Who Cares

This is part of crypto’s current image problem. The far-reaching benefits of blockchain technology are exciting when you dig into the details, but the average person struggles to see how those benefits apply to their lives now when it matters to them.

Crypto has an image problem, but one of the solutions is fairly simple: Meet people where they are. Show them how the blockchain can make their lives easier now and how it is relevant to their actual experiences. 

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A shift from Asset Class to Experience Layer

Most consumers only understand web3 through a simple number, and that is generally the price of Bitcoin. Web3 has unleashed a new asset class on the world, which has driven all of its hype to date, and is one of its biggest barriers to mass adoption. 

Most consumers are afraid of crypto. It is hard, complex, and scary. Even when new applications make it easy to ‘on-ramp’ into crypto there is still the radical volatility of the asset class. Beyond these issues is the basic fact that most consumers don’t care about asset classes. 

The focus of web3 as an asset layer is also problematic for brands. Most brands are now holding off on creating web3 tokens (NFTs) due to the unknown regulatory environment. They simply don’t want to be creating assets as that will likely classify them as a financial institution, taking them away from their core business and opening them up to a massive new world of regulation. The tiny market that would even care about these assets doesn’t even come close to justifying the risk of additional regulation. 

A shift from On-chain to “OmniWeb”

Web3, past being a speculative financial asset class, also has another big problem: the blockchain. The blockchain is a powerful database able to solve “double spend,” a potential risk to cryptocurrency in which a user might be able to spend the same digital currency twice. Through consensus mechanisms and the recording of transactions on a ledger, blockchain solves this flaw, enabling blockchain-based systems to be a transaction layer of assets and allowing for crypto to even exist. It’s a big deal! Despite this powerful feature, putting things on-chain comes with many issues that stifle the adoption of web3. 

Putting things on-chain makes transactions public. This is good for solving transparency in financial situations, but it’s bad for a lot of other general use cases. For example, brands don’t want their entire customer database visible to their competition. Additionally, blockchains are not as fast or scaleable as other databases. Blockchains are designed to be a transaction layer, and they are great at that. They are not great at storing a lot of information. So a slow, public, and expensive database has very limited use cases, and most brands and consumers find it too scary to engage. 

Web3 has to embrace a hybrid world where both on-chain and off-chain tokens work together. I call this “omniweb architecture,” where on-chain tokens can be extended via off-chain code and off-chain tokens can be leveraged by on-chain and off-chain applications.

Off-chain tokens, better known as “attestations” are exactly the same as the on-chain token – verifiable, decentralized, trusted, and programmable – however they come with the added benefit of no wallet being needed. They’re also private and are not seen by regulators as an asset. They can unlock the value of web3 with none of the hassles. 

For example, Taylor Swift could issue an off-chain token to all of her fans who follow her on Twitter, Spotify, and have been to a concert in the last year. Smart Token Labs is an Australian web3 startup that I currently advise and which is currently working on this technology. Their CMO Brent Annells believes that fans want new ways to connect. “Fans want to be rewarded for being fans but in a way they value. They don’t want to be paid or have a digital asset they can sell. They want access and to be recognized by the artists as fans,” he said.

So where do we go from here?

Zaheer Goodman-Bhyat

Co-founder ON_Discourse
and Purpose-driven Founder
& Award-winning Storyteller @HighMagic.io

The entire current conversation about web3 is focused on the financial aspects of the technology, and mostly that is simply the token prices of Bitcoin. This limited view is too scary to consumers and brands. When we let web3 fade into the background and leverage it to create better experiences both of those fears go away, and a greater value can emerge. To do this we also have to reduce the complexity of the experience, which means embracing off-chain tokens to expand on these use cases and further reduce consumer friction.

By shifting the focus from web3 as an asset layer to an experience layer, and embracing a wider understanding of web3 technology beyond just the blockchain, we can deliver to consumers truly what they want: a better digital experience.

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In the late 20th century, the technology world witnessed a seismic shift as open-source Linux rose to prominence, challenging the dominance of proprietary operating systems from the era’s tech giants. Today, we are on the cusp of a similar revolution in the realm of AI, as open-source language models gain ground on their closed-source counterparts, such as those developed by Google and OpenAI.

In the 1990s, the UNIX ecosystem was dominated by proprietary solutions from major players like Sun Microsystems, IBM, and HP. These companies had developed sophisticated, high-performance systems tailored to the needs of their customers, and they maintained tight control over the source code and licensing. However, Linux, an open-source operating system created by Linus Torvalds, started gaining traction, ultimately disrupting the market.

