From Worthless to 89000 What Has the Cryptocurrency Sector Experienced Along the Way

The trading volume has exceeded 85.4 billion US dollars.
Although we have not yet fully realized an “ownership network,” we have already seen the vibrant innovation that cryptocurrencies bring to the current internet.
For example, stablecoins such as Tether (USDT) and Circle (UDSC) are quietly changing the global payment network landscape.
According to Coinbase’s research report, stablecoins have become the fastest-growing payment method. Stripe recently completed the acquisition of the stablecoin infrastructure project Bridge, with a value of up to 1.1 billion US dollars, which is also the largest acquisition in the crypto world.
Blackbird, founded by one of the co-founders of Resy, focuses on changing the dining experience by allowing customers to pay for meals with cryptocurrencies, particularly using its own token, $FLY.
This platform aims to connect restaurants and consumers through a cryptocurrency-driven application, while also serving as a loyalty program.
Worldcoin, co-founded by Sam Altman, is a forward-looking movement that promotes universal basic income and relies on zero-knowledge proof technology.
Users scan their irises through a device called Orb, which generates a unique identifier called “IrisHash” to ensure that each participant is a unique human, thereby combating the growing presence of false identities and robot accounts in the digital space. Worldcoin has more than 10 million participants worldwide.
If we go back to the summer of 2017, we probably wouldn’t have imagined what the next 7 years would mean for the crypto industry – we wouldn’t have imagined so many applications growing on the blockchain or billions of dollars of assets stored in smart contracts.
How AI mirrors cryptocurrencies
Now I want to talk about the similarities and differences between cryptocurrencies and AI. After all, many people often compare the two.
If we compare cryptocurrencies to AI, it’s like comparing apples to oranges.
But if we look at today’s AI investments from the perspective of cryptocurrency investors, perhaps we can find some similarities: both are comprehensive technologies, each with its own infrastructure layer and application layer.
But the confusion is also similar: it is still unclear which layer will accumulate the most value, the infrastructure layer or the application layer?
“What if a headline does what you want to do” – this may be the nightmare of all entrepreneurs.
The history of internet development in the past has proven that this nightmare is not groundless, from Facebook and Zynga breaking up, to doing mobile games by themselves; from later Twitter live streaming and Meerkat, the advantages of big companies make it difficult for startups to compete.
In the crypto industry, because the economic models of the protocol layer and the application layer are different, the focus of each project is not to do every layer in the ecosystem.
Take public chains (ETH, Sol, etc.) as an example. The economic model determines that the more people use this network, the higher the gas income and the higher the token value. Therefore, the top projects in the crypto world spend most of their energy on ecosystem construction and attracting developers.
Only the emergence of popular applications will increase the use of the underlying public chains, thereby increasing the market value of the projects. Early infrastructure projects may even directly provide subsidies ranging from tens of thousands to millions of dollars to eligible application developers.
Our observation is that the value capture of infrastructure and application layers is equally important, but for capital, both infrastructure and application layers will take turns to shine, but the winners take all.
For example, a large amount of capital flooding into public chains has improved the performance of leading public chain projects, giving birth to new application models and eliminating mid-to-low-tier public chains; capital flowing into new business models, coupled with user growth, has led to higher requirements for underlying infrastructure, driving infrastructure upgrades.
So what can we refer to for investment? The simple truth is that investing in both infrastructure and application layers is not wrong, but the key is to find the top players.
Let’s fast forward to 2024 and see which public chains have survived. Here are three simple conclusions:
Disruptive technology plays a small role in the success of projects. “Ethereum killers” that were previously sought after by Chinese and American VCs and focused on professors and academic concepts (such as Thunder Core, Oasis Labs, Algorand, etc.) ultimately only Avalanche made it, and it was on the premise of professor departure and full compatibility with the Ethereum ecosystem.
On the other hand, Polygon, which was not favored by investors in the past because of its lack of technological innovation (forking ETH), has now become one of the top 5 ecosystems in terms of on-chain assets and users.
It is unfortunate that projects like Near Protocol, which focus on sharding technology and can outperform Ethereum in TPS, have raised nearly 400 million US dollars in funding but currently only have a market value of around 60 million US dollars.
Of course, the numbers fluctuate daily with market conditions, but the trend is quite clear.
Stickiness of developers and users comes from the ecosystem. For public chains, users not only include end users but also developers (ignoring miners, which is a completely different model).
For end users, the ecosystem with rich applications and more transaction opportunities will have more stickiness.
