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Last week, the that is testing an AI tool that enables news organizations to create content using AI. It is unclear what the exact capabilities of the product are but based on one comment from Google, it helps with tasks like headlines and modifying writing styles. Amidst fears of what impact this could have on journalist jobs, Google quickly put out a : New York Times reported Google clarifying statement Our goal is to give journalists the choice of using these emerging technologies in a way that enhances their work and productivity. Quite simply these tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating, and fact-checking their articles. This has sparked a heated conversation about the future of news with generative AI. The companies making AI products argue that this technology will enable media organizations and journalists to be more effective, while critics contend that it could potentially harm journalist jobs, increase misinformation, and flood the market with low-quality AI-generated content. In this piece, we unpack the by exploring a few things: impact of generative AI on the future of news Types of news articles today by complexity The business of news The anatomy of producing a news article What generative AI can and cannot do well Generative AI and the Future of News — A tale of two worlds Types of news articles today by complexity There are several types of news content on the internet, each with varying levels of complexity in their production. The complexity typically arises from factors like timeliness, the amount of research required, and the story being told. The low-complexity articles include: Factual/data news (eg. article listing mortgage rates in San Francisco, article with numbers from a company’s earnings call) — these are relatively straightforward with minimal subjective opinions or perspectives. Interest-based/informational news (eg. summer recipes published on NY Times Cooking, an article explaining what the Fed interest rate means) — there is some creativity in selecting topics but the focus is more informational and catering to specific interests. Breaking news (eg. an article about a CEO resigning, an article describing an active weather event) — these are typically short articles about a rapidly unfolding event, with limited initial information and emerging facts. The include: high-complexity articles News coverage (with context, research, and facts) — these provide a more detailed explanation of news backed by research, additional context, and often interviews with people, thereby requiring more time and effort to produce; they are also extensively fact-checked. Interpretive news — this includes opinion pieces, op-eds, and analyses (like this one) that provide interpretations/perspectives/opinions on current issues; they are often subjective and require extensive research to back up the perspectives. Feature pieces — these are typically deep dives on topics that might not really be hot news right now but important issues, investigative journalism would fall in this category; these require extensive research and interviews spanning across months, and also creative storytelling. I bring up this categorization because the purpose behind and process involved in producing these two types of articles is very different, and consequently will evolve differently with the use of generative AI. With this in mind, let’s talk about how news makes money. The business of news The business of news is tricky — most news organizations operate on an ad-supported media model, and a small subset have successfully managed to pivot to subscriptions. This has major implications for the type of content a news organization produces. The businesses (NYTimes notably now gets from subscriptions) have a content strategy that’s tied directly to consumer value — focus on to make the subscription worthwhile. For example, of the types of content above, NYT primarily does news coverage (that is well-researched), interpretive news, and feature pieces for their core news offering, backed up by interest-based content like and . Some other publications like the Wall Street Journal and the Washington Post have made strides with subscriptions using a similar approach. subscription-based news ~70% of revenue high-quality content + diverse interest-based content NYT Cooking Wirecutter However, and will continue to be so for the foreseeable future. This means that they are focused on generating high engagement: more eyeballs → more ad inventory → more revenue. The strategy that has been more effective for them is (like news coverage, interpretive news) (like factual/data news, interest-based/informational news). most publications are still ad-supported augmenting high-complexity content with a ton of high volume, low-complexity content This strategy works because high-complexity content helps deliver long-term value to consumers, while the ton of low-complexity content helps . The SEO loop looks something like: high volume of content that gets clicks → search engines think your content is valuable → all of your content gets ranked higher → more eyeballs. get eyeballs as well as win the SEO game This is no criticism of the strategy and the need to play the SEO game is a necessity for ad-supported media. The reality is that — the internet disrupted how content was created and distributed (mostly through Google Search / today), and news organizations have not recovered from the impact of that disruption. There are attempts to fix the imbalance, which are still evolving — Canada/Australia passed laws that force Google/Meta to pay what’s essentially a “disruptor tax” to support good journalism, some organizations like NPR are partially supported by , and some newspapers like the Washington Post are . news media is a terrible business Meta federal funding subsidized by (generally well-meaning) billionaires