Fetch.ai (FET) is an innovative project with the potential to change the world. It's a novel platform that connects IoT (Internet of Things) devices and algorithms to enable collective learning. Built on a highly efficient ledger, Fetch.ai's architecture offers unique smart contract capabilities for deploying ML (Machine Learning)/AI (Artificial Intelligence) solutions for decentralized problem-solving.
Open-source tools allow users to create various ecosystem infrastructures and deploy new trading models.
The Fetch.ai team consists of dynamic, rapidly growing international engineers and forward-thinking technology researchers working on the convergence of Blockchain, artificial intelligence, and multi-agent systems. They aim to build technology for both today and tomorrow; Fetch.ai (FET) is described as a collective superintelligence on top of a next-generation, highly scalable distributed ledger technology. Combined with machine learning, this provides predictions and infrastructure that will power the economy of the future.
The Fetch.ai (FET) team is composed of top-tier software engineers and researchers working across multiple fields (such as multi-agent systems, machine learning, economics, cryptography) developing fascinating and promising new technologies. The team collaborates with the world's best academics and corporate partners to further develop their solutions and implement them in real-life scenarios.
The Fetch.ai (FET) team believes its technology will improve the way we communicate, providing a voice and new opportunities to people, organizations, and the "Internet of Things (IoT)", effectively democratizing the field and improving citizens' lives.

Fetch.ai envisions a world where economic activities can occur without human intervention. They are building a decentralized network consisting of various autonomous agents that can represent themselves, other individuals, devices, and services. These agents can learn, evolve, and solve complex problems independently or collaboratively through artificial intelligence algorithms.
This scenario represents a permissionless system, and the Fetch.ai team plans to enable the self-learning network to complete millions of transactions per second, allowing for global usage and numerous use cases.
Fetch.ai is a platform aimed at connecting "Internet of Things" (IoT) devices and algorithms to enable collective learning. It was initiated by a team based in Cambridge, England, in 2017.
Fetch.ai (FET) is built on a highly efficient ledger and offers smart contract capabilities for deploying machine learning and artificial intelligence solutions for decentralized problem-solving. These open-source tools are designed to help users create ecosystem infrastructures and deploy trading models.

Fetch.ai (FET) was founded by Toby Simpson, Humayun Sheikh, and Thomas Hain. Humayun Sheikh is the current CEO of Fetch.ai. He is also the CEO and founder of Mettalex, as well as the founder of uVue and itzMe. Toby Simpson is the COO of Fetch.ai. He has also served as the CTO at Ososim Limited and held the position of Head of Software Design at DeepMind. Thomas Hain is the Chief Science Officer at Fetch.ai. Previously, he co-founded and managed Koemei.
The Fetch.ai team is led by three founding partners and four department heads. Humayun Sheikh, CEO of Fetch.ai, has a long history with artificial intelligence, being one of the early investors in AI company DeepMind, which was later acquired by Google. His entrepreneurial experience includes the AI venture itzMe and the drone company uVue.
Toby Simpson, CTO of Fetch.ai, has over a decade of experience in the CTO role at other technology companies. He was also involved with DeepMind, where he served as Head of Software Design.
The third Fetch.ai founding partner and Chief Science Officer, Thomas Hain, holds a PhD from the University of Cambridge and is an expert in Machine Learning. In addition to his role at Fetch.ai, he serves as a professor at the University of Sheffield.
The leadership team, in addition to the founding partners, includes Jonathan Ward (Head of Research), Troels F. Rønnow (Head of Software Engineering), Maria Minaricova (Head of Business Development), and Arthur Meadows (Head of Investor Relations). The rest of the Fetch.ai team comprises 10 developers, 11 researchers, and 5 administrative staff.

The service token of Fetch.ai, FET, is designed to find, create, deploy, and train Autonomous Economic Agents (AEAs) and is a crucial part of the platform's smart contracts and oracles.
With FET usage, users can create and deploy their own agents on the network. Developers can access machine learning-based utilities to train autonomous agents and distribute collective intelligence on the network by paying with FET tokens.
Validators are also enabled by receiving FET tokens, which facilitate network validation and reputation.
The Fetch.ai technology stack comprises four different elements:
Agent Framework: Provides modular and reusable components that assist in creating multi-agent systems.
Open Economic Framework: Offers search and discovery functions to agents.
Agent Metropolis: A collection of smart contracts running on a WebAssembly (WASM) virtual machine to maintain an immutable record of agreements between agents.
Fetch.ai Blockchain: Combines multi-party cryptography and game theory to provide secure, censorship-resistant consensus alongside fast chain synchronization to support agent applications.
At the core of the platform, there's a "learner" part representing a unique private dataset and machine learning system where every participant is a learner. The global market, resulting from the collective learning experiment where the machine learning model is collectively trained by the learners themselves, is also present. Then there's the Fetch.ai Blockchain that supports smart contracts for secure and auditable coordination and governance. Lastly, a decentralized data layer based on IPFS allows for the sharing of machine learning weights among all relevant learners.

Fetch.ai technology consists of three main parts: Autonomous Economic Agents (AEAs), Open Economic Framework (OEF), and Fetch Smart Ledgers, all supported by the utility Proof-of-Work consensus model and AI machine learning used by the Fetch blockchain.
Autonomous Economic Agents (AEAs) are created as digital citizens fully enabled to act on behalf of individuals, organizations, and devices. AEAs are paired with data sources and hardware systems that navigate them through the Fetch ecosystem, allowing them to derive value from its predictive nature and data discovery functions. This enables AEAs to use detailed data and predictive models to find the best solution for real-world problems. They can also learn from their mistakes, improving their performance over time.
The Open Economic Framework (OEF) is an adaptive simulation that functions to provide AEAs with maximum connectivity and interaction capacity. The OEF stores information and uses AI to optimize its use for prediction and support AEAs. AEAs can gather information from the OEF, and node operators receive token rewards for providing reliable and consistent information and services.
Fetch.ai Smart Ledgers merge blockchain with elements of directed acyclic graph (DAG) technology in a unique network structure. Fetch.ai groups transactions in a sharding scheme and processes them in chains. Unlike traditional sharding transactions, it can be assigned to several transaction lanes simultaneously.
The transfer capacity of the Fetch.ai system increases over time through chain forking. When a fork occurs, each old stripe is referred to with two new stripes, and the first transactions in these new stripes revert back to the last transaction in the old stripe. This allows the Fetch.ai blockchain to trace the origins of its new chains.

