Welcome to the 73rd Coin Report. In today’s report, I will be assessing the fundamental and technical strengths and weaknesses of Fetch.ai. This will be comprised of an analysis of a number of significant metrics, an evaluation of the project’s community and development and an overview of its price-history. The report will conclude with a grading out of 10. I hope you enjoy the read!
This is the seventh of my subscriber-exclusive Coin Reports, with Fetch.ai winning the January poll, tied with LTOxVIDT (which I will be also be publishing a report on over the next few days). Each month, I will run a poll and publish a Coin Report on the winner either that month or the following month, available only to those of you that are subscribed to my premium content.
Keep your eyes out for the February poll, which will be hitting your inboxes soon.
I hope this report will prove objective where it must be and fair on more subjective matters. For those who’d like to learn a little more about Fetch.ai prior to reading this report, here are some primary links:
Token Type: ERC-20 + Native FET on Mainnet
Consensus Mechanism: Proof-of-Stake (Minimal Agency Consensus)
Sector: Artificial Intelligence
Exchanges: Binance, BitMax, Kucoin, MXC, Bitfinex, HitBTC, Uniswap, Hotbit, WazirX and others
Fetch.ai was conceptualised as an open-source, machine learning platform based on a distributed ledger by Humayan Sheikh, Toby Simpson and Thomas Hain in Cambridge, U.K.
Fetch.ai publicly launched via the Binance Launchpad on 25th February 2019, raising $6mn in less than a minute and thus selling out its public sale, with 69,204,152 FET sold to investors at an average price of $0.0867 per FET. This comprised 6% of the maximum supply of 1,152,997,575 FET, issued as an ERC-20 token. Of the remaining supply, 10% was allocated to advisors, 20% to the team, 20% to the Foundation, 17.4% for future release and 15% for mining rewards. The final 11.6% had been allocated to seed and private investors, with 5.24% in the seed round at $0.0341 per FET and 6.38% in the private round at $0.068 per FET.
Fetch.ai launched the first iteration of their mainnet in January 2020, with v2 scheduled for release in March 2021.
FET has been in existence for around two years at this point, given its initial release on Binance Launchpad in early 2019, and the vast majority of that time has been spent in a downtrend, with only one distinct market cycle having played out. Whilst I will discuss FET’s price-history at length in the Technical Analysis section, for now it will suffice to say that Fetch.ai formed its all-time high against both Bitcoin and the Dollar on its first trading day, at 11,171 satoshis, or $0.44. Following a protracted bear market, it formed its all-time low against the Dollar at $0.0086 in March 2020, whilst its all-time low against BTC came in at 148 satoshis in early January 2021.
Whilst terms like artificial intelligence and machine learning and autonomous agents seem complex for the layman, Fetch.ai’s purpose is quite straightforward: to utilise AI and distributed ledgers to build a digital metaverse in which economic activity can thrive.
As stated in its whitepaper:
“Fetch is a decentralised digital representation of the world in which autonomous software agents perform useful economic work. This means that they can perform tasks, such as delivering data or providing services, and are rewarded with a digital currency for their efforts — the Fetch Token.
These agents can be thought of as digital entities: life-forms that are able to make decisions on their own behalf as well as on behalf of their stakeholders (individuals, private enterprises and governments for example). Fetch’s digital world is exposed to agents via its Open Economic Framework (OEF) and is underpinned by unique smart ledger technology to deliver high performance, low cost transactions. The ledger delivers useful proof-of-work that builds market intelligence and trust over time — growing the value of the network as it is used. Fetch can be neatly interfaced to existing systems with minimal effort, allowing it to take advantage of the old economy whilst building the new: plug existing data in to Fetch and watch markets spontaneously form from the bottom up.“
I look forwards to evaluating its progress in this respect.
Let’s begin with some Metric Analysis:
Below are listed a number of important metrics, all of which are accurate as of 21st January 2021. For anyone reading this who has yet to read a Coin Report, it might be worth reading this section of the first report, where any potentially unfamiliar terms are explained. For any terms or metrics specific to this post, I will provide explanations besides the figures.