All this to say — news media/journalism is an absolutely essential public utility for a well-functioning democracy but a not-so-great business. Therefore, the more self-reliant these news businesses can be (without having to depend on regulation or billionaires), the more effective they can be towards achieving their mission. Generative AI can’t solve all of these structural challenges (especially content distribution) but it can definitely make content creation more efficient without necessarily compromising on quality. The anatomy of producing a news article To understand what part of news production will generative AI have the most impact on, it’s helpful to understand the different steps involved in getting a news article published. We can break down the effort into a few sequential components: Research — finding a topic, gathering facts/data, talking to the right people for quotes or perspectives Storytelling — making sense of the research to come up with a compelling storyline Writing — turning the storyline into actual words that make an article, buttoning up the storyline, backing it up with the right data / adding supporting links Editing — improving the writing, coming up with catchier headlines, having better visuals, and fact-checking to meet standards Distribution — publishing, and distributing across channels like social media. Based on some research and also influenced by my writing experience, I estimate the effort of producing an article into something like: . Take the actual numbers with a grain of salt but they are directionally accurate. Research (30%), Storytelling (20%), Writing (20%), Editing (20%), and Distribution (10%) Now, note that — the low-complexity pieces might not need to rigorously go through all steps in the life cycle above but the high-complexity pieces do. not all formats of news articles require all of the steps As an example, this article took me ~8–9 hours to finish (so did most other articles on ), and I would like to think that my articles fall under interpretive news. If I wrote a low-complexity piece, I could probably get it done in ~2 hours. Another data point — says he can typically get a news article done in a couple of hours while a feature piece might take up to 6 months. my Substack newsletter this NYT reporter So, what can generative AI actually help with? It basically comes down to what steps in the news production process can current (and future) products do well. What generative AI can and cannot do well Here’s my take on each step. Spoiler — they are somewhat effective for writing and editing, they can be effective for research if the right products are built, and they will continue to be bad at storytelling. Research — surprisingly low capability Most current generative AI products (like ChatGPT, and Google Bard) have surprisingly low capability when it comes to Research: There are definitely certain capabilities that they are good at. For example, they are good at giving arguments for a particular point of view or providing inspiration for new topics. They are average-to-good at summarizing content and in particular, answering questions based on reading an article. For example, you can ask them to read this article and list out different steps involved in news production. However, they often produce (“hallucinations”), to what they claim is the truth, and have questionably sourced data with potential copyright infringement. See the screenshot below. factually incorrect information do not provide links/sources For a generic use case (say you want to draft an email to a future LinkedIn connection), these issues don’t matter. But when you are writing a news article, not fact-checking can severely impair your brand perception. Though the research tools can be used for directionally useful research (for example, you want to know how much market share Bing and Yahoo search have), writers/journalists still have to do additional work to find a new source to link because current tools neither provide links nor have fully accurate data. for anyone building a research product for writers, and it’s very likely that new companies will emerge here. Using cleanly sourced data + being able to provide answers with trustworthy links is a big opportunity Storytelling — low capability Generative AI’s storytelling capabilities are fairly weak today. Here’s an example: I did a bunch of research for this article, put together the research in an organized format, and asked ChatGPT to give me a storyline. See the screenshot below for results. At first glance, it looks like they “make sense”. But that’s literally all it is — it makes sense at a surface level. In reality, none of these storylines are very compelling and the research notes I provided had much more detail and nuance beyond the surface-level results provided here. You can make the argument that these are still decent, but in the context of writing an article, this was completely useless to me — at best, this was inspiration/ideas, not a storyline based on the research I submitted. If you have a low-complexity story, it will get the job done for you. If you are building a nuanced story, or have the data and need help constructing a story, current products do not get the job done. Given the high amount of subjectivity involved, I am not bullish it will get better and this will continue to be the part where writers can add the most value. Writing — medium capability If you provide current products with a detailed storyline of what you want to say, it can generate a barely decent v1 of the content. The output is still pretty cookie-cutter and it is pretty challenging to instruct the current models to craft the language in a way that it tells a story. See the screenshot for an example output after I fed ChatGPT detailed notes about the storyline for this article. At first glance, you might think it looks okay for a v1 draft. It is not — the tone isn’t right, the story