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Sequoia Capital now defines AGI vaguely: \u201cthe ability to figure things out.\u201d Such slippery hand-waving urges a pivot from AGI hype to hybrid AI\u2019s feasible value.<\/p>\n
Future Publishing via Getty Images<\/p>\n
Recently on The Dr. Data Show, my co-host Luba Gluhova and I dug into the evolving discourse surrounding artificial general intelligence<\/a> \u2013 and its stubborn incoherence. A recent publication by the venture capital firm Sequoia Capital projected the arrival of AGI by 2026, defining the concept simply as \u201cthe ability to figure things out<\/a>.\u201d<\/p>\n From an engineering and practical standpoint, this definition effectively only reiterates traditional, highly subjective definitions of AI. It is basically another way to say \u201ccapable of reasoning,\u201d which has long been a common, if circular, attempt to define AI. <\/p>\n AGI was meant to differentiate from AI. It was originally supposed to signify the \u201cwhole enchilada\u201d \u2013 a virtual human<\/a> capable of doing anything that a person can. This would make such a system fully autonomous, capable of matching human performance across a wide range of tasks \u2013 effectively operating as a virtual employee.<\/p>\n However, as the profound technical challenges of achieving supreme autonomy<\/a> become apparent, definitions of AGI shift toward more subjective criteria, blurring the distinction between AGI and AI, which itself has always faced the existential problem of being undefinable<\/a>. This shift appears to be an exercise in waving hands in order to dodge criticism. <\/p>\n AI hype often emphasizes supreme autonomy \u2013 and most of the hype at least implies it. The notion of AGI is a natural extension of that aspect of the hype: If a system can do everything a person can, it needs no human in this loop. <\/p>\n While genAI possesses remarkable capabilities and offers substantial commercial value, it currently faces a critical reliability challenge. In automated enterprise workflows, such as customer service interactions or healthcare claims processing, a minor error rate \u2013 even as low as 5% or less \u2013 can render a fully autonomous system non-viable due to the operational risks of factual inaccuracies, ethical missteps or mishandled transactions. <\/p>\n Hybrid AI: A Practical Antidote To The Hype<\/p>\n So, how do we sober up and pursue feasible deployments that realize the potential value of these technologies? The answer is hybrid AI<\/a>. <\/p>\n Hybrid AI offers a practical alternative to pursuing the ever-elusive ideal AGI. GenAI hallucinates and exhibits other unacceptable behaviors that preclude its deployment \u2013 especially for its more ambitious intended uses, such as performing the role of customer service agent, analyst, educator or all-capable virtual assistant. Rather than supreme autonomy, a feasible route to leveraging genAI and pursuing its more ambitious uses is to hybridize it with predictive AI<\/a>. <\/p>\n Here\u2019s how hybrid AI works: Machine learning models serve as a vital reliability layer<\/a>. By assigning probability-based risk scores to the outputs of generative models, predictive AI can systematically identify the specific cases with the highest likelihood of failure or behavioral error. These high-risk cases are then routed to human operators for review. This judicious inclusion of a \u201chuman-in-the-loop\u201d mitigates the operational risks associated with large language models while successfully automating a significant portion of the workload. <\/p>\n An Industrywide Pivot To Hybrid AI<\/p>\n Hybrid AI is already moving from theory to practice. A diverse array of industry leaders \u2013 including Netflix, Amazon, JPMorgan and Microsoft \u2013 are actively deploying these hybrid systems (they\u2019ve lined up to speak on the topic at the conference I chair, HYBRID AI 2026<\/a>). This empowers businesses to navigate the limitations of genAI and deploy reliable, valuable semi-autonomy. <\/p>\n