Price: $0.086 (263 satoshis)
Circulating Supply: 706,993,767 FET (source)
Total Supply:706,993,767 FET
Maximum Supply: 1,152,997,575 FET
% of Max. Supply Minted: 61.32%
Network Value: $60,857.953 (1,859.39 BTC)
Network Value at Max. Supply: $99,249,916
Exchange Volume: $6,138,573 excluding wash
Exchange Volume-to-Network Value: 10.09%
Average Price (30-Day): $0.06
Average Exchange Volume (30-Day): $6,435,482
Average Network Value (30-Day): $47,199,369
Average Exchange Volume (30-Day)-to-Network Value: 13.63%
Volatility* (30-Day): -0.1104
Average Daily On-Chain Transactions (30-Day): N/A
Average Daily Transactional Value** (30-Day): N/A
NVT*** (30-Day): N/A
% Price Change USD (30-Day): +60.2%
% Price Change USD (1-Year): +130.8%
USD All-Time High: $0.44
% From USD All-Time High: -80%
Premine % of Max. Supply: N/A
Premine Location: N/A
Liquidity (calculated as the sum of BTC in the buy-side with 10% of current price across all exchanges): 13.59 BTC
Liquidity-to-Network Value %: 0.73%
Supply Available on Exchanges: 31,613,956 FET
% of Circulating Supply Available on Exchanges: 4.47%
*Volatility is calculated by taking the average price over the given time-period, calculating the difference between it and the highest price and it and the lowest price over that same time-period, and multiplying those figures together. The closer to 0, the less volatility during that period, and vice-versa. Read this for more on volatility.
Supply Emission & Inflation:
Block Reward Schedule: According to token release schedule (pictured below, source), 77.8% of the maximum supply will be in circulation in January 2022, with close to 100% of the supply in circulation in 2026. The FET tokens allocated to mining rewards will be issued in perpetuity at a deflationary rate.
Average Block Time: N/A
Current Block Height: N/A
Annual Supply Emission: 190,038,346 FET for forthcoming year (499.8 BTC at current prices)
Annual Inflation Rate: 26.88%, declining each year
Circulating Supply in 365 Days: 897,032,113 FET (estimated, as pictured below)
Public Sale Period: 25th February 2019
Total Tokens: 1,152,997,575 FET
Total Tokens Available for Public Sale: 69,204,152 FET
Total Raised: ~$6,000,000
Average Price Per Token: $0.0867
Total Tokens Sold: 69,204,152 FET
- Team: 20%
- Foundation: 20%
- Advisors: 10%
- Public Sale (Binance Launchpad): 6%
- Future Release: 17.4%
- Mining Rewards: 15%
- Seed Sale: 5.24% at $0.0341 per FET
- Private Sale: 6.38% at $0.068 per FET
Address Count: 9,474
Circulating Supply Held By Top 10 Addresses: 13.52%*
Circulating Supply Held By Top 20 Addresses: 19.45%*
Circulating Supply Held By Top 100 Addresses: 26.32%*
Inactive Address Count in Top 20 (30 Days of No Activity): 9*
*Excluding team and exchange-owned addresses, but including subsequent private addresses.
There’s quite a lot to cover here for Fetch.ai and I’d like to begin by looking at the General metrics before looking at the (lack of) Supply Emission and Inflation and concluding with some Distribution analysis:
Beginning with Volatility, Fetch.ai came in at a fairly high level. I calculated its 30-day figure to be -0.1104, which places it in the top third among prior reports. This is potentially indicative of the beginning of a new cycle, at least against the dollar; something we will look at more deeply later in the report.
Moving on, let’s take a look at the two Liquidity-related metrics:
For buy-side Liquidity, I calculated that there was 13.59 BTC of buy support within 10% of current prices across listed exchanges, equating to 0.73% of its Network Value. This is very much an impressive figure, placing it joint 6th-highest among previous reports.
Looking at the sell-side, I calculated there to be 31,613,956 FET available for purchase on the orderbooks, equating to 4.47% of the circulating supply. This is a moderately high figure, placing it 8th-highest. What this indicates is that though there is a somewhat high degree of desire to sell FET, with an above average amount of its circulating supply placed in the orderbooks on exchanges, there is an even greater demand for FET at current prices.
Before I move on from the General metrics, let’s take a look at those related to volume:
Fetch.ai traded $6,138,573 of Exchange Volume over the past 24 hours, equating to 10.09% of its Network Value; a very strong figure when one considers that this is excluding wash volume. More impressively, its Average Daily Volume for the past 30 days was $6,435,482, equating to 13.63% of its Average Network Value for the same period. This is the 8th-highest figure recorded in these reports and is very much indicative of speculative interest.