doesn’t flow and it always looks like it’s been written by a bot. It’s very generic and does not articulate the story despite being fed a very specific narrative. If I published that article, you would not read it. And that’s the challenge with the writing capability today — it can work for low-complexity articles but for anything more complex, you essentially have to rewrite the whole draft. — writers don’t want to take terrible cookie-cutter versions and redo the whole thing line by line. What they would like is some form of a user interaction construct that lets a writer feed in a storyline and sequentially craft the article section by section, while giving active feedback to the AI tool. Cherry on top would be being able to customize the writing style by feeding past articles written by the same writer. The big product unlock here would be enabling effective human instruction I think the underlying models today do have the capability to do this and there is , which I believe will happen near-term. innovation needed at the UI layer Editing — medium capability Tools today have a good amount of capability to review articles, find errors and make corrections. These tools are also very good at cosmetic tasks, like coming up with ideas for catchy headlines or section titles that might perform well. However, there is still work needed at the UI layer to make this usable for editing — there are some partial solutions today like which lets you improve language and make sentences shorter / longer from within a Notion page, but it does not capture the full context of the page. ChatGPT does a good job of making edits across a full article but lacks the ability to take instructions to easily edit specific sections and also cannot support links (i.e. I give it a blurb with text that has hyperlinks, I get back text without any links). Notion AI I absolutely hate the editing part of the writing process, and I am sure several writers and journalists do as well — generative AI can most definitely minimize some of this editing grunt work near-term. Distribution — medium capability There are tools coming up here today that for example help you generate social snippets, or identify parts of your story that might go more viral. This will likely continue getting better in the future. Generative AI and the future of News — A tale of two worlds Based on the above analysis, you can see a clear duality emerging: , which require simpler research & storytelling, and can be written & edited easily, will start to be (or heavily AI-assisted). Low-complexity formats more and more AI-generated , which require more complex research and storytelling capabilities that don’t exist today, will but generative AI can bring a good amount of High-complexity formats continue to be primarily created by journalists efficiency by minimizing writing and editing gruntwork. While AI-generated low-complexity content seems bad at first glance, these articles are primarily written for SEO purposes or to augment existing high-quality content, and the race to commoditization had started much before the generative AI wave. For example, the Associated Press has been using since 2014. The upside here — this is not the kind of content journalists want to spend time creating and automating this will free up their time for high-complexity content. bots to publish articles reporting company earnings More and more high-complexity content will emerge. Research and storytelling capabilities with products today are limited, which means that the ability to . This, accelerated by AI writing and editing tools that take the grunt work out of publishing quality content, will be a big boon for journalists. construct a compelling story based on information and story-tell in a unique way will continue to be the biggest currency journalists have What about some of the concerns raised around using AI for news? Some are fair but I believe they are mostly solvable: A wave of junk SEO content — Google has that they won’t penalize AI-generated content and have gotten criticism for opening up a world of junk content, including factually incorrect articles. That’s a somewhat fair critique but I believe Google will crack down on this — not out of the goodness of their hearts but because the crux of a search product is to give users useful results. Google already has penalties for SEO hacking practices (like keyword stuffing, and link farming) and it would be easy to extend this framework to AI content. taken the stance Journalists will lose jobs — Some have brought up the concern that journalists might lose jobs, or be in a less advantageous position (like writers in Hollywood who are currently on strike); the big difference here is that while there are a large number of writers in Hollywood, newsroom employment in the US since 2008, not because we don’t need journalists but because news is a bad business; Generative AI can help fix the economics of the business while continuing to empower journalists do what they love. has fallen 26% Conclusion I am by no means an AI maximalist and I absolutely think there are real risks to AI that need to be addressed as we scale the technology. However, I think it’s important to analyze every AI-impacted market/situation separately and not bundle them into one big AI-impact problem. In the case of news, generative AI can tremendously improve the economics of news businesses. The products are not there today — there is a clear need for AI products that thoughtfully solve writers’ needs without a brute-force, chat-based, language model interface, but I’m confident these will emerge near-term. Low-complexity writing will be more and more AI generated and that’s okay — it can help companies run an efficient SEO machine, while journalists (assisted by AI for grunt work) bring to life a lot more high complexity content that elevates the public discourse. Thank you for reading! Also published here.

