From Chatbots To Cashbots: How Pika's AI Selves Can Help Users Earn While They Sleep

The large language models (LLMs) war is intensifying.

Since the arrival of OpenAI's ChatGPT that ignited the battle towards AI supremacy, a rush among tech companies to build ever-more-capable conversational AI systems. What started as a novelty quickly reshaped how people accessed information, wrote code, and handled routine tasks, prompting competitors to flood the market with their own models and interfaces.

Amid this frenzy, most attention stayed on text-based chatbots and image generators, and here, Pika Labs took a different path.

Originally focused on turning text and images into short videos, the company quietly expanded its scope to something more personal: AI Selves, persistent digital extensions of individual users that combine appearance, voice, memory, and the ability to act across platforms.

And now, Pika Kabs announced a new layer to this system: users who create an AI Self can now earn tokens, redeemable for cash, whenever someone else chats with the agent, or makes use of its specialized skills. or ask for their photos.

The announcement arrived with a short animated video illustrating the shift: from agents that consume tokens to ones that generate them.

It showed stylized AI characters fielding requests, sharing files, and delivering results, with an earnings dashboard displaying accumulated credits ready for cash-out. The feature is currently limited to interactions inside the Pika platform, and the company invited feedback through its Discord to refine how the system works in practice.

This move reframes the economics of personal AI.

Until now, most consumer-facing agents have been cost centers. Tools that require ongoing payments for compute, storage, or usage credits. By tying earnings directly to engagement, Pika turns the agent into a potential asset that can generate income without the owner needing to be online or actively involved.

A user might build an AI Self trained on their expertise in social media strategy, video editing, or fashion advice; others could then pay to consult it, with a portion of those fees flowing back as tokens.

The platform handles the transactions internally, converting earned tokens to cash once a threshold is met.

The implications extend beyond individual creators.

If AI Selves become widespread, they could scale personal presence in ways that were previously impossible. One person could maintain simultaneous conversations across messaging apps, professional networks, and content platforms while the AI handles routine inquiries, generates tailored responses, or executes simple tasks using its built-in skills.

For freelancers or small businesses, this blurs the line between human labor and automated output, potentially creating passive revenue streams that supplement or even replace certain services.

At the same time, it raises practical questions about transparency.

When using this kind of feature, who is speaking, the human or the AI? And about how platforms will verify the quality or authenticity of these digital agents.

The broader disruption lies in how value flows through digital interactions. A marketplace of specialized AI Selves could emerge, where users discover and pay for agents built by others rather than building everything themselves. This might accelerate the creator economy by lowering the barrier to offering services at scale, but it could also pressure traditional jobs that involve advice, consultation, or repetitive content creation.

Companies might experiment with licensing popular AI personas or integrating them into customer support. Regulators and users alike will need to consider issues of data ownership, consent for training on personal likeness, and what happens when these agents evolve beyond their original prompts.

But for now, Pika's update is a single step in one platform, yet it points to a future where AI no longer merely assists but actively participates in the economy on behalf of its creators.