Making the Most of AI and GPT-like Models: An examination of the industries most suitable for integration

Paulina Lewandowska

19 Jan 2023
Making the Most of AI and GPT-like Models: An examination of the industries most suitable for integration


The way businesses work is being revolutionized by artificial intelligence (AI) and language models like GPT. AI is quickly becoming a crucial tool for businesses wanting to stay competitive in today's fast-paced economy, from automating monotonous processes to offering insightful analysis and predictions. In this article, we'll look at how companies are using GPT-like models and AI to boost productivity, boost efficiency, and boost revenue. We will look at the numerous uses of AI in the corporate world, from customer service to financial analysis. We'll also look at how GPT-like models are specifically employed in content generation and natural language processing to scale up communication and human-computer interaction.

AI implementation by sector

AI and models like GPT can be particularly beneficial in a variety of sectors, including but not limited to:

Natural Language Processing (NLP)Language Translation, Text Summarization, Question Answering
Content CreationAutomated written content generation (news articles, product descriptions, social media posts)
BusinessAutomating tasks (customer service, sales, marketing), Financial forecasting and analysis
HealthcareMedical Diagnosis, Drug Discovery, Personalized Medicine
EducationPersonalized learning experience, Grading and providing feedback
Transport and logisticsSelf-driving cars, Supply chain management
RoboticsObject recognition, Navigation, Manipulation
GamingRealistic and engaging gameplay, New types of games

GPT-like Models in NLP and Content Creation: Automating Writing and Personalizing Content

GPT-like models have demonstrated substantial skills in the areas of Natural Language Processing (NLP) and content generation. Language translation, text summarization, and question answering are just a few of the natural language processing activities that are catered to by language models like GPT-3.

Automated writing is one of the most well-liked uses of GPT-like models in NLP and content generation. GPT-3 is capable of producing written content such as blog entries, product descriptions, and social media updates automatically. By automating the process of content production, GPT-3 can save enterprises a significant amount of time and resources because it can produce cohesive and grammatically sound text. This makes GPT-3 perfect for jobs like writing reports, email drafts, and chatbot scripts for customer assistance.

GPT-like models have uses in personalized content creation for clients in addition to automated authoring. By evaluating consumer data and creating content that is specific to the user's interests and preferences, GPT-3, for instance, can be used to provide personalized product suggestions or targeted advertising. This might aid companies in enhancing their marketing initiatives and raising client involvement.

Automation: Using AI to Simplify Business Processes 

Automating processes is one of the most obvious ways that organizations are utilizing AI. AI can handle a wide range of monotonous activities, from customer care chatbots to automated financial analysis, freeing up employees to concentrate on more worthwhile work. Simple customer care requests, like responding to frequently asked queries, can be handled by AI-powered chatbots, while more sophisticated systems can even manage complicated problems. Additionally, financial analysis tasks like fraud detection and trend prediction can be automated using machine learning models.

AI-Assist in Healthcare Revolution: Diagnosis and Treatment 

AI is being applied in a variety of ways in the healthcare sector to improve efficiency and precision. AI-powered systems, for instance, can help clinicians diagnose illnesses by reviewing medical images and making recommendations. This raises diagnostic precision while lowering the possibility of human error. Drug development is another area where AI is being used in healthcare. AI is capable of analyzing enormous amounts of data, including genetic data, to find potential novel treatments and medications. AI is also being used to develop individualized treatment regimens for patients, which take into consideration aspects like medical history, genetics, and other personal traits.

Intelligent tutoring and Personalized Learning with AI in Education 

Similar to how it is being used in business, AI is being used in education to help teachers grade assignments and give feedback to students. Based on a student's skills, shortcomings, and preferred learning style, AI is used to generate customised learning plans for them. Additionally, it contributes to the development of intelligent tutoring programs that support teachers by offering tailored feedback and assistance to students both within and outside of the classroom. AI is also being used to automate grading and assessment, which can assist save teachers time and increase the effectiveness of the educational system.

AI-Optimized Logistics and Transportation: Supply Chain Management to Self-Driving Cars 

AI is also being used by the transportation and logistics sector to boost productivity and cut expenses. The development of self-driving automobiles is one example of how AI can be used to enhance road safety and lower the frequency of accidents brought on by human mistake. Another area where AI may be used to optimize is supply chain management. This is done by forecasting demand, analyzing data, and making better decisions. AI can also improve fleet management by tracking the whereabouts and condition of vehicles, anticipating maintenance requirements, and increasing productivity.

Enhancing Capabilities and Real-world Functionality of AI-Powered Robotics 

AI is being applied in the field of robotics to enhance the capabilities and usefulness of robots. Robots are now able to recognize and interact with items in the real world thanks to AI, for instance in the field of object recognition. Another area where AI is applied to help robots autonomously navigate in challenging settings is navigation. AI can also be utilized to enhance the manipulation abilities of robots, allowing them to carry out a larger variety of activities, like grabbing and manipulating real-world objects. Robots are improving their ability to work in real-world settings and do tasks that were previously insurmountable thanks to the incorporation of AI.

From Realistic Gameplay to Game Development: AI in the Gaming Industry 

AI is also employed in the video game industry to build new game genres and more realistic and captivating gameplay. By offering more lifelike AI-controlled characters and environments, game AI is one application of AI that aims to improve gameplay realism and engagement.

