Out of Reach — Fetch.AI Fundamental Analysis

This fundamental analysis was published October, 2023, and is part of a weekly paid newsletter from the Crypto Consulting Institute that provides market insights, actionable trade signals, and monthly fundamental analyses. For more information on receiving FAs as they are released, visit: https://www.cryptoconsultinginstitute.com/newsletter

Imagine every transaction, every interaction, every piece of data is stored in an immutable ledger, accessible to all and yet secure from manipulation. This is the world of blockchain, a decentralized network that has the potential to revolutionize industries from finance to healthcare to supply chain management.

But what if the power of artificial intelligence could harness this vast storehouse of data? What if AI could analyze this data, identify patterns, and make predictions with unprecedented accuracy? This is the promise of AI-powered blockchain technology, a fusion of two transformative technologies that has the potential to change the world.

Let’s take the example of supply chain management. Today, supply chains are complex and often opaque, with goods traveling across continents and changing hands multiple times. This makes it difficult to track the progress of goods, ensure their authenticity, and prevent fraud.

But with AI-powered blockchain technology, we could create a transparent and efficient supply chain. Every step in the journey of a product, from its origin to its final destination, could be recorded on the blockchain. And AI could analyze this data to identify potential disruptions, optimize shipping routes, and predict demand.

The benefits of AI-powered blockchain technology extend far beyond supply chain management. In finance, AI could detect fraud and prevent money laundering. In healthcare, AI could analyze patient data to identify potential health risks and develop personalized treatment plans. In education, AI could personalize learning experiences and provide real-time feedback.

Envision a service that seamlessly integrates parking availability, charging stations, wheelchair accessibility, real-time weather updates, current restaurant table availability, and review information of nearby establishments, all tailored to the user’s preferences based on their historical usage patterns. This service would not only incorporate new information instantly but also learn from the user’s interactions to continuously refine its recommendations. This personalized and proactive approach to information delivery is what the Fetch-powered ecosystem enables.

Fetch unlocks the potential of static, previously unsearchable data, transforming it into a valuable commodity that can be traded and utilized within the ecosystem. Instead of replacing existing data marketplaces, Fetch is an intermediary, bringing them together and infusing them with life. This creates a dynamic data landscape that responds to user needs in real-time, revolutionizing how we interact with information.

AI-powered blockchain technology has the potential to make our world more efficient, more secure, and more equitable. It is a fusion of two transformative technologies that have the power to change the world for the better.

But before we dive in, is the future today, or at the very least imminent?

Bluntly, no.

Yet, we should appreciate that AI Crypto will play a role in one of the key performing narratives of the next bull market.

This fundamental analysis looks into the Fetch.AI token and explores its proof of concept and stated use case. Through this FA, we will give an overview of the core value proposition of Fetch.AI, examine some of the challenges, and wrap up by evaluating how we should seek to play the AI narrative, building on knowledge from previous FAs on APIs and data computation, as we creep closer to the next bull market.

Fetch Agents — A Dog with a Bone

The Fetch ledger is a unique data structure that incorporates features from both transaction chains and directed acyclic graphs (DAGs). This hybrid design provides a combination of the benefits of both technologies, resulting in a ledger that is both efficient and secure.

Transaction chains, like those used in Bitcoin and Ethereum, are characterized by their linear structure, where each transaction is linked to the one before. This creates a tamper-proof record of transactions, but it can also lead to scalability issues, as the chain grows longer.

Directed acyclic graphs, as we have covered in previous FAs, offer a more flexible and scalable structure. In a DAG, transactions can be linked to multiple other transactions, creating a network of interconnected data. This allows for faster transaction processing and reduces the risk of congestion.

The Fetch ledger combines the best of both worlds by adopting a hybrid structure. It utilizes a DAG for transaction processing, but it also maintains a transaction chain for security purposes. This allows the ledger to achieve both high throughput and robust security.