Exploring the Impact of Generative AI on the Future of News 

খবরের ভবিষ্যৎতে জেনারেটিভ এআই-এর প্রভাব অন্বেষণ করা

Erkundung der Auswirkungen generativer KI auf die Zukunft der Nachrichten

Explorando el impacto de la IA generativa en el futuro de las noticias

Explorer l'impact de l'IA générative sur l'avenir des actualités

समाचार के भविष्य पर जेनरेटिव एआई के प्रभाव की खोज

ニュースの未来に対する生成 AI の影響を探る

뉴스의 미래에 대한 생성적 AI의 영향 탐구

Explorando o impacto da IA generativa no futuro das notícias

Üretken Yapay Zekanın Haberin Geleceği Üzerindeki Etkisini Keşfetmek

Khám phá tác động của AI sáng tạo đối với tương lai của tin tức

探索生成人工智能对新闻未来的影响

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgment and References Acknowledgment The authors in this project have been funded by UK EPSRC grant “FinTrust: Trust Engineering for the Financial Industry” under grant number EP/R033595/1, and UK EPSRC grant “AGENCY: Assuring Citizen Agency in a World with Complex Online Harms” under grant EP/W032481/1 and PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1. References [1] Philip Adler, Casey Falk, Sorelle A Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. 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Privacy-preserving Computation of Fairness for ML Systems: Acknowledgement & References

