The Life Age: A new era of connectivity

Generative AI has had underwhelming business outcomes because it has been used as a search tool in an old paradigm. Instead, it should be used as the enabling tech of a new paradigm: the Life Age.

Abstract: The Life Age is a new era of connectivity

The Life Age is starting. Generative AI tools are the enabling technology of this age. They allow formerly mute processes like AI agents or organizations to come alive. These units can “auto communicate” to connect automatically. The Life Age can only scale if these units are harmonized. Two features are required for harmonization:

  1. The units must act based on their goals.

  2. The units must share a medium of communication.

We are building both of these features at Momentum. We have invented the field of Emergent AI for this purpose. Emergent AI harmonizes the actions of AI agents to allow a higher-level behavior to emerge.

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Background: The Information Age required curation

Some inventions are so powerful that they define human eras. The period before the invention of writing is called “prehistory.” The period following the invention of the steam engine is called the Industrial Age. Computers and the Internet began the Information Age. The ubiquity of these technologies is responsible for this name. The total number of people using the Internet increased from 2.6M in 1990 to 5.4B in 2024.

Most adults are connected to the Internet and most data is digitally transmitted. Navigating the large number of connections and data has required curation. Search engines like Google are gateways to the Internet, social networks like Facebook help manage relationships, and e-commerce platforms like Amazon curate our product discovery. The Information Age has created a feedback loop between connections and curation. More curation is needed as connections increase, and the ability to curate allows for more connections.

Figure 1: The Information Age required curation. The Life Age requires harmonization.

Problem: The Life Age requires harmonization

Generative AI models are “super communicators.” They are the enabling invention of the Life Age. They have not delivered on their initial promise so far. High-flying startups are being acqui-hired, researchers are questioning their economic impact, and big tech is tempering expectations. This shortcoming is caused by the use of Gen AI for curation (search) in an Information Age paradigm. Their true value is to enable “auto communication” between formerly lifeless and mute processes (AI agents and organizations). Humans feel more powerful when these units come to life to serve them.

Achieving the higher-level goals of AI agents and organizations requires harmonization. Biological processes offer helpful examples of harmonized behavior. The video below shows a white blood cell pursuing a bacterium. Neither cell has eyes or a brain. However, they each share three features:

  1. Goals: Survival for the bacterium and pursuit for the white blood cell.

  2. Action: Movement through the liquid.

  3. Communication: Awareness of each other through chemicals in the liquid.

We harmonize auto communicators (e.g. AI agents) by allowing them to pursue and communicate their goals. The Life Age creates a feedback loop between auto communicators and harmonization. More harmonization is needed as AI agents increase, and the ability to harmonize allows for more AI agents.

Momentum harmonizes similarly to the Prefrontal Cortex

We are addressing the need for harmonization in the Life Age. Coordinating the outputs of humans was already a challenge in the Information Age. Workers spend 57% of their time on coordination. Executives cite “super alignment” as their most desired superpower (we interviewed 1000 professionals). It is more difficult to harmonize (align) the outputs of groups of humans and AI agents in the Life Age. Our solution (Momentum) harmonizes around goals. Humans perform a critical role in our solution: they dream, set goals, and lead. Our solution harmonizes the goals of groups of humans and AI agents to accomplish their higher-level group objectives.

Using the brain as an analogy (Fig. 2), our solution functions like the prefrontal cortex (PFC). Broca’s area is for understanding speech. ChatGPT maps well to this area, as it relates the contexts of language inputs and outputs. The Visual Cortex is for understanding images. Dall-E maps well to this area, as it relates the contexts of language and images. Animals have similar areas in their brains for auditory and visual inputs. What differentiates us as humans is our larger PFCs, the centers of planning and action. We have larger PFCs for two reasons:

  1. We perform more complex planning and acting than animals.

  2. We are social animals. Planning and acting are more complex in social settings.

Our PFC uses the other parts of the brain as sensors and tools to model the “self” and the world. These abstractions help with planning and allow the PFC to predict the results of its actions. Our solution sits on top of Gen AI models. In addition to building a world model, Momentum provides the Gen AI models with the most accurate “context window” based on the current goals. An accurate context window is critical to obtaining the most relevant results from Gen AI models. Building Momentum has required the invention of a new field distinct from Gen AI. We have come to call this field Emergent AI. At Momentum, we are first applying this field to bringing organizations to life and making them antifragile.

Figure 2: The PFC sits on top of the other areas to build a world model for planning and action.

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From Tehran to the Subcontinent

A combination I like

A scene I like

Father: “You boys are about all I’ve done in life. Otherwise I’ve drawn zilch. You are all I have. You’re all I want to have. Sweet boy.”

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