Best Crypto and Blockchain Marketing agencies

Crypto Exchange is a high-tech platform in which all trade transactions are conducted using modern software created based on the latest IT solutions. The emergence of new types of currencies, in particular, cryptocurrencies, gives a chance for the rapid development of the world economy as a whole. In turn, structural changes in the international economic system gave impetus to the emergence and development of new types of exchange technologies.

Thus, crypto exchanges appeared which allowed its participants anywhere in the world to buy, sell and exchange one cryptocurrency for others, or for the fiat of other countries. Each crypto exchange tries to offer customers convenient ways to convert financial instruments and provides the ability to conduct transactions on its own terms. The high rates of development and distribution of cryptocurrencies, which are based on Blockchain, as well as the gradual wide recognition by the world community and leading economists, ensure the further improvement of exchange technologies. This means that in an effort to provide the most comfortable conditions for its customers, each crypto exchange will take them to an ever-higher quality level of service with innovative nuances. But at the same time, within the framework of the technological process of stock trading, which is available to users (from professional traders to amateurs), the question of psychology and its role in the decision making has not been canceled. Successful trading depends on 70% primarily on the psychology of a trader and only 30% on the trading scheme/strategy.

Trading on the exchange, it is necessary to develop discipline, self-control and be able to respond quickly to changing stock charts. All of this will allow you to earn and minimize your losses more effectively. Everyone should remember, from the amateur to the professional, that in the financial markets you can not only earn money but also lose money. Cryptocurrency rates are still subject to political and regulatory influences; their value is influenced by the reputation of the company’s founders, informational insertions about blockchain projects and plans for their further development, scandals, and disclosures. Nevertheless, there are simple rules for successful trading from the field of psychology, which will reduce the risks when trying to make money on cryptocurrency and not only.

There are a number of problems that always hinder every beginner — amateur:
• Excitement
• Fear
• Greed
• Unwillingness to learn new things
• Imaginary visualization of results

All these problems have psychological aspects. Emotions, feelings, and desires significantly influence the trading decisions made by the trader. This happens all the time, not only on traditional exchanges but also in the cryptocurrency sphere as well. Excitement is an emotional state when it seems to a person that he is lucky, and as the series of successful transactions continues, he performs larger by volume financial transactions. Often, the excitement motivates to turn away from long-term transactions and trends and look towards short-term operations. After all, it seems that the more often you successfully complete operations, the more capital you earn. Not at all! The more often you make mistakes, leading to a default on your account. Money only is earned on long-term trends and operations. Traders are often worried, fearing an unsuccessful deal closing.

Of course, a loss is bad, but sometimes it is better to close a position in minus than to lose a large amount only because of the hope of a quick price reversal.

Therefore, fear often pushes for the wrong strategic decisions. Fear of loss, as a result, becomes a sentence for your positioning in profit. On the same face with fear, if not strange, is the factor of greed. Having essentially a different source of inspiration, greed, like fear, leads to a generally pitiable result — to the default of your trading account. The reluctance to learn new strategies, technologies and denial of forecasting also leads to failure. Successful is who always strives to learn new things, and perceives the fact and necessity of continuous learning. Since learning is a process of striving for the progress of its results and professional qualities. Another scourge — Wishlist or visualization. Everyone wants to see the price move in the right direction. This is pretty dangerous. By visualizing the price jump in the right direction, you can dream and invest too much in cryptocurrency. This will lead to losses.

Here you should always remember to diversify your investments. Remember your psychological portrait even when you program your trading strategies, algorithms, and bots. After all, your algorithm is essentially your psychological portrait. Finally, the above-mentioned flaws, especially in the strategy can dominate and damage your deposit and reputation. The main signs of competent crypto-trade are the same as on other exchanges (such as FOREX). This is a kind of algorithm for a sustainable profit strategy:
• Risk no more than 10% of the deposit
• Use risk per trade of 5% or less
• Do not close profitable deals too early
• Do not accumulate losing trades
• Fix quick speculative profit
• Respect the trend
• Pay more attention to liquid assets (cryptocurrency)
• Set your personal entry and exit rules for trades and stick to them
• A long-term trading strategy gives you maximum steady profits
• Do not use the principles of Martingale tactics if there is no experience.

You cannot double the volume of the transaction if it closed in the red zone. If a loss was incurred, then the cryptocurrency market situation was predicted incorrectly and it was necessary to work on improving the analytical skills, and not to conclude a larger deal, which probably also closes in the negative

It is obvious that the psychology of trading significantly affects the performance of stock speculation both in the traditional market and in the field of cryptocurrency. It is important to remember that the success of a person in any field of activity depends on the emotional component, namely the internal balance. Exchange trading is a nervous activity, and if you do not learn to take emotions under control, the results can be disastrous. The basis for achieving success in stock trading, in my opinion, are two fundamental factors. The first factor relates to the field of formulation of the trading idea, and the second — to the area of its implementation.