2020 has been a bad year for the financial markets, be it stocks, forex or properties, made worse by the COVID-19 pandemic. Does it mean that cryptocurrencies and blockchain projects are dead? Not at all! new projects with great potential continue to emerge and for new investors there are still ground floor opportunities that may offer good ROIs once the markets recover from the current financial crisis. Below are 10 new tokens we think are doing reasonably well and will ride through the current crisis in due time. We used a few criterias to select them, such as these projects must have groundbreaking technologies, provide solutions to real world problems, listed on CMC or CoinGecko, showing decent trade volume and respectable ratings by credible crypto info sites. Overall, these tokens have displayed steady performance during this bearish market period. Level01.io is an innovative decentralized finance platform, licensed by European regulators from Estonia. It has the backing of well known financial services providers such as Thomson Reuters/Refinitiv and DigiFinex. Level01 What’s so unique about it? It combines many technologies: artificial intelligence, multi markets trading and blockchain into a single platform and app  to  provide novice and seasoned traders the fairest and safest  derivatives exchange on the market. While many blockchain trading platforms concentrate solely on crypto trading, Level01 allows  you to trade derivatives  on many assets: Forex,, Gold, Oil, Bitcoin, Ethereum and etc. To decrease risks associated with these assets and speculative nature of derivatives trading, Level01 has its own AI algorithm, called Fairsense that estimates the real risk/reward ratio, using trusted data from Thomson Reuters, which helps users to be well-informed before making any trade decisions. Level01 is a P2P platform, which means it matches  contracts between users directly,  without any middlemen or brokers in the process. That’s why it has low fees and fast trade execution, while eradicating issues associated with traditional brokerages such as hidden fees, price manipulation and prolong settlement at the same time. Level01’s native token LVX has performed greatly against the current global pandemic and economic crisis, with it’s prices remaining steady and even climbing slightly compared to others crashing to over 35% including Bitcoin at this time of writing. Exchange tokens are constantly in high demand as more people move into crypto or digital currencies.  For example, the prices of Binance and Bitfinex tokens have gone up by hundreds of percent.   Options or derivatives trading have much higher volume and liquidity than the crypto market. Add to the fact that many people are forced to stay home due to the global pandemic, options trading will definitely pick up more interest and traders. The token of a DeFi platform that combines cutting edge technologies and multi markets is  definitely one of the most interesting pick of this year. ATOM ATOM is a token of , a long-awaited blockchain, which allows to create child blockchains with rules, defined by users. It consists of two parts: Tendermint Core and Inter-Blockchain Communications. Tendermint is the layer for blockchain development, a PoS system, that allows to create tokens, decentralized applications and child chains. Inter-Blockchain communications allows to create Hubs, that connect various child blockchains and even external blockchains into one zone with a common architecture. Cosmos is very fast and even has its own Ethereum implementation, called Ethermint, which allows to run smart contracts written in Solidity. Cosmos ATOM token is actively traded at many exchanges, has a daily volume of $277,880,624 USD, and has many partnerships, including Binance, which made a fork of Cosmos to create its own Binance Chain. ALGO This project, , has a simple goal to be as user-friendly as possible, staying fast and secure at the same time. Algorand is a smart contract platform, similar to Ethereum or EOS, but it’s based on pure PoS consensus model. It has the pure finality, meaning that once a transaction is processed, it can’t be reversed. The platform allows to stake ALGO tokens, acting as a validator node, and receiving staking rewards in return. Algorand ALGO has a daily volume of $141,785,689 USD and a large lists of partners - such as Bixin Capital, Continue Capital, Brainchild and Eterna. That’s a solid list, and definitely, ALGO is going to have a successful future. RSR It’s a token of the project, which aims to create a stablecoin cryptocurrency, RSV. The project has three phases: Reserve A centralized phase. RSV is pegged 1:1 to the US dollar. In this phase, dollars are simply held as the collateral on the bank accounts. The decentralized phase - RSV will be backed by a decentralized basket of assets, but still pegged to the US dollar. It will be unpegged from the US dollar. its price will be determined based on the balance of supply and demand. RSR token is used to create new RSV tokens. When the price of RSV on the market is too high, arbitrageurs can send RSR to Reserve smart contract and receive new RSV tokens, to sell them and lower the price. The whole ecosystem is very similar to the MakerDAO. Currently, there’s also another