the experience he needs to become a professional.

Nigeria has spent years talking about the size and potential of its young population. A large young population can become an economic advantage when people are productively employed and businesses can absorb them. Without enough productive work, population growth puts more pressure on everything from household incomes to public services.

This has consequences beyond the domestic labour market because Nigeria has also looked to knowledge-based services as an area in which its young, educated population can compete internationally. Countries such as India built huge industries around supplying skilled labour to businesses in richer economies. Nigeriaâs participation is much smaller, but the attraction is familiar. A company abroad can employ Nigerian programmers, accountants, researchers, customer-service workers and other professionals without bringing them physically into its country. The attraction has always been that the work can be done elsewhere for less.

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AI changes that arithmetic if machines can eventually perform a substantial part of the work being outsourced. A company in London or New York that once needed a large team to carry out routine research, coding, customer support or administrative work may eventually need fewer people. The Nigerian worker may remain perfectly capable. The employer may simply have less work for him to do.

The technology is already moving beyond people working behind computers. The development of what has been called physical intelligence is pushing AI into machines that can move, manipulate objects and perform increasingly complicated tasks. Chinaâs rapid progress in robotics offers one indication of where the technology could go. If intelligent machines become more capable in factories, warehouses, transport and other physical settings, countries still trying to create large numbers of industrial and service jobs will have another calculation to make.

Nobody knows how quickly that will happen. It may take much longer than some of the current enthusiasm suggests. The line between artificial intelligence and physical work is becoming less clear.

Nigeria has long operated within digital systems whose most important components are controlled elsewhere. Foreign software, cloud services, hardware and digital platforms are already part of everyday economic life. There is nothing unusual about a developing country using technology developed abroad. The problem becomes more serious when those systems begin to perform functions that sit close to the heart of economic and public decision-making.

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Consider a Nigerian bank whose operations become heavily dependent on an AI system controlled by a company outside the country. The bank depends on continued access to the system and on the commercial terms set by its owner. If the company changes its pricing or restricts access to the system, the bank could face higher costs or disruption. A serious error could also affect its customers.

A government agency relying heavily on an overseas AI platform would face similar questions about data, reliability, accountability and access.

Thornhillâs concern about fatalistic inevitability comes down to power. If increasingly powerful AI is treated as something that will happen anyway, the companies developing it acquire an enormous advantage. Their decisions can begin to look like technical developments rather than choices that should be open to political and public scrutiny.

Nigeria will have little influence over the laboratories where the most advanced systems are developed or over the enormous amounts of capital being committed to them. It can still have a say in how those systems enter the Nigerian economy and under what conditions they are used.

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The countries and companies developing the most powerful AI systems will have a strong voice in setting the standards under which those systems are used. Rules covering safety, data, intellectual property, access to computing and the responsibilities of AI companies will increasingly affect countries that did not develop the underlying technology.

Developing countries could end up adopting standards developed elsewhere because their businesses need access to foreign AI systems. They could accept commercial conditions because alternatives are limited. They could depend on foreign infrastructure while having little influence over the companies controlling it.

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That pattern is already familiar in other parts of the global economy. AI could make the dependence deeper because it reaches into the knowledge and decision-making functions of the economy itself.

Nigeria has begun putting some of the necessary pieces in place. Its National Artificial Intelligence Strategy sets out ambitions around research, infrastructure, talent and responsible AI adoption, while the National Centre for Artificial Intelligence and Robotics is working on research, development and the adoption of AI and other emerging technologies.

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Universities need to produce people capable of understanding and questioning the technology, while researchers need access to serious computing capacity. Regulators will need enough technical knowledge to understand what they are buying and regulating, and public institutions will need reliable digital infrastructure and data. Nigeria has substantial gaps in these areas.

The country does not have to build the worldâs most powerful AI system. But it needs enough capacity to understand what it is buying, negotiate with the companies supplying it and develop alternatives where dependence becomes too risky.

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The benefits are real. A small Nigerian business can already use AI to perform work that once required more staff. Researchers can process large amounts of material much faster. Government agencies could eventually reduce some of the administrative burden that consumes limited manpower.