To formulate a trading idea, on the one hand, methods of technical and fundamental analysis are used to select an exchange instrument and determine the moment of opening and closing a position on it. On the other hand, capital management methods are used to determine the optimal size of the position being opened. As you know, without these two crucial moments it is impossible to achieve stable success in stock trading. As experience shows, for the most part, people have enough intelligence to master all the necessary theoretical knowledge of technical and fundamental analysis in a few months of intensive training.

There are no special intellectual difficulties. But, as the same experience shows, this is clearly not enough for successful exchange trading, since all knowledge may turn out to be a useless load if the second success factor is not sufficiently present — the practical implementation of trading ideas, which is no longer based on the intellectual sphere, and psycho-emotional. It is within this area that the main problem arises for many traders, which prevents the receipt of stable profits. As a rule, this is due to the psycho-emotional profile of a person. It depends on how the trader will behave in the psychologically stressful situations that the exchange trading is full of. Inherent in all human emotions and feelings — fear, greed, excitement, envy, hope, etc. very often have a decisive influence on the behavior of traders, not allowing them to follow strictly the trading strategy and plan, even if they have one. From a psychological point of view, the process of stock exchange activity can be divided into stages, after which the trader can return to the starting point. The above scenarios and risk factors are one of the options for the behavior of an exchange speculator; however, it often happens exactly the opposite. Having suffered losses from his first transactions in the market, the trader loses interest in exchange trading, he gives up and he falls into despair. In this case, the first step to victory is the admission of defeat. It would seem silly and ridiculous, but it works. After that, there are two options: either the trader leaves the exchange forever, or returns to the battlefield.

Such “returns” may occur more than once. In addition, at some other time, after repeated analysis of his actions, mistakes made and their consequences, a person from a beginner begins to turn into an experienced trader, which is marked by the stability of his activity and, perhaps, by slow, but surely growth of his deposit and profit. The psychological basis for success in trading, which leads to victory and the absence of which is equivalent to defeat, are as follows:
• It is not only the lack of self-control, discipline and focuses on the process that causes the defeat
• Self-control, discipline, and ability to concentrate is not enough to achieve success
• To achieve success, it is equally important to be able to adapt to changes.

In principle, one can consider the idea that traditional approaches to the psychology of trading are limited. In the majority of benefits for traders, the key qualities necessary for successful exchange trading are only self-control and discipline. Of course, these qualities are necessary for any field of business activities. Trading is not an exception, especially considering that it is in the risk zone. But self-control and discipline are not enough to achieve success. Trading is a business. Moreover, any business does not stand still. You cannot find a formula for success and use it forever. You will need to monitor trends and constantly look for new successful solutions.

The main feature of a successful trader is adaptability to changes. The lack of development leads to defeat, large monetary losses. Many technology companies continued to produce stationary computers when laptops became popular. The same companies continued to produce laptops when tablets appeared and became popular. The products of these companies were of high quality, and their employees organized pre-set tasks in an organized manner. But they lost large sums due to the fact that they could not adapt to changes in demand. If we draw a parallel with the sphere of investment, the similarities will become noticeable. The stock market, like any other subject to change. One period is replaced by another. Those methods that allowed achieving success in the previous period can lead to failure in the current. The key concept in stock trading is volatility. The change in this indicates the onset of a new period. When volatility increases, the trade becomes riskier. Accordingly, with a decrease in this indicator, the degree of risk during trading operations decreases. With a high level of volatility, trends most often unfold. Strong and weak positions can be swapped out. With a high level of volatility, trends continue for some time. From the foregoing, it should be concluded that market processes and methods during periods of high and low volatility differ strongly. You cannot use the same methods during changing market trends. Often it is the adherence to the previous methods, excessive discipline leads to collapse as well. The fact that the investor was defeated does not mean that he suddenly became morally unstable, unorganized. Trading is trading.

