Bittensor For The Long Run

Bittensor is the future of synthetic intelligence and we're all in.

For brief context: Bittensor is an open-source network and protocol founded to deliver on a simple mission: to use programmable incentives to accelerate development for open-source intelligence markets.

We are a team of EU-funded researchers building Subnet 17 (SN 17) — an intelligence market on Bittensor dedicated towards generating 3D assets.

Before continuing, this piece assumes a basic underlying understanding of Bittensor. An in-depth look at the underlying principles and development of the network is explained here.

404—GEN

In SN 17 we consider value creation along two (related) vectors:

1. AI model improvements (research value); and

2. Commercial traction (economic value).

"If I have seen further it is by standing on the shoulders of Giants." - Isaac Newton, Letter to Robert Hooke (1675)

We — and Bittensor as a whole — are strong believers in building upon Open Source (OS) software. To date, OS software has suffered from a lack of proper incentive structures.

A Replication Study on Measuring the Growth of Open Source M Dorner, M Capraro, A Barcomb, K Wnuk (2022) arXiv: 2008.07753

By creating an incentive structure (via Bittensor’s $TAO) together with proper validation mechanisms, it is possible to now encourage competition to leverage (and drive improvements beyond) State Of The Art (SOTA) OS AI models.

Our team at SN 17 use Bittensor’s incentive mechanism to address the near-infinite demand for 3D content that exists within gaming, and which extends far beyond into entertainment (film and VFX), as well as consumer and retail applications. With recent consumer hardware and end-user device advances, we believe this demand will multiply exponentially as AR, VR and XR products and services mainstream over the next 24 months.

The end result is a bottleneck in which creatives who lack the necessary capital are unable to fulfill a global demand for AR, VR, XR, 3D experiences, social gaming, and more.

SN 17 aims to relieve that bottleneck by initially targeting three specific use cases:

1. Synthetic dataset generation;

2. Creation of 3D asset marketplaces;

3. Organic consumer applications.

Synthetic dataset generation is the creation of 3D models used as inputs for AI training, and a logical first step to move from research to economic value. SN 17 is currently capable of producing 100K+ verified 3D models every eight hours, and this power can be harnessed towards generation of real-time industry trends and content creator needs.

404—GEN will be the world's largest 3D dataset in 60 days

Effectively, SN 17 has the ability to dwarf web2 market leaders like Unity, Kitbash and Sketchfab in an intelligent way by curating synthetic generation requests according to real-time demand vectors (e.g. latest industry search trends) and storing models onchain.

Examples of 404—GEN 3D outputs

Ultimately, it is the broader consumer applications that are most exciting to our subnet – steady state, this system has the ability to accelerate the non-technical creation of 3D assets across all skill levels, matching the growing demand for immersive objects and experiences around the internet.

Gaming is a market that already craves this type of innovation. User Generated Content (UGC), is heavily relied upon to inform persistent user retention and engagement. On SN 17, we aim to power 3D UGC on any platform by allowing for direct API calls to SN 17.

To further these goals we have created a web front end, discord bot and Blender plugin that provide sample generative capabilities and open-source templates for the community to build upon for their own purposes.

Ways to interact with 404—GEN

We are also partnering with innovative gaming, XR, and consumer applications to support the integration of SN 17 into their virtual world creation.

Ultimately, with SN 17, it will be possible for anyone to text prompt entire virtual worlds into existence.

The improvement of foundational models and growth of open source repositories creates a uniquely diverse and fractured landscape for 3D Gen AI. This coupled with the fact that 3D Gen AI research is still nascent (compared to 2D) means that a ‘market winning’ technology has yet to be determined. Approaches such as 3D Diffusion, Neural Radiance Fields (NeRFs), and Gaussian Splattering (Splatts) all compete with different underlying neural network architectures.

Research into all three of these approaches is rapidly developing and benchmarking solutions change nearly every week. Currently, closed source platforms have an advantage. But Bittensor offers exactly the type of open competitive landscape that we believe can ultimately overcome that gap.

Specifically, we believe a diversity of models are required to cover the problem constraints of generating 3D assets in unique and novel ways. Simply put, SN 17 incentivizes:

1. Choosing the right model for the right job; and

2. Rewarding the best miners building atop SOTA 3D Gen AI.

We've already witnessed the latter point applied to SN 17 – today, top miners on SN 17 are those who seek out new models and modify them. The principle was explained well by one SN 17 miner who was asked how they were constantly earning high rewards:

SN 17 miner explanation of incentive-based performance. In this sense, SN 17 is a dynamic and real-time representation of SOTA 3D Gen AI.

A visual example of model performance is evident by evaluation of how the results of SN 17 have changed in the 2 months that we have been live on mainnet. Below are results from May and July of 2024. Showing improvements to both the underlying models as well as the validation mechanism which scores results.

3D generated models on SN 17 May 2024 (Left) and July 2024 (Right)

This is just the beginning.

We are at the cusp of exponential growth within the broader 3D Gen AI landscape. One such indicator for future quality of 3D is to consider the historic trend we have seen in the 2D space.

Shown below is the evolution in quality between Midjourney v1 and Midjourney v5.1 (over a 15-month period).

The cryptonomic commons.

MidJourney 2D Improvements from Jan 2022 (v.1) to May 2023 (v.5.1)

While furthering 3D AI research is key towards future value creation, there is also a broader economic rationale behind Bittensor. Creating incentives to improve AI models creates development trajectories towards monetization. The underlying technology of SN 17 has already reached levels of quality suitable for paying enterprises today.

This is because the creation of virtual environments rely on the highly specialized and time consuming process of manually modeling, sculpting or procedurally scripting 3D digital assets. The costs in both financial and human capital are highly restrictive. Further, computing power is growing at an exponential rate and with it, consumers’ expectations regarding the size, density and visual fidelity of virtual worlds.

Meet Bittensor

Perhaps such levels of innovation could be facilitated by a multibillion dollar entity such as an OpenAI or Google, but then ultimately every user created worlds would be subject to risks inherent in partnership with a central authority and creatively limited by any components deemed proprietary, closed source or competitive.

As a team building a subnet on Bittensor, what is truly unique about SN 17 is not only the value being created, but the way in which it is created – via decentralized systems combined with collective intelligence and ownership.

We’re all in.