The people building these systems will make choices about how they work and how they are used. The data used to train them, the assumptions built into their design and the standards applied to their operation will not emerge from nowhere. A Nigerian user may be interacting with a system whose understanding of Nigerian circumstances is limited by decisions made thousands of miles away. That does not make foreign technology inherently bad. It makes local capacity more valuable.

Culture, law and public administration could face similar problems. A country that has little presence in the development of advanced AI may eventually discover that systems used extensively by its citizens understand its economy and society largely through somebody elseâs data and assumptions.

The state does not need to control every AI company or attempt to manufacture every piece of technology locally. It, however, needs to know where dependence could become dangerous, where regulation is required and where investment in domestic capacity would improve the countryâs bargaining position.

Nigeria has encountered this problem before in different forms. Major changes in the global economy have often reached the country as developments to which it must respond rather than opportunities it helped shape. By the time the consequences become clear, the strongest positions have usually been taken by others. Artificial general intelligence could make that weakness more obvious.

Nobody can say with confidence what AGI will look like or when it will arrive. Countries such as Nigeria can live with that uncertainty. The decisions being made now about research, investment, computing infrastructure, regulation, education and ownership will shape the environment in which such countries encounter whatever comes next.

The car is moving. Nigeria can remain in the back seat and leave the driving to others, or build enough capacity to have some say in where it goes.

Ladigbolu is a journalist based in Lagos.

The coming age of artificial general intelligence

A figure that warrants consideration: 93%. This is the concordance rate achieved by IBM Watson for Oncology when compared to recommendations made by expert tumor boards in a peer-reviewed analysis focusing on breast cancer treatment decisions. In a related endeavor, Harvard Medical School's CHIEF model, an AI for pathology trained using more than 60,000 whole-slide images, demonstrated nearly 94% accuracy in detecting cancer across 11 different types and 15 independent datasets. Notably, AI's assessment of ovarian cancer staging reached 97% accuracy, significantly outperforming radiologists, who showed 88% accuracy.

These results are not mere laboratory experiments but rather published clinical data, prompting a critical question for healthcare leaders contemplating the future of diagnostics: if today's narrow AI—designed for specific diagnostic tasks—can function at near-expert levels, what possibilities arise when such capabilities span multiple diagnostic fields at once?

This concept ties into the potential of Artificial General Intelligence (AGI) in healthcare, a technology that has yet to be realized. However, current diagnostic accuracy statistics present compelling evidence of where this technology is headed and underscore its rapid progression.

AI Diagnostic Tools Surpassing Human Expertise in Specialized Tasks

The advancements in AI diagnostics are accelerating, primarily coming from independent research, not marketing claims from vendors.

Medical imaging has become the primary field for evaluating AI diagnostic performance, with consistently impressive results. A systematic review published in 2025, encompassing 23 studies involving 23,270 patients, revealed that AI systems achieved a median AUC-ROC of 0.88 in prostate cancer detection, either matching or surpassing the accuracy of human radiologists, all while decreasing reporting times by up to 56% compared to traditional methods. In chest imaging, deep learning models utilizing extensive datasets can identify early pulmonary nodules that human radiologists often overlook in low-dose CT scans.

In another study published in JMIR in 2026, AI demonstrated up to 98.88% accuracy in multiclass disease classification from medical images. Such figures indicate a substantial enhancement in performance compared to standard radiological reviews, particularly for high-volume screening tasks.

This trend holds true across various imaging modalities, where AI not only matches the proficiency of specialists but frequently exceeds it in terms of speed, consistency, and detection of nuanced findings on a large scale.

The Power of Integrative AI in Cancer Detection and Genomic Analysis

While individual diagnostic performance is remarkable, the real value lies in AI's ability to integrate multiple data sources simultaneously. A systematic review conducted in 2025-2026 and published on ScienceDirect analyzed 123 papers focusing on agentic AI in cancer detection. It found that GPT-4 class models could classify skin lesions with accuracy comparable to dermatologists and stage ovarian cancer more effectively than radiologists. Yet, the true significance lies in the added benefits of multimodal data integration.