Therefore, we have every right to assert that under the psychology of trade in the markets is meant human preparedness for the risks that inevitably accompany any activity. Trading on the stock exchange is based on the interaction of the three most important components: capital management, analysis, and the psychology of trading (which cannot be considered in conjunction with the other aspects of trading). The psychology of human behavior is a source for understanding what is happening in financial markets. The source for understanding the events occurring in the financial markets and the behavior of traders during exchange trading is the psychology of the human person. Emotions — greed, fear, doubt, hope, a sense of self-preservation — are peculiar to any person in life — are clearly manifested in the hard rhythm of decision-making during the dynamic course of exchange trading (which was partially considered above). Knowledge of human psychology and their behavioral characteristics must be used to achieve success. The psychology of a trader is formed from a multitude of grains — it is a belief in what one does in the stock market, in one’s actions, in own system of one’s decisions, in the trading method. In addition, the psychology of a trader is that one can unload oneself emotionally, one does not accept the intellectual challenge that the stock market carries. On the contrary, becomes restrained, calm when making decisions on operations in the stock market. There are many situations where a trader expresses his attention and focus; he does not disperse it on the tracking of news factors or on the receipt of stimuli from the news agencies. Consequently, the crowd psychology is the factor that makes prices move, therefore, in addition to assessing one’s own psychological state, one must be sensitive to changes in the mood of other market participants, move in the flow, not against it, and then success will not take long.

Of course, you can argue that why do I need this psychology? After all, besides creating your own strategies and individual work, some exchanges (including crypto exchanges) allow minimizing risks by following the strategies of experienced traders; this service is called a PAMM account. PAMM provides an opportunity for clients (Subscribers) to follow the trading strategy of experienced and professional traders (Providers). Provider’s trading results are publicly available. With the help of the rating of accounts, graphs of profitability and reviews of other traders, you can choose the most suitable Provider and begin to follow his strategy. Again, in this case, the provider is a human with all the ensuing consequences. And psychological aspects are not foreign to professionals as well, including victories and mistakes. The financial market attracts people the possibility of obtaining independence, including financial. A successful trader can live and work in any country in the world without having either a boss or subordinates. The motivation of people on the exchanges can be different: from getting a higher percentage than from a bank to making several thousand dollars a day. At the same time, there are two main categories of people in the financial market (including cryptocurrencies): investors who acquire assets or currency for a relatively long period, and speculators who profit from changes in the prices of certain assets for short periods. Many believe an easy way to make money is not for everybody. First, the skillful use and manipulation of the psychological aspects of a human make it possible to become a speculator. And this, of course, in addition to knowledge and analytical skills. Experience shows that successful speculation is the right state of mind. It would seem that this is the simplest thing that can be acquired by a human. But in fact, this self-tuning is available to very few. It is also necessary to distinguish the psychology of the market and the personal psychology of the trader. The behavior of the market as a whole depends on people since it is the stock market crowd that determines its direction.

However, quite often traders lose sight of the most important component of victory — managing their personal emotions, that is, their psychology. Without control over oneself, there can be no control over one’s trading capital. If a trader is not tuned to the trend range of the stock crowd, if he does not pay attention to changes in her psychology, then he will also not achieve significant success in trading. To succeed in the exchange, one needs to take a sober look at exchange trading, recognize its trends and their changes, and not waste time on dreams or lamenting about failures.
Any price of a financial instrument is a momentary agreement on its value, reached by a market crowd and expressed in the fact of a transaction, i.e. it is the equilibrium point between the players for a rise and a fall, or the “equilibrium” price. Crowds of traders create asset prices: buyers, sellers, and fluctuating market watchers. Charts of prices and trading volumes reflect the psychology of the exchange. In addition, this is always worth remembering! After all, the main purpose of the presence of the analysis of psychology in stock trading is not the quantity, but the quality of transactions. A person striving to become a good trader needs to remember the words of DiNapoli, a well-known stock exchange trader: “The most important trading tool is not a computer, not a service for supplying information, or even methods developed by a trader. It is he himself! If a trader is not suitable for this — he should not trade at all”! Therefore, before pushing orders on the trading platform, think about whether you are suitable for this role.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet marketing Solution, EU structural funds, ICO projects, NGO & investment projects, project management, comprehensive support of business

Andreessen Horowitz: ‘’Software Is Eating the World’’

Overture

Recently, due to the growing interest in blockchain and fundraising by ICO/IEO/STO, many projects are ready to sell services or access to software using the blockchain or attracting crypto assets as an alternative investment tool. Founders and project teams in their WPs are trying to tell the public and investors about the bright future of their revolutionary ideas or what perspectives in the project can provide. For most cases, the bright future of many projects is described using the ‘’ marketing fairy tale’’ instrument, beautifully brought you what you wanted to hear. Inexperienced investors seem to believe this, but investors are experienced, ready to operate with larger amounts than $10, such tricks less and less willing to consume, because the crypto hype is gone, now only business remains. That is just such investors, more and more thoroughly trying to find answers in the fabulous WP related to how the business model will work in the project, in its tokenomics, and whether there is a sense when calculating different business development options, to invest in a project? Unfortunately, not all project teams understand that capitalists began to want from them, and they do not understand those who respect their money. The eyes wide shut of the team of such a project is the first sign that they did not think yet about it or worse, the team did not understand why everyone wants something from such an “innovative” project, there is no person in a team who is ready at least to show investors such model in private. Not just to justify the importance and need for their product in the market, but to prove the importance of the product in the market for the investors firstly, but above all for the team itself.