The DAG design is purposed to facilitate the exponentially scaleable nature of machine learning, which plays a crucial role in enhancing the performance, accessibility, and trust of the Fetch ecosystem. It enables three key capabilities:

• Understanding the Past: Machine learning models are employed to capture and analyze agents’ behavior within the ecosystem, providing valuable insights into past actions and patterns.
• Planning the Future: Machine learning algorithms optimize resource allocation and task distribution among agents, ensuring efficient workload management and convergence towards desired outcomes.
• Grasping the Present: Machine learning facilitates the dissemination of current information and beliefs across the network, fostering a shared understanding and enabling agents to make informed decisions.

Real-world costs and consensus-building mechanisms, such as proof of work or stake, can be tailored to suit specific transactions and participants. This flexibility empowers agents to utilize historical data and reputation information when making decisions. At the same time, users can engage with the system based not only on contractual obligations but also on trust.

Among the numerous applications of machine learning within the Fetch ecosystem, a few stand out for their transformative potential:

• Decentralized Data Marketplaces: Machine learning algorithms can facilitate the creation and operation of secure, decentralized data marketplaces, enabling agents to trade data assets seamlessly and efficiently.
• Supply Chain Optimization: Machine learning can optimize supply chains by predicting demand, identifying bottlenecks, and recommending corrective actions, leading to improved efficiency and reduced costs.
• Asset Management: Machine learning can assist asset management by analyzing market trends, identifying investment opportunities, and making informed trading decisions.

Integrating machine learning into the Fetch ecosystem seeks to disrupt various industries and enable individuals to engage in a more secure, efficient, and trustworthy digital world.

At a high level, the Fetch.AI ecosystem is a three-layer architecture that consists of the following layers:

Access Layer, known as the Open Economic Framework layer, it serves as the primary point of interaction for users and businesses. It provides various tools and services to help users access and utilize the Fetch.AI network.

Autonomous Economic Agent Layer: This layer comprises autonomous agents, also called Economic Agents (AEs). These AEs are intelligent software programs that can act on behalf of their owners to perform tasks such as data trading, supply chain optimization, and asset management.

Smart Ledger Layer: This layer is the foundation of the Fetch.AI ecosystem, providing a secure and decentralized network for AEs to interact and exchange value. It is based on a novel blockchain technology that combines elements of transaction chains and directed acyclic graphs (DAGs).

The three layers of the Fetch.AI ecosystem work together to provide a comprehensive and scalable platform for building and deploying AI-powered applications. The Access Layer simplifies user interaction, the Agent Layer enables autonomous and intelligent behavior, and the Ledger Layer ensures secure and transparent transactions.

Here are some examples of how Fetch can bring static data to life:

• Personalized Travel Recommendations: Fetch can analyze a user’s travel history to identify their preferred destinations, modes of transportation, and accommodation styles. Based on these insights, Fetch can provide personalized travel recommendations, including parking availability, charging stations, wheelchair accessibility information, and real-time weather updates.
• Restaurant Discovery and Reservations: Fetch can leverage a user’s dining preferences to recommend restaurants that match their taste. It can also check for table availability in real time and enable users to make reservations directly through the service.
• Smart City Infrastructure Optimization: Fetch can integrate data from various city infrastructure systems, such as traffic sensors, parking meters, and environmental monitoring stations. This integrated data can then optimize traffic flow, improve parking availability, and provide real-time air quality updates to citizens.

By unlocking the power of static data and connecting it with user preferences, Fetch seeks to transform how we interact with information and revolutionize various industries.

$FET Tokenomics and Technical Analysis (As of 06.11.2023)

Price: $0.38

Market Cap: $397,134,545

Circulating Supply: 1,043,462,805

Total Supply: 1,152,997,575

Short term: $FET recently broke through and flipped the golden pocket to support and is currently shaping up to retest the Q1 highs that coincide with the 0.5 fibs and is painting a bull flag, signaling a move higher. Should the price flip the 0.5 fib/Q1 highs to support, there is a clear path to retesting Q2 highs at $0.44. The bears would seek a retest and subsequent break of the 0.618 golden pocket. Bulls would seek a break out of $0.39 and tightly manage a long to Q2 highs.

