{"id":29752,"date":"2026-07-20T04:00:00","date_gmt":"2026-07-20T02:00:00","guid":{"rendered":"https:\/\/www.thebrokernews.ch\/?p=29752"},"modified":"2026-07-08T16:33:30","modified_gmt":"2026-07-08T14:33:30","slug":"humanity-is-an-average-no-single-person","status":"publish","type":"post","link":"https:\/\/www.thebrokernews.ch\/en\/humanity-is-an-average-no-single-person\/","title":{"rendered":"Humanity is an average: No single person fits that description"},"content":{"rendered":"
Humanity versus the Human: Artificial General Intelligence (AGI) versus what is known as Artificial Individual Intelligence (AII).<\/span><\/div>\n\n

The mission statements of leading AI labs speak of billions, but almost never of an individual. An analysis of the founding documents of OpenAI, Anthropic, and Google DeepMind shows just how consistently the individual has disappeared from the language of artificial intelligence\u2014and why this is more than just a linguistic footnote. <\/strong><\/p>\n\n

Every major AI lab describes its purpose in almost identical terms: the goal is to develop artificial intelligence that benefits humanity. A noble goal, no question. It appears almost identically in the founding documents of the entire industry. But \u201chumanity\u201d is an aggregate, an average value at the level of the entire species. The term says nothing about the individual reading this text. <\/p>\n\n

What the Founding Documents Actually Say<\/h6>\n\n

A look at the primary sources confirms this pattern. In its charter, OpenAI<\/a> commits to ensuring that artificial general intelligence benefits \u201call of humanity.\u201d Anthropic<\/a> defines its mission as the responsible development of advanced AI \u201cfor the long-term benefit of humanity.\u201d Google DeepMind<\/a>, for its part, aims to research intelligence in order to advance science and \u201cbenefit humanity.\u201d<\/p>\n\n

The term \u201cindividual\u201d or \u201csingle person\u201d does not appear in any of the three key texts. Even where \u201cindividual\u201d appears on these companies\u2019 broader websites, it does so in operational subordinate clauses\u2014for example, as an illustration of an external effect such as \u201cindividuals who are educated.\u201d However, it never appears in the mission statement itself. <\/p>\n\n

The numbers speak for themselves: None of the three laboratories mentions \u201cthe individual\u201d in its core mission. All three mission statements focus on \u201chumanity,\u201d \u201csociety,\u201d or \u201cthe world.\u201d And some eight billion people are summed up in a single word: humanity. <\/p>\n\n

Why this is neither a coincidence nor a scandal<\/h6>\n\n

This observation is not necessarily a mistake. Collective formulations of benefit are quite common in mission statements, comparable to the preamble of a constitution, which speaks of \u201cus and our descendants\u201d rather than individual citizens. The dominant mindset in AI development focuses on aggregate benefits: improvements for the species, for the economy, and for society as a whole. <\/p>\n\n

But that is precisely where the real gap lies. Nothing in these founding documents requires an AI system to understand, model, or specifically cater to the psychology, motivation, or decision-making style of an individual. By their very definition, large AI systems are optimized to improve the average of everyone. But who is designed to understand the individual? <\/p>\n\n

From Artificial General Intelligence to Artificial Individual Intelligence<\/h6>\n\n

This question brings into play a conceptual distinction that is becoming increasingly relevant: Artificial General Intelligence (AGI) versus what can be described as Artificial Individual Intelligence (AII).<\/p>\n\n

AGI, as pursued by OpenAI, Anthropic, and Google DeepMind, arises from the accumulation of billions of data points and the average of everything humanity has ever created. It is thus, by definition, a reflection of the collective. It is extraordinarily powerful, but has no knowledge of the individual sitting in front of it. <\/p>\n\n

The alternative approach reverses the starting point. Instead of assuming an average for humanity and hoping that this will also apply to a specific person, an individual-centered approach begins with an individual\u2019s own decision-making drivers, motivators, and personality traits, and builds from there. <\/p>\n\n

Best Fit: People as the Starting Point, Not as an Average Value<\/h6>\n\n

This is precisely where Best Fit<\/a> positions itself with its trademarked category AII\u2122, Artificial Individual Intelligence. The company sees itself as the counterpart to the traditional AGI approach: whereas AGI is defined by scale\u2014by billions of averaged data points\u2014AII\u2122 is defined by precision\u2014by the decoded characteristics of a single individual. <\/p>\n\n

The company\u2019s approach involves a gamified sequence of behavior-based questions that takes about 90 seconds to complete, thereby replacing long, tedious questionnaires. It is based on Nobel Prize-winning behavioral science research, not generic personality test heuristics. The result is a decoded profile of a person\u2019s key decision drivers, motivators, and personality traits\u2014essentially, the psychological operating system behind their actual actions, engagement, and decisions. Because the process is designed to be fast and engaging, it is suitable for large-scale use\u2014for example, in recruiting, coaching, matching, and the personalization of financial and healthcare services\u2014without sacrificing depth. <\/p>\n\n

The question remains<\/h6>\n\n

An analysis of the founding documents shows that none of the leading AI labs includes the individual human being in its mission statement. Approaches like Best Fit\u2019s aim to close precisely this gap\u2014not by making AI bigger, but by making it more personal: by trying to understand the individual human being whom AGI, by its very nature, has never taken into account. <\/p>\n\n

It remains to be seen whether this will develop into a viable alternative to the aggregated AI logic. The question itself, however\u2014who understands the individual when large systems are optimized for the average\u2014will continue to be a key issue for the industry in the coming years. <\/p>\n\n

Binci Heeb<\/p>\n\n

Read also: Will AGI lead us in the future?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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