Indeed, so far, such project teams think that they have already earned by raising the desired amount; However, they do not realize that to earn money — this is when the product of the project, after a period of risky payback, with fixed costs and risk control (at the initial stages, expenses are covered by the collected budget to the project), is already making a profit, which also should not be treated as a win in a casino, but as a fact that your product or digital service is in demand on the market. Be sincere, we are still silent on how to treat such profit and how to distribute it. That is another story. Moreover, we have already talked about the problem of working with the budget itself(https://medium.com/coinmonks/the-budget-of-the-ico-project-or-the-secrets-of-the-madrid-court-dd8ee48bc7d2) But in this article, we are considering the next step — when a part (at least) of the budget is already spent on creating a product/service (just more than MVP), and you need to think about how your innovative product will be sold. To make forecasts for its economic development, including the most pessimistic outcome of development. The goal is to prove to a serious investor, and to yourself — that profit is possible and that you are ready and understand how to structure it. And here, we just stress out everything not to tokenomics — as to the system of the pricing policy of your tokens (although many recognize tokenomics as fantasy), but rather to the second part of it, to my opinion, it’s very important and interrelated with Tokenomics — to a financially sustainable business model. Every company needs a business model that ought to be logical and scalable. You need to predict and know how your company’s revenue cash flows work, and how these revenues can increase/decrease over time. And if you cannot explain all this to investors, they will not pay attention to your project. In my opinion, for the majority of blockchain projects, SaaS can become such a model, both from the practical and economic side.

What Is SaaS In Practice?

The SaaS model (Software as a Service) is a system for selling a software product, where user access is provided via the Internet. That is, instead of buying and installing software on your computer locally, the service is available through the www or, as they say, from the cloud (yes, at least from the darknet). The user of the SaaS system, who gets access to the application, as if rents it, paying a certain amount for a period of time. Due to this, such a solution is cost-effective. Thus, an economic effect is achieved, which is considered one of the main advantages of SaaS. But its main advantage is that the user does not need to deal with the technical side of the issue: installation, support, updating, compatibility, and other issues — that is, one can only use the necessary functionality for their business purposes. SaaS provider cares about the technical health of the application, provides technical support to users, and independently installs updates.

The Main Differences Of SaaS From Standard Software:

· There is no need to purchase a license to use the product: instead, its rent is paid for a certain time. This can be a monthly payment or payment for the amount of data. In this service (support and upgrade the system) is already included in the price.

· Several clients can use one service simultaneously. They can access it from different operating systems and browsers remotely from anywhere in the world where there is an Internet connection.

· If the service is not satisfied with something or the need to use it is gone, you can simply not extend the payment for the service.

Services based on the SaaS model today are quite a lot, and they all have a client. However, alongside with all their benefits, they have certain disadvantages.

SaaS benefits:

· There is no need to install software on each computer — this is the main advantage of the model, as mentioned above.

· Reducing the financial costs of purchasing a software product and its subsequent support.

· From the side of the developer, such a model allows you to deal with the problem of piracy — the distribution of unlicensed copies of a software product since the final program does not fall into the hands of the user in finished form.

· Such systems, as a rule, are cross-platform and cross-browser, that is, they do not require a technically specific operating system or browser to work with the application.

· Using SaaS allows the employee not to become attached to a workplace or computer: access to the application can be carried out from anywhere in the world

Disadvantages Of SaaS:

· Your commercial data using the SaaS system will be transferred to a third-party provider, which is not always safe.

· Low system speed, which depends on the speed of the Internet

· Due to interruptions in Internet access, there is downtime at work, which is very unreliable from an employer’s point of view.

· However, all these problems are already fading away, since modern technologies allow for stable and fast access to the Internet, and data encryption technologies allow for reliable exchange of commercial information via the Internet. That is why SaaS systems are becoming more common.