Now, with regards to Fetch.ai’s supply emission, in short, there is not a lot, as all FET was technically created in the genesis block but some of it remains locked and allocated to future releases and mining rewards for the network. If we refer to the emission schedule from above, we see that ~897mn FET is expected to be in circulation in one year’s time, meaning that 190,038,346 FET is expected to join circulation during that time. This equates to 499.8 BTC at current prices and gives Fetch.ai an annual inflation rate of 26.88%, decreasing yearly. Close to 100% of the supply is expected to be in circulating in 2026.
However, more significant is the relationship between this Supply Emission and the Volume metrics mentioned above:
Given that ~190mn FET will join circulation over the next year, we can work out that the average daily supply emission is 520,653 FET, or 1.37 BTC-worth at current prices. This equates to $44,776 of daily supply emission. As Fetch.ai traded ~$6.1mn of real volume over the past 24 hours, and an average of $6.46mn of real volume daily for the past month, we find that Fetch.ai’s average daily supply emission is covered 137x by its 24-hour volume and 143.7x by its Average Exchange Volume. Further, Liquidity of 13.59 BTC covers the average daily supply emission by almost 10x. Clearly, there are no real headwinds for price growth with regards to emission.
In short, any decreases in price are undoubtedly driven either by distribution from larger holders or distribution from smaller holders, and not from the daily emissions being dumped on the market.
Let’s wrap up this section with a look at Distribution:
Looking at the Fetch.ai rich-list, I found that there were 9,474 holders, which places it in the bottom-third among prior reports.
Of these holders, the top 10 addresses control 13.5% of the circulating supply; the top 20 control 19.45%; and the top 100 control 26.32%. These figures are excluding team and exchange addresses. Team and exchange addresses collectively control 880mn FET among the top 10 and 899mn FET among the top 100, which means that 76% of the maximum supply is in the hands of team and exchange addresses in the top 10. As a percentage of maximum supply, the top 10 non-exchange and non-team addresses control 8.29%; the top 20 control 11.93% and the top 100 control 16.14%.
Now, regarding the activity of the top 20 non-exchange/team addresses over the past 30 days, many of these were inactive for that period; 9 addresses, in fact. Of the remaining 11, 4 addresses were in distribution and 7 in accumulation, with cumulative net inflows of +22,702,553 FET over the past 30 days, equating to 3.21% of the circulating supply.
Let’s now take a look at the Fetch.ai community:
There are two primary aspects of community analysis: social media presence and Bitcointalk threads. I’ll begin with the former before moving on to the latter.
Concerning social media presence, there are four main platforms to examine: Twitter, Facebook, Telegram and Discord.
Fetch.ai is present on all platforms except Facebook. To begin, let’s look at the various social metrics that I calculated from the Fetch.ai Twitter and Facebook accounts:
Twitter Followers: 27,090
Average Twitter Engagement: 0.39%
Facebook Likes: N/A
Facebook Posts (30-Day): N/A
Average Facebook Engagement: N/A
As usual, I will be using RivalIQ‘s social benchmark report for evaluation purposes.
Fetch.ai has a moderately large Twitter audience of 27,090 followers, which places it 10th-highest among coins previously reported on. Its engagement is not so great at 0.39%, placing it in the bottom-third of prior reports. However, this is 8.6x greater than average across all industries of 0.045% and 14.4x greater than the average in the Tech and Software industry of 0.027%.
There is no Facebook page for Fetch.ai.
There are 750 members of the Fetch.ai Discord group.
It appears that the group is not really in use by the community, with less than a dozen messages posted across the various channels throughout January.
There are 10,111 members of the Fetch.ai Telegram group. There are also 3,674 subscribers to the Fetch.ai Announcements channel.
I have provided my key takeaways from recent activity in the group below:
- This is clearly the community hub, as there is a great deal of activity here, with hundreds of daily messages and constant conversation.
- Community queries (primarily regarding staking) are responded to promptly.
- Current staking rewards are ~10% APY.
- The v2 mainnet launch is expected in March 2021, but the ERC-20 token will remain in use for the foreseeable future.
- Much of the discussion does focus on FET staking, at present, as there some issues users are finding regarding web browser functionality with MetaMask.
- Fetch.ai recently integrated with the Amadeus global distribution system, providing access to over 770,000 hotels.