token, RSD, a dollar stablecoin, as a temporary solution until the backing mechanism is developed. The Reserve ecosystem is still in the early stage, but when finished, it has the big potential, because the market is always in need for more stablecoins. Now the daily trading volume of RSR isn’t very high yet - $6,078,110 per day. BEAM is an open-source implementation of MimbleWimble, a protocol with enhanced privacy and high speed. It has no addresses and data storage is minimal, thus its miners don’t have to store Gigabytes of blockchain data. All of its transactions are confidential, and it features many types of transactions: escrow, atomic swapping and time-locked transactions. Despite being private, the Beam wallet can be configured to share its transactions for external audit - this allows Beam to be legally compliant. Also it supports the creation of various additional assets, such as real estate tokens, corporate debts and simply new currencies. Beam The current daily volume of the BEAM token, according to CMC is $32,577,799. It’s an actively traded currency, and its development is supported by a good dozen of funds, including Node Capital, Collider Ventures and many others. With the demand for both privacy and legal compliance, Beam might meet a solid demand in the next years - that’s why Beam made it to our list. Hedera Hashgraph was promoted as “an alternative to the blockchain of the next generation”. It’s very fast, more than 10,000 transactions per second, and it’s based on hashgraph, which is another variation of distributed ledger technology. Hedera Hashgraph was backed by large corporations, such as Google, IBM, Boeing, Deutsche Telekom and Nomura, right from the start. Hashgraph technology doesn’t need miners, it’s similar to IOTA, because it’s also an another variation of directed acyclic graphs - that’s why it’s so fast. Aside from the cryptocurrency tokens, Hedera also offers smart contracts and file services. While its smart contracts concept is the same as on many other platforms (it supports Solidity language), the file service is worth explaining: it allows to create time stamps and link hashes of external documents to the public ledger, thus verifying them. Hedera Hashgraph HBAR, the native token of Hedera Hashgraph, is traded on many exchanges, including Bittrex, with a daily volume of $14,157,877. It definitely won’t be an Ethereum killer, but with such giants as Google on board of its council, it has the potential to be very high in the crypto ranks. Matic isn’t a separate blockchain, it’s a scalability solution for Ethereum, a second layer on top of the main network. It claims to be an implementation of Plasma, the solution similar to Lightning Network of Bitcoin, but in reality it’s not a true Plasma, as it’s not a child chain, but rather a side chain. Thus it doesn’t rely in its security on the Ethereum network. However, Matic is a well-known blockchain project. It has a working implementation, it has a PoS system, and it’s gonna be great for using in games for storing and transferring in-games items in form of tokens. In theory, it can achieve the speed of 65,000 transactions per second. The native token of Matic is traded with a daily volume of $34,757,963. Matic TOMO is a highly-scalable blockchain that supports all Ethereum smart contracts and runs on PoS implementation with 150 nodes. Its main advantage is nearly zero-fee transactional system and instant confirmation time. Any user can run a masternode by having more than 50,000 of TOMO coins, or simply use them to vote for other masternodes and receive a share of its profits. It also has a large number of partners and investors, including HyperChain Capital, OneBlock Capital, Blockchain Investment Club and many others. Tomo has its own wallet, but voting and staking can be done via MetaMask or MEW. Its current trading daily volume is $36,758,886. TomoChain BTT All of us know this one. is the native token of BitTorrent, a popular P2P network, now owned by Tron Foundation. BTT token runs on the Tron blockchain, allowing content creators to be rewarded and spend their tokens without a middleman. Also it gives access to BitTorrent Speed, the service with a larger bandwidth, which means that by paying BTT you’ll be able to download everything faster. It’s interesting, considering that BitTorrent currently has more than 100 million users and has 22% upstream traffic of all Internet. Tron Foundation and its leader, Justin Sun, are known for great marketing of their products, so we can expect BTT to rise in crypto ranks in the future. Its daily trading volume is very high - $86,971,887. BTT ABBC Coin The last token in our list is the . They claim to be the future of the payment security. ABBC project creates a marketplace, that can host many merchants, and provides them an ecosystem for seamless connection with customers. It works on a DPoS consensus, allowing the network to handle as many customers as needed. It has some serious partnerships with the Rockefeller Foundation, Overstock, tZero, Alexander Capitals and many others - meaning that it’s a serious project and we have to watch it closely in the future - it may not be the sure bet, but at least it’s on the right track. Its daily trading volume is $32,389,797. ABBC Coin