The main part of SaaS users is a small and medium enterprise, for which the purchase of a ready-made software and its subsequent support are quite expensive, therefore its rent is more profitable. In addition, such a system will be beneficial for companies with a wide network of offices or branches, between which there should be a constant data exchange, so even large companies may be interested in SaaS technology. Here are a couple of examples of SaaS that you could already use or, for sure, have known for a long time:

· Corporate mail on Gmail, Yandex or other clients is perhaps the most massive and simple example of SaaS technology.

· CRM and ERP — systems for project and resource management.

· Online document management systems (same google docs), organizers, calendars are all also examples of SaaS, although many are free.

· Website hosting services are also a prime example of SaaS.

· It can also include online games as services built on the same model, although they are not usually classified as SaaS.

· You can also add marketplaces that have recently gained widespread popularity due to the development of blockchain projects and cryptocurrency fundraising with the help of ICO

In addition, there are numerous industry solutions, even in the development and promotion of sites: site designers, automatic site promotion systems, eternal and rental links exchanges and much more. As is clear from the above, SaaS as a practical side of the business model of a ‘’service — client’’ is not perfect, so some alternative solutions appeared based on its model but have its own modifications, which can also be claimed by the blockchain sphere:

· Cloud platforms. If you do not want to give your commercial data to a third-party provider, then it will be beneficial for you to rent not the application, but the computer capacity on which to install the purchased software.

· Application Hosting. This model differs from SaaS in the server-side architecture, so for the average user, the difference will not be visible. Its essence is that the hosting provider installs a separate copy of the application for each client instead of serving multiple users at the same time. This process is more difficult to administer and perform software updates, so this service is more expensive

· S + S is a model from Microsoft that suggests using a software client for accessing the service, not a browser

Differences SaaS model from the model of practical use of conventional software:

· The application is initially adapted for remote use

· One software is used by multiple clients

· Payment for software is either monthly in the form of a monthly fee or based on data volume

· Software support is already included in the fee

· Software update is transparent to users

· If the need to use the software is temporarily gone, then the client will be able to easily stop its use and payments to the developer or application provider

Like any phenomenon, the SaaS model has both positive and negative sides. Positive factors:

· No need to install software on employees’ workplaces, since it is accessed via the Internet, that is, a regular browser

· Reducing the cash costs of deploying software or a system in a company — these are also the cost of rent per visit, remuneration of employees, and so on

· Reduced maintenance costs and system upgrade costs

Negative factors:

· The need to transfer commercial data to a third-party service provider

· Not very high-speed system or applications

· Possible interruptions to the Internet, and, as a result, interruptions in work.

The rapidly evolving IT, the development of encryption technologies, and the consolidating SaaS image dispel these mentioned above fears. In recent years, the SaaS business model has been actively developing in the IT market. In some ways, this scheme can be compared to rent — with the only difference that, within the SaaS model, all the physical servers and components of the program remain on the vendor’s territory (that is, the supplier). What is it for? The fact is that the traditional software purchase scheme has significant drawbacks. First, the company has to withdraw significant financial resources from the company’s turnover. Secondly, the customer most often uses only part of the functionality, and you have to pay immediately for the entire package. Entrepreneurs are accustomed to treating this as an inevitable evil: without modern software, it will still not be possible to fully optimize the company’s work. SaaS can be considered as an alternative to the classic version of the software installation, with its advantages and disadvantages.

Software as a service relates to cloud technologies for business and has modifications: models like PaaS (platform as a service) and IaaS (infrastructure as a service) have much in common with SaaS. SaaS is the simplest cloud model from the user’s point of view. IaaS assumes only access to a virtual server, PaaS — access to a server and basic software (operating systems, database management). In terms of economic structure, there is more in common. However, which model to use will be prompted by the project itself and its goals. The understanding of the SaaS varies even among expert practitioners. In a broad sense, even popular web applications like Skype or mail services from Yandex and Google can be attributed to SaaS technologies. Yet in the classic sense, SaaS is a business-oriented model. Another controversial issue is whether the presence of a subscription fee is an obligatory characteristic of this concept. We will proceed from the narrower definition of SaaS as a subscription business application.

Economic Features Of SaaS As A Business Model

Yet, in the classical sense, SaaS is a business-oriented model that many start-ups in the IT sector can use. This is a cozy model for a SaaS startup at an early stage with a model that sells a SaaS solution through its website, offers a 30-day free trial (or less), gets most of its trial users organically and converts them into paid clients through online marketing. Therefore, the key factors for a SaaS startup are the organic growth rate, the marketing budget and customer acquisition costs, the conversion rate, ARPU and the churn rate. If your SaaS startup has a higher level of interaction, where revenue growth is largely determined by the number of personnel, then the model plan needs to be changed accordingly. Here are some of the economic features of the model:

· All starts with the registrations you add. Consider registrations (subscriptions) that are not monitored and traceable registrations (registrations with AdWords and other paid advertising where you can track the cost of getting registration). You may have to break this further depending on your customer acquisition channels.