- Fetch.ai will be launching an agent-based oracle network in the future.
- The incentivised testnet went live recently, with 1,000,000 FET in rewards for participants.
- Autonomous Travel Agents are now possible with the Fetch.ai system, reducing costs for consumers and improving privacy.
- Overall, there is palpable excitement about Fetch.ai from the community, particularly regarding the rest of 2021.
There is no BitcoinTalk thread available for Fetch.ai.
And that concludes my evaluation of the Fetch.ai community.
Let’s now look more closely at development:
For the following Development analysis, I will be evaluating project leadership, the website, the roadmap, the whitepaper, the wallets and finally providing a general overview:
The Fetch.ai team is comprised on 35 employees as per the website, with 45 employees listed on LinkedIn. There are also 12 advisors listed on the website. The team are currently hiring for 8 positions.
More specifically, the core team consists of:
- Humayun Sheikh, CEO and Co-Founder, investor in DeepMind
- Toby Simpson, COO and Co-Founder, producer at Creatures with 30+ years experience in software
- Thomas Hain, CSO and Co-Founder, Professor at Sheffield with experience in advance machine learning
- Jonathan Ward, CTO, PhD in Machine Learning at UCL
- Maria Minaricova, Director of Business Development, previously at Oracle and GEANT
- Attila Bagoly, Software Engineer
- Peter Bukva, Principal Software Engineer
- Joshua Croft, Application Lead
- Robert Dickson, Senior Software Engineer
- Ed Fitzgerald, Lead Software Engineer
- Nathan Hutton, Senior Software Engineer
- Aristoteles Triantafyllidis, Software Engineer
- Jiri Vestfal, Software Engineer
- Juan Besa, Software Engineer
- Denis Trapeznikov, Senior Software Engineer
- Lokman Rahmani, Senior Software Engineer
- Emma Smith, Machine Learning Engineer
- Jia Liu, Senior Cryptography Engineer
- Yujian Ye, Machine Learning Scientist
- Matt McDonnell, Senior Research Engineer
- James Riehl, Multi-Agent Systems Engineer
- Diarmid Campbell, Senior Software Engineer
- Marco Favorito, Machine Learning Engineer
- David Galindo, Head of Cryptography
- Ali Hosseini, Artificial Intelligence Researcher
- David Minarsch, Lead Economist
- Jin-Mann Wong, Research Scientist
- Chris Atkin, Senior Marketing Executive
- Jay Loe, Video and Graphics Content Creator
- Kevin Racaza, Graphic Designer
- Lisa Condon, HR Generalist
- Gary Wood, Head of IT
- Jonathan Winch, IT Systems Administrator
- Bart Centlewski, DevOps/Senior IT Engineer
- Stefanos Malliaros, SecDevOps Engineer
The advisory board comprises:
- Alexandra Brintrup, Lecturer in Digital Manufacturing at University of Cambridge, Lead at the Manufacturing Analytics Research Group at Cambridge
- Michael Wooldridge, Head of Department and Professor of Computer Science at University of Oxford, Recipient of ACM Autonomous Agents Research Award
- Anisoara Calinescu, Senior Lecturer in Computer Science at University of Oxford
- Kash Iftikhar, Vice President of product, strategy and GTM for Oracle Cloud Infrastructure
- Monique Gangloff, Senior Scientist at University of Cambridge, Department of Biochemistry, with 35+ international peer-reviewed publications
- Melvyn Weeks, Assistant Professor in Economics at University of Cambridge
- Abe Ulusal, Executive Director at Mitsui Bussan Commodities Limited
- Philip Price, Ferrometrics LLC and Deputy Chairman of the London Metal Exchange Steel Trading Committee
- Jonathan Fish, co-founder of Javelin Commodities Trading, previously at EDF Trading and Goldman Sachs
- Jamie Burke, Founder and CEO of Outlier Ventures
- Steve Grand, AI Specialist, creator of Creatures, OBE for Services to Computing
- Niall Armes, Consultant, previously CEO and CSO at TwistDx, PhD from the Imperial Cancer Research Fund
The website can be found here.
The Fetch.ai website is clean in its design, appears organised and comprehensive, but is not particularly well-branded.
If we begin with the homepage, the tagline reads Artificial Intelligence for Blockchains, which to be honest is not particularly specific, but we do find a more focused overview below it, which highlights the decentralised nature of the project, as well as the desire to build a digital economy using machine learning on distributed ledgers. We are invited to buy FET or stake it, also.