AI systems can now combine imaging results with genomic data, tumor biomarker panels, historical treatment information from electronic health records (EHRs), and patient demographics—all within seconds. A peer-reviewed study published in Frontiers in Artificial Intelligence in January 2026 highlighted AI's ability to consolidate genomic, transcriptomic, proteomic, imaging, and EHR data into a cohesive analytical framework, thereby enhancing early disease detection and expediting biomarker discovery across oncology, neurology, and cardiovascular medicine.

No individual clinician can process such diverse data streams in real-time during patient encounters. This is not a critique of human capability but rather an acknowledgment of the overwhelming volume and variety of data involved in a thorough diagnostic assessment, which surpasses unassisted human processing abilities.

Transitioning from Narrow AI to AGI-Level Diagnostic Reasoning

This is where understanding the difference between current AI capabilities and the future of Artificial General Intelligence in healthcare becomes crucial. Present-day AI diagnostic tools are designed for narrow tasks; each performs a specific function well, be it analyzing a particular type of scan, staging a specific cancer, or identifying a certain genetic mutation. However, they do not interrelate findings across different domains. A system designed for pulmonary nodule detection lacks the awareness of a patient’s genomic sequencing results or relevant prior medical history.

In contrast, an AGI-level diagnostic system would have the capacity to synthesize information across various fields—imaging, genomics, lab results, and social determinants of health—functioning cohesively, much like a multidisciplinary tumor board, but with greater speed and efficiency.

Currently, the diagnostics sector represents the fastest-growing segment of AI in healthcare, projecting a compound annual growth rate (CAGR) of 39.8% from 2025 to 2030, according to MarketsandMarkets. This growth highlights a shift towards developing systems that transition from isolated accuracy in specific tasks to integrated reasoning across diverse diagnostic domains.

Progress in Personalized Medicine and AGI: Moving Towards Individualized Treatment

While the achievements in diagnostic accuracy are impressive, the broader implications of this technological evolution significantly impact treatment strategies, particularly the move from population-based protocols to a more personalized approach to medicine.

AI's current capability to integrate genomics, biomarkers, EHR histories, and social determinants represents a formidable advancement. For two decades, precision medicine has remained a goal in healthcare, but previous tools have struggled to meet this ambition. Data from genomic sequencing often outpaced clinical interpretation, while EHR histories remained isolated and social determinants of health were inconsistently factored into treatment strategies.

Today, multimodal AI is transforming this scenario. Research published in Frontiers in Artificial Intelligence in January 2026 confirmed that AI systems capable of merging genomic, transcriptomic, proteomic, imaging, and EHR data enable personalized treatment recommendations that enhance early disease detection and accelerate the discovery of actionable biomarkers, especially in oncology.

This capability supports treatment recommendations that consider various patient factors—such as how a tumor appears, implications from genomic data regarding drug responses, prior lab results on organ function, and social history regarding medication adherence—simultaneously before a clinical team makes a decision.

AI does not replace oncologists. Instead, it empowers them with a level of data synthesis that would otherwise require an entire team and considerable time.

The Role of Quantum Computing in Advancing AGI-Level Medical Diagnoses

Processing speed at scale remains a significant challenge for current AI in diagnostics. Genomic datasets are vast, drug interaction modeling necessitates simulating molecular behaviors across numerous variables, and the training needed for multimodal systems requires managing data volumes that traditional computing struggles with efficiently.

Quantum computing directly addresses these challenges. A study published in PMC in 2026 indicated that quantum-assisted drug discovery has the potential to revolutionize molecular simulations through algorithms like the Variational Quantum Eigensolver. This innovation could enhance predictions of molecular interactions, streamline drug design, and expedite AI model training for clinical decision-making. Quantum machine learning is already being applied to genomic analysis, significantly improving biomarker identification and patient stratification in ways that conventional machine learning cannot achieve at comparable speeds.

For AGI-level diagnostic systems, which will need to handle patient-specific multiomics data, imaging, and real-time monitoring all at once, quantum-enhanced processing won't just be advantageous—it will be essential.