Mid to long term: $FET still forms a higher-low on higher time frames. A loss of Q3 lows at $0.16 would invalidate the high time frame uptrending structure. A break of Q1 highs at $0.61 would validate a likely continuation of an uptrend on higher time frames. Mid to long-term trade thesis’ should pay close attention to Bitcoin Dominance charts for accumulation/DCA plays.

Throwing a Feint — Fetch.AI Discussion

Information regarding the Fetch ecosystem in action has been limited to proof-of-concept papers and videos. As far as an accessible product is concerned, there is scarce evidence, apart from what the marketing team has remarked, that Fetch.AI will be operational for the masses anytime soon.

This presents an opportunity to discuss the AI Crypto narrative in general, as it relates to Fetch.AI.

Those of you reading this are acutely aware of the attention the launch of ChatGPT 3.5 for public consumption prompted in terms of hype not just inside of the crypto space for the potential of AI technology but more broadly through the mainstream.

The catch is the ease with which anyone can prompt the generation of AI images, text-to-image, and post queries that often are responded to with varying degrees of accuracy. Moreover, the sheer volume of work that can be completed through AI significantly scales user productivity. For example, ChatGPT 3.5 is thought to be capable of passing the New York State Bar Exam for participants to qualify as a lawyer. However, ask it a political question, or leave room for bias to manifest, and you will quickly see the programmer’s views pushed as objective truth. Despite AI’s intelligence, there is still a long way to go.

With this in mind, we have to ask ourselves a big question: Does blockchain technology really have the potential to take AI to the next level?

The idea of an autonomous economic agent, as the core value proposition of Fetch.AI, indeed has allure. But, while AI is remarkably impressive in its current form, it still has a long way to go, and as it stands, AI is finding multiple use cases within the bounds of its current capabilities, without the assistance of Web 3.

Those of you who are CCI clients would recall a key question on fundamentals by Joe Shew through the CCI course — Does the project solve a real-world problem today, or in the future? The former is likely to capture value through utility, say having to purchase $ETH to pay for gas fees on the Ethereum network; the latter is speculation, purchasing a promise of utility that has yet to be seen.

AI, at this point and as it pertains to blockchain, offers little more than a buzzword. A narrative, similar to the crypto gaming narrative, was a high-performing narrative before there was even a fully released game that could onboard users and create a network effect. What we see with the AI Crypto craze is similar, except that its complexities are far more exorbitant without the foggiest idea of what an AI product that cannot exist without a blockchain will look like. The AI Crypto narrative will likely behave similarly to the metaverse narrative, which, as we know, was prompted mainly by Mark Zuckerberg changing ‘Facebook’ to ‘Meta.’ Should we continue to see significant advances in AI, we will likely see some contagion to the AI crypto narrative again.

The next critical question we must resolve is what added value is there in integrating blockchain technology with AI? Currently, ChatGPT and Bard are accessible to anyone with an internet connection; moreover, there are no complicated onboarding processes, such as learning to use a cryptocurrency wallet. What need is there for people to buy a token to access AI when it can be done from Web2 open-source that already exists?

When it comes to Fetch.AI, on the surface, it is a revolutionary value proposition. Autonomous Economic Agents operate as autonomous digital entities that grow in efficacy through interaction with queries and other AEAs to facilitate a query end-to-end. A common example in the whitepaper is windshield wiper data being able to inform weather forecasts, and vice versa. Another example is enquiring about a trip to Japan, and having activities, accommodations, and reservations made by prompting a single query. It’s a big idea. But after 6 years, Fetch.AI appears to have little to show. While there is talk of companies like Bosch utilizing their technology, and closed beta for multiple AI-related products, there has not been so much as a screen share in real-time of an Agent in action.