Fetch.ai smart ledgers are monitors that support, evaluate, and track interactions between AEAs in the system. To do this, Fetch.ai utilizes a unique utility Proof-of-Work (uPoW) consensus protocol.
The Fetch.ai team believes this consensus mechanism has several benefits over traditional PoW mechanisms. In traditional PoW, nodes must download every block and sequentially add them to the chain, consuming much time and energy. PoW systems can also lead to miner centralization.
The DAG system will consider any transaction valid after confirmation by two nodes and free up computational resources to train AI. In uPoW, less powerful nodes can still earn some rewards by verifying low-value transactions. This creates a more efficient system that scales well and encourages more widespread node distribution.

Fetch.ai (FET) incorporates machine learning and artificial intelligence across all three layers of its protocol. They are used in four different layers to provide trust information:

Deep learning methods are used to implement each of the above layers. Fetch.ai utilizes process mining, Long Short-Term Memory, and recurrent neural networks. Using these deep learning methods for natural language processing allows Fetch.ai to predict the uniqueness of AEAs in the system by evaluating their past behaviors.

The Fetch.ai team focuses mostly on the technical aspects of the project and allows their community to grow organically, resulting in a smaller but more passionate supporter base.
The largest social group to date for Fetch.ai is currently a Telegram user group with over 10,000 users. Apart from that, Fetch.ai has a relatively low level of engagement on social platforms. Although they have become more active on Medium recently, their Twitter account has only 3,850 followers, and both Facebook and Medium have only 56 followers.
Their YouTube channel has 477 subscribers with 27 videos published last year. Their Reddit page, typically a significant social media channel for blockchain projects, has only 67 readers, and almost all activity has occurred in the last 7 days, likely around the ICO hype.

Given the current level of scrutiny ICOs are under, having clear development goals and activities along with a clear roadmap is truly important.
One of the best places to get an idea of how much work is being done on a project is to look at their GitHub repositories. This provides a direct indicator of the amount of code pushed and hence the development work.
Fetch.ai's GitHub has four public repositories that are quite sparse in terms of recent commits. Indeed, a look at their website claims they are pushing towards 100,000 lines of C++ code, which is quite impressive for a project that is just completing its public ICO. For comparison, there are many projects from last year that completed an ICO and have sparse GitHub’s with almost no code activity.
What to expect from the project in 2019 is laid out on their website roadmaps. Below are some of the most significant developments that have taken place in the quarters of 2019:

This allows the team to develop the technology in a relatively agile manner and adapt to any necessary changes.

The FET token is the trading medium in the Fetch.ai network and allows AEAs to transact with each other, exchanging FET tokens for services, data, or other products. This seamlessly permits machine-to-machine transactions.
FET will initially be an ERC-20 token but the team plans to create a native token later on. When this native token is launched, ERC-20 tokens will be exchanged at a fixed conversion rate for native tokens, and these ERC-20 tokens will be burned. As the mainnet has a planned release in Q4 2019, the native tokens will be launched simultaneously, though this seems like a tight timeline.
The initial coin offering of FET took place on February 25, 2019, at 14:00 UTC. It sold out just 15 minutes after the launch.
During the ICO, private investors could purchase FET tokens using the native BNB token. The high-profile launch of the token on Launchpad and its subsequent listing on an international exchange instantly added value to the token. Additionally, Fetch.ai has been mentioned in renowned news publications like Forbes, Tech Crunch, Business Weekly, and The Guardian. These publications have helped to increase the price of the FET token after its launch.
FET was sold at a price of $0.0867 per token with a minimum purchase of $20. The Fetch.ai team also set a maximum personal purchase limit of $3,000 to ensure that no one was excluded from buying FET tokens. This is a wise move by the Fetch team to set a maximum purchase limit due to the issues previously experienced with the BTT token.
FET is designed to be infinitely divisible, facilitating ease of use even in the smallest microtransactions. This will be highly beneficial for the operation of AEAs and also allow the project to keep the coin supply low if desired.

As of the writing of this article, the live Fetch.ai (FET) price is $0.879579 USD with a 24-hour trading volume of $64,125.621 USD. Fetch.ai has decreased by -1.97% in the last 24 hours. With a live market cap of $656,265,604 USD, it ranks at #116. There is a circulating supply of 746,113,681 FET tokens and a maximum supply of 1,152,997,575 FET coins.

Fetch.ai is one of the more ambitious blockchain projects, especially as it attempts to integrate artificial intelligence. The team seems to have the skill, knowledge, and industry experience necessary to create something that could solve numerous real-world problems across various sectors, from supply chains to energy and much more, which holds great promise for success.
However, there are still very real risks that the project may not be able to realize its visions. Development is expected to be a long process and costly as well. The ICO will help to cover these costs, but it would be beneficial if Fetch.ai could secure some strong corporate partnerships to help support the team's forward efforts.
The project becoming more open and transparent is positive news. In the past, a lack of publicly disclosed information led some malicious individuals to declare the project possibly a scam. This has changed in the last few months, and the added transparency now makes Fetch.ai (FET) a much more promising project.