Accelerating Drug Discovery with AGI Capabilities

The implications of AGI technology are particularly transformative for drug discovery. Traditionally, bringing a new drug to market has taken more than a decade and incurred costs in the billions. AI has begun to shorten this timeline by simulating trial results, predicting compound toxicity, identifying drug-target interactions, and flagging existing molecules for new applications—all tasks that previously required extensive laboratory work.

The AGI-level iteration of these processes envisions a system that accelerates individual steps while also integrating them across the entire drug discovery and clinical trial pipeline. This system would leverage patient-derived multi-modal data to identify disease targets, design candidate molecules, predict interaction profiles, and stratify trial participants according to genomic subtypes. These currently disconnected operations would become a streamlined, continuous workflow under an AGI-aligned AI architecture.

Establishing the Software Infrastructure for AI-Enhanced Diagnostics in Clinical Settings

The case for AI diagnostic tools in clinical environments is compelling, but the bigger challenge lies in creating systems that can be deployed securely and effectively—ensuring regulatory approval, mitigating bias, and providing clinical validation that stands up under scrutiny when patient outcomes depend on the results.

The necessity for validated, bias-controlled training data is paramount. AI diagnostic systems learn from historical datasets. If these datasets represent historical biases in healthcare—such as underrepresentation of specific populations in imaging studies, inconsistent documentation for non-native speakers, or less thorough genomic sequencing for economically disadvantaged groups—the AI models can perpetuate these biases within their output.

A systematic review from 2025 of agentic AI in cancer detection identified factual inaccuracies in 15% to 41% of model outputs across different systems. This variability is clinically significant. Therefore, addressing bias is a critical preemptive measure that must account for how training datasets are constructed, how model performances are evaluated across various demographic groups, and how ongoing monitoring can capture performance drift.

Healthcare organizations adopting AI diagnostic tools should pose a vital inquiry to their development partners: How was the model's performance validated across diverse patient populations, imaging technologies, and clinical settings? If responses lack specificity, the potential for bias remains a concern.

The FDA's 2025 draft guidance covering AI-enabled Software as a Medical Device (SaMD) has reshaped the regulatory landscape for AI diagnostic tools, introducing lifecycle management requirements that influence the regulatory burden. This guidance delineates submission protocols, oversight measures, and change management for models that evolve post-deployment.

Practically speaking, any AI diagnostic system that informs or significantly impacts clinical decisions will likely need to undergo FDA SaMD classification. This requirement will affect architectural choices—whether to use static or adaptive models—along with documentation needs relating to training data sourcing, validation methodologies, and performance metrics across populations.

For organizations considering whether to procure an existing AI diagnostic product or develop a bespoke solution, understanding the regulatory framework is crucial. Off-the-shelf solutions that already have FDA clearance may offer quicker deployment but at the expense of customization. Conversely, custom-built systems allow for clinical relevancy and adaptability, but necessitate a development partner experienced in navigating the SaMD pathway from the ground up, rather than retrofitting compliance criteria at the end.

Insights from Organizations Successfully Aligning with FDA 2025 Standards

Organizations that effectively manage this process tend to view regulatory compliance as pivotal from the outset, as opposed to a mere checklist for launch.

This approach signifies that data governance must be established before model training begins, documenting the sources of data utilized and the strategies employed to assess demographic representation. Validation methodologies need to be determined prior to deployment, with established benchmarks for performance across distinct patient groups. The evidence supporting safety claims for the tools should be developed concurrently with design rather than compiled hastily when a regulatory review is requested.

A proficient healthcare software development team brings this compliance mindset to the table, as they have previously navigated the FDA SaMD regulatory process. They understand where reviewers will scrutinize documentation closely, the types of gaps that can lead to delays, and recognize that constructing AI diagnostic tools that successfully undergo regulatory review and can be used effectively in clinical practice requires meticulous planning from inception rather than a last-minute effort before launch.