· Then we assume that the project converts a certain percentage of subscribers into the category of paying customers (with a delay of one month if you have a 30-day free trial). A model can contain only one conversion rate, regardless of the source of registration. You can change this if your conversion rate varies depending on the source of registration (as a prediction option)

· Next, you need to calculate the possible income by multiplying the (approximate) projected number of customers that you may have in the middle of the month by your average projected income per account. If your project provides a multi-level pricing model, then consider the possibility of modeling it.

· Turning to costs — everything should be clearly written, calculated and justified. Be attentive to the project and its costs. An overlooked thing will surely hit the project on time.

· As for profit and loss, and cash flow, everything is very simple, use the assumption that your EBIT is equal to your operating cash flow. That is, you monthly collect money from your customers, you do not make any additional investments (in terms of accounting) and that are no taxes or interest payments. Simplification of forecasting works well for most SaaS startups, but it, of course, should become more sophisticated as it grows and the configuration of calculations is constantly becoming more complex, adding additional parameters

· Last — when predicting the SaaS business model, do not go for the synthetic decoration of the situation. Financial plans with an EBIT margin of 90% in the short term (up to 3 years) are a utopia and a classic mistake that can occur if you forecast your earnings to grow exponentially but do not provide your project with a real increase in costs. After all, if the load grows in a project, then the number of necessary personnel will grow at least, including the technical components of the project

The SaaS business model should give you the answer to the following questions:

1. What are the key factors affecting the profitability of a project and model?

2. How does the project attract and acquire customers, how is this reflected in costs and revenues?

3. What are the drivers of customer value and the life cycle of your product/service? How are these factors expressed in variables when calculating a model?

4. How do all these variables interact?

To become profitable using the SaaS business model, you should keep in mind the following truth: Lifetime> customer acquisition costs + service provision costs (paid and free). In simple terms, the lifetime value of your paying customers should be more than the costs needed to acquire them, plus the costs of servicing all users (free or paid). There are many different factors that affect profitability, including:

· Cost per acquisition

· Media efficiency (traffic sources, CTR, impressions)

· % Conversion Registration Funnel

· Average number of viral invitations

· Lifetime cost

· retention rates

· revenue structure

Understanding these components, you can customize your model and find out what indicators you need to use to achieve profitability in the SaaS model. At a high management level, here are some things that need to be tracked in the model:

· How do you pay for traffic? (CPM/CPA/CPC)

· What do intermediate metrics look like? (Impressions/CTR/etc.)

· How does your registration/subscription funnel work?

· How much do you spend on users you register?

Here are some factors to think about in terms of planning to increase or decrease the purchase price (CPA — cost per acquisition):

· Traffic source

· Cost model

· User requirements

· Audience and subject

· Funnel design

· Viral marketing

· A/B testing process

About the funnel in SaaS. As soon as you register your users on the site, the question arises how to convert them all into paying customers and whether there are any viral effects. In reality, the following happens:

· Every certain period of time comes to a group of newly registered users (both acquired through advertising and through viral marketing)

· Some % of these users turn into paid users

· Some % of these users then send out virus invitations

· Revenue is generated by creating a database of only paying users

· But costs are added up due to the base of both active and passive users

The point is to create the right combination of functions for segmenting people who are willing to pay but without alienating the users who make up your free audience. Do it right, and your conversion rate can reach 20%. Do it wrong, and your LTV will be very close to zero. That is why these functions should be built into the core of the SaaS business. You need to find a balance between free and “pay for”! Just remember that during the time it takes you to figure out your funnel, the viral cycle and everything else, all the free users you create more costs in your system!

About saving users in SaaS. Of course, it’s not enough just to get paid users, you need to save them. If you have a very high churn rate, then at best you will be stuck on a treadmill (you do a lot of work, you barely cover the cost, but you should forget about the profit). In the worst case, losing a lot of money is easy if the CPA exceeds the LTV. How sensitive are hold/save parameters to LTV? Here is a small example: the lifetime value (LTV) is the amount of revenue that a user generates from the first period to the abandonment of your service.