For developers, the ecosystem with more users and better infrastructure, such as complete wallets, block explorers, and decentralized exchanges, will be prioritized for development. Overall, it presents a flywheel effect where developers and users drive each other.
The network effect of the top players is bigger than imagined. The number of Ethereum users and the amount of funds in on-chain applications are more than all the “Ethereum killers” combined.
When people (especially those outside the industry) think of smart contract chains, they almost always think of Ethereum (just like when people think of AGI, they think of OpenAI today) – it has almost become the industry standard for developing blockchain applications.
In addition, existing top public chains already have a large amount of cash, which can provide investments or donations to developers that startups cannot reach.
Lastly, because most blockchain projects are open source, mature top ecosystems allow decentralized application building blocks to have more possibilities.
So, what are the significant differences between public chains and the development of large-scale models?
Requirements for infrastructure. According to a16z’s statistics, most AI startups spend 80-90% of their early funding on cloud services.
AI application companies spend an average of 20-40% of their revenue on fine-tuning costs for each customer.
In simple terms, the money is earned by NVIDIA and AWS/Azure/Google Cloud.
Although public chains also have mining rewards, the cost of hardware/cloud is borne by decentralized miners, and the scale of data currently processed by blockchains and the billions of data labels required for AI is still negligible. Therefore, the cost of infrastructure is much smaller compared to large-scale models.
Liquidity, liquidity, liquidity. Public chains that have not launched their mainnets can issue tokens, but AI large-scale model companies without users and revenue find it difficult to go public.
So although the performance of various “professor chains” may not meet expectations in the end (after all, Ethereum is undoubtedly the No.1), from the perspective of investors, they are not likely to lose money and it is even unlikely to go to zero.
Large-scale model companies are different. If they can’t raise the next round of funding and there are no white knights, they are easily doomed. In this regard, venture capitalists should be more cautious.
Actual productivity improvement. Through ChatGPT, LLM has found its product-market fit and has truly been widely used by B2B and B2C, improving productivity.
Although public chains have experienced two bull and bear cycles, they still lack a killer app, and application scenarios are still in the exploratory stage.
Perception of end users. Public chains and end users are strongly correlated. If they want to use a decentralized application, they must know which public chain it is on, and then go through the trouble of moving assets to that public chain, thus forming a certain stickiness.
AI, on the other hand, is more silent, like cloud services and processors inside computers. No one cares whether the ride-hailing software is backed by AWS or Alibaba Cloud. Because ChatGPT’s memory is very short-lived, no one cares whether they are chatting on ChatGPT’s homepage or on an aggregator. So, it is more difficult to retain C-end users.
Regarding the application scenarios of encryption in AI, many teams have given their own insights, and it is generally believed that decentralized financial networks will become the default financial transaction networks for AI agents. I think the following diagram accurately summarizes the current stage.


Finding the needle in the haystack more agilely
When I joined the cryptocurrency industry, I had almost no confidence in the concept of decentralization. I believe most industry participants felt the same way in the early stages.
People joined this industry for various reasons – for money, technology, curiosity, or just by chance.
But if you ask me today if I have confidence in cryptocurrencies, I will give a definite answer. You cannot deny the entire industry because there are scams in the crypto industry, just as you cannot deny the entire financial industry because of Madoff’s scandal.
An example that recently happened to me is my friend R (pseudonym).
He successfully turned an idea into a company with 200 employees, positive cash flow, and a market value exceeding 200 million US dollars.
R’s entrepreneurship was based on his understanding of decentralized value.
“My girlfriend is a small internet celebrity on TikTok, but internet celebrities can only get a small portion of the audience’s tips,” he once told me. The world’s largest creator network is not fair, “I want to build a decentralized version.” At that time, I thought he was joking, but about three years later, he actually launched this project. This platform now has hundreds of thousands of users.
For someone like me, who joined this industry at the age of 24 after graduating, the past 7 years have shown me enough aspects of the world: idealists, scammers; those who have achieved excessive returns and those who have lost everything.
I remember my former boss, who made a lot of money in the crypto industry, once said:
“You still have to work hard, otherwise you will become a rich ordinary person.”
I think there is a respected investor who described the work of VCs as “finding the needle in the haystack.” For me, VC investment in the crypto world is also such a process.
The only difference is that the haystack of cryptocurrencies may move faster. So we must always stay agile.
The author of this article is JW (@bestmosquito), the founder of Impa Ventures. Impa Ventures is a fund that focuses on early-stage investments in the Web3 industry.
Shiran and James, the other two partners of Impa Ventures, and analyst Guo Yunxiao also contributed to this article.
This article is a collaborative reprint from:
Deep Tide