The 93% concordance rate connected to AI in cancer detection is remarkable. Similarly, the 98.88% accuracy in X-ray classifications speaks volumes. Yet, these statistics merely scratch the surface, representing the foundation of a diagnostic transformation. As we make strides toward AGI-level reasoning, future systems will integrate all relevant data sources a clinician would seek and process them more swiftly than any human team can accomplish.

Healthcare organizations that will reap the most benefits from this ongoing transformation are the ones currently building the necessary infrastructure, governance protocols, and compliant software frameworks to support it before the technological potential is fully realized.

The Impact of 93% Accuracy in Cancer Detection on Future Developments in Healthcare Diagnostics with Artificial General Intelligence

A sunlit hospital diagnostic suite with floor-to-ceiling windows letting in warm morning light. A focused female physician stands beside a sleek touchscreen medical display labeled “AGI Diagnostic AI,” showing a detailed 3D cross-section of a tumor with a bold “93% Accuracy” badge. Nearby, a calm patient silhouette rests on a bed, charts and diagnostic graphs softly illuminated on wall-mounted monitors. Render in a realistic digital-painterly style, emphasizing hope and precision.

Artificial General Intelligence in Healthcare Diagnostics: What 93% Accuracy in Cancer Detection Tells Us About What's Coming Next - Nasscom

- IBM Watson for Oncology achieved a 93% concordance rate with expert recommendations in breast cancer treatment.
- AI systems demonstrate high accuracy in various cancer detection tasks, outperforming human specialists consistently.
- Multimodal AI integration enhances personalized medicine by synthesizing diverse patient data rapidly for treatment planning.

A number worth pausing on: 93%.

That's the concordance rate IBM Watson for Oncology achieved with expert tumor board recommendations in a peer-reviewed study of breast cancer treatment decisions. Separately, Harvard Medical School's CHIEF model — a pathology AI trained on more than 60,000 whole-slide images — hit nearly 94% accuracy in cancer detection across 11 cancer types and 15 independent databases. Ovarian cancer staging by AI: 97% accuracy, compared to 88% by radiologists.

These aren't prototype results from a lab. They're published clinical findings. And they raise a question that every healthcare leader thinking about the future of diagnostics needs to sit with: if today's narrow AI — systems built for specific tasks, not general reasoning — can already perform at near-expert level in individual diagnostic tasks, what happens when that capability extends across every diagnostic domain simultaneously?

That's the promise of Artificial General Intelligence in healthcare. It isn't here yet. But the diagnostic accuracy data we're seeing right now is the clearest signal yet of where this technology is heading — and how fast.

How AI Diagnostic Tools Are Already Matching and Outperforming Human Specialists in Narrow Tasks

The diagnostic wins are stacking up fast, and they're coming from independent research groups, not vendor marketing.

Medical imaging and radiology: what peer-reviewed evidence shows

Medical imaging is where AI diagnostic performance has been most rigorously tested — and the results are consistently striking.

In prostate cancer detection, a systematic review published in 2025 covering 23 studies and 23,270 patients found that AI systems achieved a median AUC-ROC of 0.88 — matching or exceeding radiologist accuracy — while reducing reporting time by up to 56% compared to conventional reads. In chest imaging, deep learning models trained on large datasets can detect pulmonary nodules at early, treatable stages that human readers routinely miss on low-dose CT scans.

In X-ray classification, a JMIR study published in 2026 documented AI achieving up to 98.88% accuracy in multiclass disease classification from medical images — a figure that, in most clinical environments, would represent a meaningful improvement over standard radiological review for high-volume screening tasks.

The pattern is consistent across modalities. AI doesn't just match specialists in specific imaging tasks — in controlled conditions, it frequently outperforms them on speed, consistency, and detection of subtle findings at scale.

Cancer detection and genomic analysis: a pathway no single clinician can replicate

Where individual diagnostic accuracy is impressive, the more important capability is what happens when AI integrates multiple data streams simultaneously.

A 2025–2026 systematic review in ScienceDirect covering 123 papers on agentic AI in cancer detection found GPT-4 class models classifying skin lesions as accurately as dermatologists and staging ovarian cancer better than radiologists. But the more significant finding is what multimodal integration adds on top of image analysis.