Think of it as an infinite amount that looks like:

LTV = rev + rev * R + rev * R ^ 2 + rev * R3 + …

Where rev is the income generated by the user for a certain period of time, and R is the retention rate of the user between certain periods. So, LTV = 1 / (1-R) ​​* rev

Let’s decide for clarity that for a certain period of time you have earned $1, and you have, say, 1000 paying subscribers/users. For example, compare the difference between 50% retention and 75%:

At 50%: LTV = 1 / (1–0.5) * $ 1 * 1000 = $ 2000

At 75%: LTV = 1 / (1–0.75) * $ 1 * 1000 = $ 4000

This means that in this case, by increasing the retention rate by half (relatively speaking), you will actually double your income. And even more when you reach the status of “application killer” and reach a retention level of about 90%. So we get:

LTV = 1 / (1–0.90) * $ 1 * 1000 = $ 10,000

You need to know that retention rates, as a rule, are not fixed numbers — they, as a rule, become well, the longer the group of users stays with you! Thus, the main factors influencing this coefficient are reduced to:

• Product Design

• Notifications (optimize them)

• If successful, the effects of saturation

On cash flow in SaaS. In the paid user model, it takes time to get a break — even point. You pay for the user in advance, but then the revenue cash flow decreases over several periods of time. As a result, you are prone to negative cash flow for a certain number of periods of time, which then becomes positive. This effect is exacerbated if your model specifically depends on virus capture because you will not get significant users in the virus version until your user base becomes large. As for the average revenue per paying customer, then, as a rule, you find that your customer base consists of several segments. You can rate them differently through different subscription levels (Free vs Pro vs Business) or pay-after or with many other models. Ultimately, you can put all of this into one parameter, which is called revenue from each paying customer. You can also divide income by the number of users (paying or not) to get the average revenue per user (ARPU). As for the cost of service, your predictions will vary. The main thing is to try not to do anything too expensive for free users! In the end, given that typical conversion rates are <10%, and subscription services are usually <$ 20 per month

Lifetime value in SaaS. Essentially, you calculate the number of payments that the paying users will generate during the entire time, called “user periods” in the model. The revenue per payer to get the total amount then multiplies this. For the paid acquisition model, it is more important to calculate LTV not for paying users, but for all registered users (paid or free). This way, you can find out if you can manage your traffic profitably by buying ads. Then you compare this LTV with the effective LTV that you get from the user's purchase, and then consider their viral effects. There is no doubt that there are inaccuracies in the SaaS business model. There is something to think about and what to improve. Here one cannot agree with the majority of experts who propose to improve the following in SaaS:

· Benchmarks of real data for comparison

· More detail for choosing from attracting users for affiliate or advertising or other options

· The degree of saturation in the viral model

· The best model for retention rates, except for one fixed indicator

· More complex cost accounting per user (infrastructure/employees/etc.)

· Simulation with multiple sources of income, including transaction fees

· Calculation of costs for the purchase of advertising, with an increase in profits/while reducing profits. And so on

As you could understand, you have already come across SaaS technology many times, and the number of such services continues to grow due to their relevance and ease of use. At the same time, SaaS, as a business model, in my opinion, lags behind the technology itself. Few projects and startups work with it and the majority, unfortunately, are paying less attention. However, progress does not stop there, offering more and more perfect and interesting ideas, taking into account the needs of each case. Accordingly, technological progress will require its model to substantiate, then SaaS and its variants and modifications are gradually being introduced into the digital economy and even into the crypto sphere step by step.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet Marketing Solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business

The metrics may not be the best model of what’s actually happening in the business every day, or people may use different definitions of the same in a way that makes it hard to understand.

Ultimately, good metrics aren’t about raising money from VCs — they’re about running the business in a way where founders know how and why certain things are working or not and can address or adjust accordingly. And from now, we will discuss and talk about some expert opinions as from Andreessen Horowitz VC Fund management. Let’s start.

#1 Bookings Vs. Revenue

A common mistake is to use bookings and revenue interchangeably, but they aren’t the same thing. Bookings are the value of a contract between the company and the customer. It reflects a contractual obligation on the part of the customer to pay the company. Revenue is recognized when the service is actually provided or ratably over the life of the subscription agreement. How and when revenue is recognized is governed by GAAP. Letters of intent and verbal agreements are neither revenue nor bookings.

#2 Recurring Revenue Vs. Total Revenue

Investors more highly value companies where the majority of total revenue comes from product revenue (vs. from services). Why? Services revenue is non-recurring, has much lower margins, and is less scalable. Product revenue is what you generate from the sale of the software or the product itself. ARR (annual recurring revenue) is a measure of revenue components that are recurring in nature. It should exclude one-time (non-recurring) fees and professional service fees. ARR per customer: Is this flat or growing? If you are upselling or cross-selling your customers, then it should be growing, which is a positive indicator for a healthy business. MRR (monthly recurring revenue): Often, people will multiply one month’s all-in bookings by 12 to get to ARR. Common mistakes with this method include:

(1) Counting non-recurring fees such as hardware, setup, installation, professional services/ consulting agreements;

(2) Counting bookings (see about bookings).