Moreover, the absence of a tangible minimally viable product, and overemphasis on marketing coupled with departures of engineering talent from the core team, indicates a willingness, or even an incentive, for Fetch to play the narrative like a fiddle.

The problem in AI cryptos since the release of SingularityNet ($AGIX) is that it does not solve a real-world problem today but promises to resolve AI-based technological problems as we see the technology continue to roll out. If AI moves at the speed we have seen in blockchain technology, and we do not approach that long-awaited road bump in its development whereby AI cannot continue to evolve without blockchain technology. It is unlikely we will see anything particularly groundbreaking come out of it.

This is not to say there is nil value, but perhaps the cryptocurrency space needs to mature sufficiently to realize its full potential. The actual machine learning process is not enriched by being on the blockchain, but perhaps the data storage and computational networks provided by blockchain enriches the AI data processes. Reducing overheads of data and energy costs will be a primary objective that needs to be resolved for AI, The Department of Energy estimates that computers consume roughly 1–2% of the global electricity supply. In 2020, this figure was estimated to be around 4–6%. If we continue at this rate, it is projected to rise 8–21%; conversely, Bitcoin is estimated to consume around 7 gigawatts of electricity, equal to 0.21% of the world’s supply. Alternate blockchains and distributed ledgers create a free market for GPU (data computation) utilization. This is what blockchains can offer AI in the short term.

AI-driven smart contracts with NFTs could play a role in metaverses to have autonomous non-player characters (NPCs), which would undoubtedly be a sophisticated advancement in gaming. Still, the overall application of AI would likely become a derivative of advancements in the Metaverse and Augmented Reality. There is additional low-hanging fruit in AI-driven trading bots that exist in some form today but require ongoing tweaking to maintain an optimal strategy or risk trading a portfolio into liquidation with configurations that do not align with market conditions.

In light of the above, and for the likes of Fetch.AI — you are investing in a narrative, not an operational and measurable utility. With blockchains, we can track user growth and transaction volume. There are permissionless DeFi protocols that anyone who takes the time to learn how can experiment with. While products like Fetch.AI that supposedly have products continue to operate behind closed doors, this will likely make investors nervous until a permissionless product is accessible. Until then, Fetch.AI is worth monitoring but is not a bag worth marrying. Any investment in it should be knowingly done so purely toward positioning yourself to capitulate aggressively on the narrative, not for the utility. Suppose you believe in a future where Crypto and AI intersect. In that case, data computation cryptos with existing and accessible utility are likely a safer place to make more significant sum investments in the mid-term, and any position you build in Fetch should be solely for taking profit as new liquidity rushes into a bull market.

References

Coingape, AI Crypto Token Fetch.AI Reveals Ambitious 2023 Roadmap; FET Price Poised For Bull Run?, March 3rd 2023, https://coingape.com/ai-crypto-token-fetchai-reveals-roadmap-fet-price/

Fetch.ai Whitepaper, https://whitepaper.io/document/447/fetch-whitepaper

Fetch Newsletter, https://www.linkedin.com/pulse/breaking-ai-make-safer-how-meta-recruited-striking-actors-more-ecwvf/?trackingId=5j2dZUXgTSS37MdWjyvc7w%3D%3D

Penntoday, ‘The hidden costs of AI: Impending energy and resource strain’, https://penntoday.upenn.edu/news/hidden-costs-ai-impending-energy-and-resource-strain

Techcrunch, ‘Blockchain startup Fetch.ai grabs $40M to provide monetization and other tooling for AI-generated information’, March 30th 2023, https://techcrunch.com/2023/03/29/blockchain-startup-fetch-ai-grabs-40m-to-provide-monetization-and-other-tooling-for-ai-generated-information/

Youtube, ‘Top 6 AI Crypto Coins | All Hype or Future of Blockchain?’, April 10th 2023 , https://www.youtube.com/watch?v=hCFOyNd7Wno