Top 10 New Tokens To Watch In 2020

What Will be the Major Factor of User Adoption of Blockchain Technology in 2020?

It has become common knowledge that blockchain has immense disruptive power that allows it to push a multifaceted narrative that seems to encapsulate the entirety of the global market, especially the financial markets. While this is a given, very few startups have successfully implemented blockchain solutions targeted at a large demographic and are poised to take on a financial sector that is traditionally known to thrive on intermediate services. Though that does not readily apply to as  the platform is slowly establishing itself as a force to be reckoned with in the derivative exchange sector. Level01 In this article, I will explore some of the features that set Level01 apart as well as the probability of achieving the company’s vision. What is Level01? Simply put, Level01 is a decentralized exchange that has combined blockchain and AI technology to facilitate trades, in the process eliminating the need for middlemen. It is worth noting that a decentralized exchange is not a new concept, as several startups are currently tweaking different variations of solutions with the intent of establishing the autonomy of traders. However, LevelO1 sets itself apart from other entries in the market via its decision to cater to a trading niche that few blockchain-based platforms are keen on tackling. Level01 provides a trustless and transparent ecosystem for trading derivative contracts as opposed to the conventional crypto-trading niche that decentralized exchanges predominantly adopt. What Are the Features of Level01? 1. A P2P Matching and Trading System One of the core features of the Level01 platform is its proprietary hybrid trading engine that matches trades on cloud servers and uses smart contracts to process trade settlement. In other words, there are no elements of middlemen or brokers, which translates to lower fees and instant trade execution. Also, with the incorporation of an automatic trade matching system and smart contracts, it becomes possible to eradicate human errors, disputes, as well as unnecessarily long settlement processes. 2. An Intuitive System to Fast-Track the Process of Creating Trades Given Level01 is a decentralized, P2P network, there is, therefore, no need for the stifling registration and trade creation processes associated with traditional derivative exchanges. For Level01, the key to creating a decentralized ecosystem is to unburden traders from all of the requirements that may reduce their autonomy or subject them to the authority of an unknown entity. 3. An Infrastructure That Tokenizes Funds Asset tokenization is utilized within the Level01 project as its P2P network capitalizes on the innovative features of blockchain technology to securely tokenize funds entered into trades. As such, the tokenized assets are registered on an immutable ledger and protected with cryptography to eliminate the chances of data manipulation as well as theft. The incorporation of these measures is a testament to Level01’s commitment to implement a transparent business model for its derivatives exchange. 4. Multi-Markets Trading Options While other crypto exchanges box themselves into the crypto trading sector, Level01, on the other hand, has adopted a framework that allows it to implement a new narrative of blockchain’s well-documented innovative edge. In other words, the Level01 platform has diversified its offerings and provided trading services to mainstream traders who are looking to distribute risks across markets with varying volatility, and at the same time, capitalize on blockchain’s unique capabilities. Here, traders can trade derivative contracts of commodities, digital assets, and forex. By doing so, users get to switch between markets effortlessly and eliminate the risks associated with concentrating one’s fund in a single asset class. 5. FairSense AI Risk & Reward Projection In a show of technical prowess, the Level01 team has built a trademarked AI system – the FairSense Pricing Algorithm – that would optimize the trading experience offered to users. With this technology, traders can access estimated true market values of options and derivative contracts, which gives them a fairer chance at earning higher returns consistently. Also, the pricing algorithm utilizes premium-sourced data, relating to market trends, to track as well as provide dynamic, fair, and real-time prices to users. Needless to say, Level01 is one of those projects that have found a balance between two of the most coveted technologies – that is, AI and Blockchain. 6. Seamless Integration System with Thomson Reuters Level01 has partnered with Thomson Reuters utilizing the company as an independent data source for its Artificial intelligence deep learning algorithm. According to Jonathan Loi, the CEO of Level O1, the startup had intentionally opted for the renowned data and news propagator because it incorporates similar trust principles that fuel Level01. Jonathan loi : stated “Thomson Reuters is a respected industry leader in providing intelligent information and data, with trust principles aligned with our vision. Our collaboration with Thomson Reuters ensures we have the necessary quality data for Level 01 algorithms to perform in the most efficient manner,” Final Thoughts Having explored the core features of the Level01 trading platform, it is safe to say that the team backing the project has dared to tread a unique path to provide services to traders who understand the place of technology in a competitive landscape. The startup has successfully surpassed the numerous challenges to bring its platform to fruition, and steadily attracting conventional traders to its fairer more beneficial offering.