AI can also be applied to the creation of new game mechanisms and game genres, such as games that adjust to the preferences and skill level of the player. AI can speed up the process of finding and fixing flaws in games, giving gamers a better gaming experience. Game testing can also be improved by AI. We may anticipate even more advancements in the application of AI to gaming as it continues to develop, pushing the limits of what is feasible in the gaming sector.


In conclusion, the incorporation of AI and models resembling the GPT into numerous industries and businesses is proving to be quite advantageous. These technologies are transforming how we conduct business, improve medical diagnosis in healthcare, personalize education, optimize logistics and transportation, and even transform the gaming sector. GPT-like models have enormous potential for content creation and natural language processing. Businesses should be aware of the advantages and potential of these technologies. The integration of AI and GPT-like models in several industries has a promising future.


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Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.


Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics


The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.


What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.

What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained


18 Mar 2024
What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Enter Berachain: a high-performance, EVM-compatible blockchain that is set to redefine the landscape of decentralized applications (dApps) and blockchain services. Built on the innovative Proof-of-Liquidity consensus and leveraging the robust Polaris framework alongside the CometBFT consensus engine, Berachain is poised to offer an unprecedented blend of efficiency, security, and user-centric benefits. Let's dive into what makes it a groundbreaking development in the blockchain ecosystem.

What is Berachain?


Berachain is an EVM-compatible Layer 1 (L1) blockchain that stands out through its adoption of the Proof-of-Liquidity (PoL) consensus mechanism. Designed to address the critical challenges faced by decentralized networks. It introduces a cutting-edge approach to blockchain governance and operations.

Key Features

  • High-performance Capabilities. Berachain is engineered for speed and scalability, catering to the growing demand for efficient blockchain solutions.
  • EVM Compatibility. It supports all Ethereum tooling, operations, and smart contract languages, making it a seamless transition for developers and projects from the Ethereum ecosystem.
  • Proof-of-Liquidity.This novel consensus mechanism focuses on building liquidity, decentralizing stake, and aligning the interests of validators and protocol developers.


EVM-Compatible vs EVM-Equivalent


EVM compatibility means a blockchain can interact with Ethereum's ecosystem to some extent. It can interact supporting its smart contracts and tools but not replicating the entire EVM environment.


An EVM-equivalent blockchain, on the other hand, aims to fully replicate Ethereum's environment. It ensures complete compatibility and a smooth transition for developers and users alike.

Berachain's Position

Berachain can be considered an "EVM-equivalent-plus" blockchain. It supports all Ethereum operations, tooling, and additional functionalities that optimize for its unique Proof-of-Liquidity and abstracted use cases.

Berachain Modular First Approach

At the heart of Berachain's development philosophy is the Polaris EVM framework. It's a testament to the blockchain's commitment to modularity and flexibility. This approach allows for the easy separation of the EVM runtime layer, ensuring that Berachain can adapt and evolve without compromising on performance or security.

Proof Of Liquidity Overview

High-Level Model Objectives

  • Systemically Build Liquidity. By enhancing trading efficiency, price stability, and network growth, Berachain aims to foster a thriving ecosystem of decentralized applications.
  • Solve Stake Centralization. The PoL consensus works to distribute stake more evenly across the network, preventing monopolization and ensuring a decentralized, secure blockchain.
  • Align Protocols and Validators. Berachain encourages a symbiotic relationship between validators and the broader protocol ecosystem.

Proof-of-Liquidity vs Proof-of-Stake

Unlike traditional Proof of Stake (PoS), which often leads to stake centralization and reduced liquidity, Proof of Liquidity (PoL) introduces mechanisms to incentivize liquidity provision and ensure a fairer, more decentralized network. Berachain separates the governance token (BGT) from the chain's gas token (BERA) and incentives liquidity through BEX pools. Berachain's PoL aims to overcome the limitations of PoS, fostering a more secure and user-centric blockchain.

Berachain EVM and Modular Approach

Polaris EVM

Polaris EVM is the cornerstone of Berachain's EVM compatibility, offering developers an enhanced environment for smart contract execution that includes stateful precompiles and custom modules. This framework ensures that Berachain not only meets but exceeds the capabilities of the traditional Ethereum Virtual Machine.


The CometBFT consensus engine underpins Berachain's network, providing a secure and efficient mechanism for transaction verification and block production. By leveraging the principles of Byzantine fault tolerance (BFT), CometBFT ensures the integrity and resilience of the Berachain blockchain.


Berachain represents a significant leap forward in blockchain technology, combining the best of Ethereum's ecosystem with innovative consensus mechanisms and a modular development approach. As the blockchain landscape continues to evolve, Berachain stands out as a promising platform for developers, users, and validators alike, offering a scalable, efficient, and inclusive environment for decentralized applications and services.


For those interested in exploring further, a wealth of resources is available, including the Berachain documentation, GitHub repository, and community forums. It offers a compelling vision for the future of blockchain technology, marked by efficiency, security, and community-driven innovation.


How is Berachain different?

  • It integrates Proof-of-Liquidity to address stake centralization and enhance liquidity, setting it apart from other blockchains.

Is Berachain EVM-compatible?

  • Yes, it supports Ethereum's tooling and smart contract languages, facilitating easy migration of dApps.

Can it handle high transaction volumes?

  • Yes, thanks to the Polaris framework and CometBFT consensus engine, it's built for scalability and high throughput.