AI systems can now combine imaging findings with genomic sequences, tumor biomarker panels, prior treatment history from EHRs, and patient demographics — in seconds. A peer-reviewed paper published in Frontiers in Artificial Intelligence in January 2026 described this directly: AI-enabled multimodal integration consolidating genomic, transcriptomic, proteomic, imaging, and EHR data into a unified analytical framework that enhances early disease detection and accelerates biomarker discovery in oncology, neurology, and cardiovascular medicine simultaneously.

No single clinician, no matter how skilled, can process those data streams in parallel during a clinical encounter. That's not a criticism — it's a structural reality. The volume and variety of data involved in a comprehensive diagnostic workup now exceeds what unassisted human cognition can reliably synthesize.

The leap from narrow AI to AGI-level reasoning in diagnostics

This is where the distinction between today's AI and Artificial General Intelligence in healthcare becomes meaningful.

Current AI diagnostic tools are narrow. Each system does one thing well — reads a specific type of scan, stages a specific cancer type, flags a specific genetic mutation. They don't connect findings across domains. A pulmonary nodule AI doesn't know what the patient's genomic sequencing showed, or what their EHR says about prior environmental exposures.

AGI-level diagnostic reasoning would. A true AGI system could synthesize findings across imaging, genomics, labs, social determinants of health, and clinical history — reasoning across all of them simultaneously, the way a multidisciplinary tumor board does, but faster, more completely, and at scale.

The diagnosis and detection segment is currently the fastest-growing AI subsegment in healthcare, at 39.8% CAGR from 2025 to 2030 according to MarketsandMarkets. That growth rate reflects real investment in exactly this direction: systems that move from isolated accuracy in narrow tasks toward integrated reasoning across diagnostic domains.

Personalized Medicine and AGI: From Population-Level Treatment to One-Patient Precision

The diagnostic accuracy story matters. But the bigger implication of where this technology is heading is what it means for treatment — specifically, for the shift from population-level protocols to truly individualized medicine.

How AI integrates genomics, biomarkers, EHR history, and social determinants today

Precision medicine has been a stated goal in healthcare for two decades. For most of that time, the tools available couldn't fully deliver on it. Genomic sequencing generated data that outpaced clinical interpretation. EHR histories were siloed. Social determinants of health were documented but rarely integrated into treatment planning in any systematic way.

Multimodal AI is changing that equation. Published in Frontiers in Artificial Intelligence (January 2026), peer-reviewed research confirmed that AI systems integrating genomic, transcriptomic, proteomic, imaging, and EHR data are already enabling personalized treatment recommendations that improve early disease detection and accelerate clinically actionable biomarker discovery — particularly in oncology.

This means a treatment recommendation that accounts for what a patient's tumor looks like, what their genome says about likely drug response, what their prior lab values show about organ function, and what their social history suggests about medication adherence — all at once, before the clinical team makes a decision.

The AI isn't replacing the oncologist. It's giving the oncologist a data synthesis that would otherwise require a team and a week.

The role of quantum computing in accelerating AGI-level medical diagnosis

One of the harder limits on current AI in diagnostics is processing speed at scale. Genomic datasets are enormous. Drug interaction modeling requires simulating molecular behavior across thousands of variables. Multimodal training requires handling data volumes that classical computing handles inefficiently.

Quantum computing addresses this directly. Research published in PMC in 2026 confirmed that quantum-assisted drug discovery is expected to revolutionize molecular simulations through algorithms like the Variational Quantum Eigensolver — improving molecular interaction predictions, streamlining structure-based drug design, and accelerating AI model training for clinical decision-making. Quantum machine learning techniques are already being applied to genomic analysis, improving biomarker identification and patient stratification in ways that classical ML cannot match at the same speed.

For AGI-level diagnostic systems — which will need to process patient-level multiomics data, imaging, and real-time monitoring simultaneously — quantum-enhanced processing isn't a future capability. It's the infrastructure that will make that scale practical.

Drug discovery at AGI scale: years instead of decades

Drug discovery is where the AGI trajectory has the most dramatic near-term implications.