Overview of the Innovative Features Brought to Financial Markets by Level01

New entrants into the banking industry have given bank CEO’s pause and have put their CIO’s under pressure to come up with relevant digital strategies to defend their business from customer leakage to new products and services offered by emerging ‘ FinTech ’ companies. Who are these FinTech companies? Most FinTech companies fall into the following categories Lending Personal finance management Payments technology Bitcoin Companies such as , , , , , , , , are good examples those that have taken advantage of areas of weakness in banks digital strategy. Lending Club Prosper SoFi Zopa Square, Stripe Betterment Wealthfront Personal Capital, eToro Faircent Although the threat of losing customers is real in the long term, in the short term the consumer is keeping their core relationship with their bank and using the services of the FinTech new entrant for value added products and services. Why are the large retail banks keeping their core banking systems and why are new FinTech companies not replacing them? The answer is complex and revolves around cost, regulation and reputation. The challenge of updating legacy core banking systems is onerous and, in some cases, can introduce systematic risk if . Banks have resisted upgrading their core even as industry pundits have been heralding the upcoming upgrades. not well managed banking systems for many years It is very expensive to create a new core deposit account system with all of the associated features. Replacing a core banking system from scratch will cost in excess of $100mil for standard functionality. Cost: Since core banking is the lifeblood of the retail economy government has created decades of regulations overseeing every aspect of activities that flow through the core banking systems. These legacy systems have these complex regulatory solutions built in. Regulation: Although banks are not popular, people still hold animus for the financial crisis, most people still trust that the existing banks will process their transactions and are very cautious about moving their relationship to untested new entrants. Reputation: Will Banks Digital Strategy move quickly enough ? A look at some of the findings of shows that CEO’s are becoming more dependent on data, analytics and technology that delivers value to a more demanding customer base. Unless Banks invest more in technology and innovation they will loose business to FinTech companies that are not constrained by legacy systems and approaches. PWC’s Global survey of financial services The future of banking is still being debated and although many have theorized that traditional banking will eventually be eliminated by new FinTech solutions is not yet clear that will be the case. In the mean time banks are automating more of their manual processes using workflow management tools and new technologies to speed up decision and approval of loans. Banks have had to change their thinking such as using and to acquire new customers and defend against new entrants like . They are also looking at every competitive new entrant with a view to either integrate or with them. social media mobile Apple Pay partner FinTechs have momentum in building a base of customers, but traditional banks can compete effectively if they’re willing to put the customer’s priorities at the center of their technology strategy. Some banks are removing errors and from their branches and call centers and driving their customers to more meaningful self-service channels. But they still need to evaluate ‘what do my customers want online’ vs ‘what am I offering my customers online’ to out compete their FinTech challengers. The real test of how well banks compete with FinTechs is how they can service their existing customers with innovation and new product offerings. especially among young adults manual processes The worst-case scenario is that banks will become relegated to being ‘processors’ or the ‘back end systems’ for these new FinTech companies. Evolution or Revolution? Banks have no choice but to move quickly to focus their technology budgets on the most effective . Customers’ rising expectations for is setting up a competition between FinTech and traditional banks for consumer relationships. These relationships will be true cross product and deeper than existing banking relationships when . customer engagement technologies anytime, anywhere banking technology converges US Banks have been slowly embracing digital solutions with European Banks leading more innovation and coming in a distant third. There are a number of reasons each market is moving at different speeds, legacy system complexity, management intransigence, cost and regulation being leading influencing factors. Culture is also playing a large role in the adoption of new technologies in each market and this will also play out as new services become more commonplace like the in Africa. Asia M-pesa system . Revolution occurs in any system, either financial or political, when the environment it depends on for survival changes at a faster rate than it can evolve In the end I don’t think there will be a ‘revolution’ in banking because the combination of regulation and cost will give Banks the time they need to ‘evolve’ through innovation and absorb the new technologies they need by buying targeted FinTech players to service their ever changing customer needs. Thanks for reading Best regards: Norman King If you enjoyed this article, please hit the like button; leave a comment or share with your network. Also, please check out my other LinkedIn posts on Medium here, also here. Available for consulting, advisory and speaking engagements. email: normanking@brook-port.com Reference: Images are from Pixabay.com,

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