As we scroll down the page, we find some recent news regarding autonomous travel agents, with Fetch.ai having built a framework that allows hotel operators to use AI travel agents to “market, negotiate and trade their inventory”, with 770,000+ hotels connected. Payments can be received in fiat or crypto and all is facilitated by FET. A further reading link is provided here (one I will cover a little later). Next, we find a more comprehensive overview of the project, discussing the fact that Fetch.ai is based in Cambridge and is building the infrastructure that will allow for thriving digital economies. Current use-cases include trade optimisation in finance, reconfiguration of public transport networks, adaptation of smart cities, the removal of middlemen from the gig economy and the connection of energy networks to smart grids. Current partners include Telekom’s Innovation Laboratories, Blockchain for Europe, Trusted IoT Alliance, Binance and Sovrin. As we come to the footer of the page, we find links to the roadmap (covered below), the team (covered above), a comprehensive FAQ and social platforms.
Turning back to the navigation menu in the header, we find detailed pages on Use Cases and the Token.
Beginning with the former, here we find several use-cases for the technology, including:
- Smart Cities, with a field trial in Munich, Germany
- Commodity exchange and DeFi
- Collective Learning, whereby doctors can utilise it for optimal diagnosis, for example
- Signs agents, where traffic signs can communicate with vehicles
- Supply Chains for future pattern analysis
- Transport & mobility, with autonomous economic agents operating on an individual’s behalf and adjusting to unforeseen consequences
- Rail, for inter-communication of all trains and stations in a country
- Smart Home, where autonomous agents can decrease daily energy usage by up to 20%
Next, we find more details on the FET token itself, which is said to be the fuel for the ecosystem. The three primary uses are:
- To build and connect agents to the network, using FET for deployment to allow an agent to operate.
- Train your agent by paying FET to access machine learning.
- Participate in validation by staking FET.
FET is ultimately the gateway that allows users to transact data and services between autonomous agents.
Overall, the website is highly-informative for a potential new visitor. Good stuff.
The roadmap can be found here.
At present, Fetch.ai have a detailed Q1 2021 roadmap available, presented as an overview of four key goals, as opposed to a more traditional chronological roadmap. There is no roadmap available for long-term goals, at present.
The focus of the first quarter for the team is the planned upgrade to mainnet v2, which is scheduled for March 2021. Given this focus, the team will be working on upgrades to the four core components of the ecosystem: the Ledger; the Agent Framework; SOEF – Agent Search and Discovery; and the Collective Learning Framework.
The Ledger will see the v2 mainnet release, with Tendermint integration for faster, more secure consensus; the release of the Beacon World testnet, where FET holders can create and operate nodes in anticipation of mainnet release (phase 1 already live); and integration of a secure pseudo-random number generator that is cross-chain interoperable for use in agent-based applications.
The Agent Framework will have its v1 launch, which is expected to be feature-complete for commercial and community deployment; the release of the Autonomous Economic Agents GUI and CLI, as well as the AEA Registry; the launch of the Agent Communication Network, allowing agents to communicate and negotiate prior to conducting transactions; and updates to the FET tokenomics and incentives.
Agent Search and Discovery (Simple-Open Economic Framework) will have identity support via proof-of-identity for peer-to-peer trust; integration within the mainnet where FET tokens will be required for complex searches and large data operations; peer-to-peer networking for multi-node operation; and semantic and active search design. This is expected to be the quarter in which the SOEF will evolve towards “the #metaverse for autonomous agents.”
Regarding the Collective Learning Framework, there are two key progressions: the CoLearn User Interface for interacting with the backend of the collective learning system; and the CoLearn model upgrade, with improved logic.
Whilst the expectations for Q1 are focused and ambitious, it would have been great to see some further reading resources linked regarding these goals, potentially breaking them down further for less technologically-literate community members, as understanding much of the roadmap requires a knowledge of specific jargon that a potential new visitor to the project may not be aware of yet. Further, even a brief overview of what to expect post-Q1 2021 would be helpful to better determine the long-term vision of Fetch.ai.
The whitepaper is a highly-technical document, published originally in 2018 and updated in February 2019, thus it is a little outdated for our purposes. However, you can read it here, if you so wish. Instead, I have opted to provide my key takeaways from the most recent blog posts, to better understand where Fetch.ai currently stands in its developmental progress:
- In partnership with Datarella, Fetch.AI launched their Smart City Field Trials, which will take place in Munich, utilising the technology to provide smart mobility solutions in commercial real estate.