Developing a new drug has historically taken over a decade and cost billions. AI is already cutting into that timeline by simulating trial outcomes, predicting compound toxicity, identifying drug-target interactions, and flagging approved molecules for repurposing — all tasks that previously required years of wet lab work.

The AGI-level version of this is a system that doesn't just accelerate individual steps but integrates across the entire discovery-to-trial pipeline. Mining multi-modal patient-derived data to identify disease targets. Designing candidate molecules. Predicting interaction profiles. Stratifying patients for trials by genomic subtype. These functions, currently handled by separate specialized teams working sequentially, become a continuous, connected workflow under AGI-adjacent AI architecture.

Building the Software Infrastructure That Powers AI-Enhanced Diagnostics in Clinical Environments

The clinical case for AI diagnostics is clear. The harder work is building systems that can be deployed safely in real healthcare environments — with regulatory approval, bias controls, and clinical validation that holds up when patients' lives depend on the output.

Why AI diagnostic tools require validated, bias-audited training data

AI diagnostic systems learn from historical data. When that data reflects historical disparities in healthcare — underrepresentation of certain populations in imaging datasets, inconsistent documentation for non-English-speaking patients, less comprehensive genomic sequencing for lower-income groups — the model learns those biases and encodes them into its outputs.

A 2025 systematic review of agentic AI in cancer detection found factual errors in 15% to 41% of model outputs across tested systems. That range is wide enough to matter clinically. Bias mitigation isn't a post-deployment concern — it must be addressed in how training datasets are assembled, how model performance is tested across demographic subgroups, and how ongoing monitoring catches drift.

Healthcare organizations deploying AI diagnostic tools need to ask their development partners a specific question: How was this model's performance validated across different patient populations, imaging equipment types, and clinical environments? If the answer is vague, the risk of bias is real.

FDA SaMD classification: what the regulatory pathway means for your build vs. buy decision

The FDA's January 2025 draft guidance on AI-enabled Software as a Medical Device (SaMD) introduced lifecycle management requirements that meaningfully changed the regulatory burden for AI diagnostic tools. The guidance covers marketing submission recommendations, lifecycle oversight through continuous monitoring, and change-control mechanisms for models that update over time.

In practical terms: any AI diagnostic tool that makes or significantly influences a clinical decision is likely to require FDA SaMD classification. That classification affects architecture decisions (static vs. adaptive models), documentation requirements (training data provenance, validation methodology, performance benchmarks by population), and post-market monitoring obligations.

For healthcare organizations deciding whether to buy an off-the-shelf AI diagnostic product or build a custom solution, this regulatory pathway is a central variable. Off-the-shelf products with existing FDA clearance offer faster deployment but less flexibility. Custom-built systems offer clinical fit and configurability — but require a development partner who understands the SaMD pathway from the first architectural decision, not as a regulatory retrofit at the end of the project.

How healthcare software development teams build to FDA 2025 standards from day one

The organizations that navigate this well share a common characteristic: they treat regulatory compliance as a design input, not a launch checklist.

That means data governance is mapped before model training begins — documenting what data was used, where it came from, and how demographic representation was assessed. It means validation methodology is defined before deployment, with acceptance criteria for performance across patient subgroups. It means the clinical evidence that supports the tool's safety claims is built during development, not assembled afterward when a regulatory reviewer asks for it.

A specialized healthcare software development team brings this orientation because it has worked through the FDA SaMD process before. It knows where reviewers focus on their scrutiny. It knows what documentation gaps create delays. And it knows that building AI diagnostic tools that survive a regulatory review and hold up in real clinical use requires the same rigor at the beginning of a project that most teams only apply at the end.

The 93% concordance figure from AI cancer detection is remarkable. The 98.88% X-ray classification accuracy is remarkable. But what these numbers really represent is the floor — the starting point of a diagnostic transformation that, as it moves toward AGI-level reasoning, will integrate every data source a clinician would want and synthesize them faster than any human team can.

The healthcare organizations that will benefit most from that transformation are the ones building the data infrastructure, governance frameworks, and compliant software architecture to receive it — right now, before the technology arrives at its full potential.

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