The Linux revolution was propelled by three key factors: rapid community-driven innovation, cost-effectiveness, and adaptability. By embracing a decentralized development model built off the x86 personal computer, Linux empowered developers worldwide to contribute to its growth. This allowed it to evolve more quickly than its rivals and adapt to a diverse range of applications. Furthermore, Linux’s open-source nature made it significantly more cost-effective than proprietary alternatives, which relied on expensive licensing fees.

Fast-forward to the present day, and we are witnessing a similar upheaval in the AI landscape. The past two months have seen open-source AI projects such as EleutherAI GPT, Stanford Alpaca, Berkeley Koala, and Vicuna GPT, make rapid strides, closing the gap with closed-source solutions from giants like Google and OpenAI. Open-source AI models have become more customizable, more private, and pound-for-pound more capable than their proprietary counterparts. Their adoption has been fueled by the advent of powerful foundation models like Meta’s LLaMA, which was leaked to the public and triggered a wave of innovation. 

The Linux saga offers important lessons for the AI community, as the similarities between the rise of Linux and the current open-source AI renaissance are striking. Just as Linux thrived on rapid community-driven innovation built off the backs of the x86 PC, open-source AI benefits from a global pool of developers and researchers who build upon each other’s work in a collaborative manner off the backs of gaming GPUs. This results in a breadth-first exploration of the solution space that far outpaces the capabilities of closed-source organizations.

Another parallel is the cost-effectiveness of open-source AI. Techniques such as low-rank adaptation (LoRA) have made it possible to fine-tune models at a fraction of the cost and time previously required. This has lowered the barrier to entry for AI experimentation, allowing individuals with powerful laptops to participate, driving further innovation.

Moreover, open-source AI models are highly adaptable. The same factors that make them cost-effective also make them easy to iterate upon and customize for specific use cases. This flexibility enables open-source AI to cater to niche markets and stay abreast of the latest developments in the field, much like Linux did with diverse applications.

The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives. The experience of proprietary UNIX-based systems in the face of Linux’s rise serves as a stark reminder of the perils of ignoring this trend. In fact, with image generation bots, OpenAI’s Dall-E and Google’s various closed models are barely a point of discussion as the world flocked to open Stable Diffusion models.

To avoid being left behind, closed-source AI organizations must adapt their strategies. Embracing the open-source ecosystem, collaborating with the community, and facilitating third-party integrations are crucial steps. By doing so, these organizations can position themselves as leaders in the AI space, shaping the narrative on cutting-edge ideas and technologies. Companies like Replit, MosaicML, Together.xyz, and Cerebras are doing just that, releasing open-source models, but offering services, finetuning, or operations as a service instead.

“ The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives.”

The flip side of the argument is that this is only possible for a certain model size. There are many emergent behaviors that have only been witnessed on the largest models. While open-source AIs that are an order of magnitude smaller than GPT-3 have already surpassed GPT-3’s quality, this does not necessarily apply to models of the scale of GPT-4 and beyond. With continued scaling in sequence length, parameter count, and training data set sizes, it is possible the gap between open-source and closed-source widens again.

Furthermore, while models are free to use, services that are built on top will still require significant investments. Google, Microsoft, and Meta are able to build these closed-source services for use in people’s everyday lives due to the moat of their platforms. Lastly, the cost of inference is a significant barrier given most consumer devices do not have the horsepower required for models larger than 7 billion parameters (GPT-3 is 175 billion parameters, GPT-4 is over 1 trillion), and it is possible that only the largest organizations can afford to scale their model out to billions of users.

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Molly Crabapple

Artist and writer in New York City.  Her work was nominated for three Emmys and is in the permanent collection of the Museum of Modern Art.

Like most illustrators, I’ve been horrified by the rise of AI image generators, like Dall-e, Midjourney, and Stable Diffusion. These systems are trained on billions of images obtained without the knowledge, compensation, or consent of their creators, and churn out passable-looking images in seconds, for pennies or for free. Since they are faster and cheaper than any human illustrator can be, these generators threaten to destroy the industry that I have devoted my life to. Worse, they are using our own stolen images against us. My work, like that of many other illustrators, is stored in the LAION-5B database, which billion-dollar corporation Stability AI uses to train their generator. Even more alarmingly, DALL-E, owned by multi-billion dollar Open AI, can create ersatz versions of my work if you type “drawn in the style of Molly Crabapple.”

One might argue that the rise of AI image generators is only a problem for illustrators like me. But what about you, editor, art director, or publisher? Why should you forgo something so convenient? Sure, generative AI threatens mass unemployment for millions of people, far beyond the illustration field, but appeals to ethics and solidarity won’t stop you from using it any more than it stopped you from using Amazon or Uber or AirBnB. You need stronger stuff.

Well, here are some reasons why you should avoid using AI art generators, even if the future of illustrators does not concern you in the slightest.

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