- Fetch.ai launched collective learning nodes in LA and London to provide 97% diagnostic accuracy for COVID-19 patients, distinguishing them from pneumonia cases.
- The Mobility Framework was launched, with a decentralised delivery network that uses autonomous economic agents to return revenue to the local economy from large centralised businesses.
- Fetch.ai partnered with Cudo to scale up machine learning service delivery.
- Hegic launched their Autonomous Hegician options trading tool built on Fetch.ai.
- The team partnered with Conflux – the only state endorsed, public permissionless network in China – in order to further the adoption of AI.
- Fetch.ai partnered with Waves to conduct joint research and development.
- The team began working with Warwick Business School on a solution to reduce daily energy costs by 13-18% on their student campus.
- Fetch.ai integrated Chainlink Oracles to their mainnet.
- The team recently launched their incentivised testnet for the v2 mainnet, with over 1,000,000 FET available to participants.
- Fetch.ai announced Mettalex, which will be a commodity derivatives DEX powered by Fetch.ai, as well as Atomix, which is a DeFi lending platform.
- A codebase and application toolkit is set to be released in February for their Autonomous AI Travel Agents, connecting over 770,000 hotels.
- Fetch.ai recently partnered with Yoti for identity automation and credential verification.
As FET is an ERC-20 token, it can be stored on any ERC-compatible wallet, including hardware wallets like Ledger and Trezor, as well as a plethora of web wallets, mobile wallets and local clients.
And that concludes my fundamental analysis of Fetch.ai.
As we can see from the charts printed above, FET has spent the bulk of its two-year existence in a downtrend, having been issued during a bear market.
If we begin by looking at FET/BTC, from the weekly we can see that the pair printed its all-time high when it listed at 11,200 satoshis, before plunging for two months straight until it found support at 3,450 satoshis. This support was brief, as the following few weeks took FET even lower to a new all-time low at 1,413 satoshis, before another consolidation took place in the summer of 2019. As July came, the 1,400-satoshi support gave way too, with FET falling through to October in a slow bleed to 380 satoshis, where it found a new range until March 2020. During the capitulation event in March, this range support broke too, and the pair made a new low at 192 satoshis, which ultimately became the range support for its first ever bull cycle. As price consolidated above 190 satoshis in mid-2020, FET began to get accumulated, eventually breaking out of the range in July, closing the weekly above prior support turned resistance at 380 satoshis. Subsequently, the pair rallied hard for several weeks, testing prior support at 1400 satoshis as resistance and rejecting in late August. This was the first weekly higher-high in its existence. Since, FET has retraced the entire pump, much like newer altcoins did in late 2016 -> Q1 2017. The all-time low has recently been swept, as can be seen on the daily chart, but price has broken back above range support and looks poised for a move back above the 360dMA. I have been buying the retracement into 190 satoshis, and I’m looking to hold this position at least into 913 satoshis, if not a retest and potential breakout above 1400 satoshis later this year. For risk-averse traders, await a breakout and close above the confluence of resistance at 380-400 satoshis and buy the retest, as that will be the confirmation of the cyclical reversal, in my opinion.
Turning to FET/USD, the early part of its history is much the same as the BTC pair, with an all-time high at $0.44 forming in its first week, before months of capitulation culminating in an all-time low at $0.0087 in March 2020. Price then rallied for five months into resistance at $0.188, having then retraced back to the reclaimed range support at $0.032 in November 2020. Since, we have seen re-accumulation occur above this support, with last week breaking out of the range and beginning the second wave of its bull cycle. I expect the next three months to be fruitful for FET, with an initial upside target of $0.20, but a longer-term target of $0.33 if not the all-time high at $0.44.
And that concludes my evaluation of Fetch.ai.
This report is now approaching 5,000 words, and it is time to draw it to a close.
My final grading for Fetch.ai is 8 out of 10.
Here, you can find my grading framework, for reference.
Lastly, here is a link to a Google Sheets file with any significant data from previous reports compiled for cross-comparative purposes. I will keep this updated as I continue to write these reports.
I hope this report has proved insightful and that you’ve enjoyed the read! Please do feel free to leave any questions in the Comments, and I’ll answer them as best I can.