#3 Gross Profit

While top-line bookings growth is super important, investors want to understand how profitable that revenue stream is. Gross profit provides that measure. What’s included in gross profit may vary by company, but in general, all costs associated with the manufacturing, delivery, and support of a product/service should be included. So be prepared to break down what’s included in — and excluded — from that gross profit figure.

#4 Total Contract Value (TCV) Vs. Annual Contract Value (ACV)

TCV (total contract value) is the total value of the contract and can be shorter or longer in duration. Make sure TCV also includes the value from one-time charges, professional service fees, and recurring charges. ACV (annual contract value), on the other hand, measures the value of the contract over a 12-month period. Questions to ask about ACV:

What is the size? Are you getting a few hundred dollars per month from your customers, or are you able to close large deals? Of course, this depends on the market you are targeting (SMB vs. mid-market vs. enterprise). Is it growing (and especially not shrinking)? If it’s growing, it means customers are paying you more on average for your product over time. That implies either your product is fundamentally doing more (adding features and capabilities) to warrant that increase or is delivering so much value customers (improved functionality over alternatives) that they are willing to pay more for it.

#5 LTV (Life Time Value)

Lifetime value is the present value of the future net profit from the customer over the duration of the relationship. It helps determine the long-term value of the customer and how much net value you generate per customer after accounting for customer acquisition costs (CAC). A common mistake is to estimate the LTV as a present value of revenue or even gross margin of the customer instead of calculating it as net profit of the customer over the life of the relationship. Reminder, here’s a way to calculate LTV:

• Revenue per customer (per month) = average order value multiplied by the number of orders.

• Contribution margin per customer (per month) = revenue from customer minus variable costs associated with a customer. Variable costs include selling, administrative and any operational costs associated with serving the customer.

• Avg. life span of the customer (in months) = 1 / by your monthly churn.

• LTV = Contribution margin from customer multiplied by the average lifespan of the customer.

Note, if you have only a few months of data, the conservative way to measure LTV is to look at the historical value to date. Rather than predicting average life span and estimating how the retention curves might look, we prefer to measure 12 months and 24-month LTV. Another important calculation here is LTV as it contributes to the margin. This is important because a revenue or gross margin LTV suggests a higher upper limit on what you can spend on customer acquisition. Contribution Margin LTV to CAC ratio is also a good measure to determine CAC payback and manage your advertising and marketing spend accordingly.

#6 Gross Merchandise Value (GMV) Vs. Revenue

In marketplace businesses, these are frequently used interchangeably. But GMV does not equal revenue!

GMV (gross merchandise volume) is the total sales dollar volume of merchandise transacting through the marketplace in a specific period. It’s the real top line, what the consumer side of the marketplace is spending. It is a useful measure of the size of the marketplace and can be useful as a “current run rate” measure based on annualizing the most recent month or quarter.

Revenue is the portion of GMV that the marketplace “takes”. Revenue consists of the various fees that the marketplace gets for providing its services; most typically these are transaction fees based on GMV successfully transacted on the marketplace, but can also include ad revenue, sponsorships, etc. These fees are usually a fraction of GMV.

#7 Unearned or Deferred Revenue … and Billings

In a SaaS business, this is the cash you collect at the time of the booking in advance of when the revenues will actually be realized. As we’ve shared previously, SaaS companies only get to recognize revenue over the term of the deal as the service is delivered — even if a customer signs a huge up-front deal. So in most cases, that “booking” goes onto the balance sheet in a liability line item called deferred revenue. (Because the balance sheet has to “balance,” the corresponding entry on the assets side of the balance sheet is “cash” if the customer pre-paid for the service or “accounts receivable” if the company expects to bill for and receive it in the future). As the company starts to recognize revenue from the software as service, it reduces its deferred revenue balance and increases revenue: for a 24-month deal, as each month goes by deferred revenue drops by 1/24th and revenue increases by 1/24th. A good proxy to measure the growth — and ultimately the health — of a SaaS company is to look at billings, which is calculated by taking the revenue in one quarter and adding the change in deferred revenue from the prior quarter to the current quarter. If a SaaS company is growing its bookings (whether through new business or upsells/renewals to existing customers), billings will increase. Billings is a much better forward-looking indicator of the health of a SaaS company than simply looking at revenue because revenue understates the true value of the customer, which gets recognized ratably. But it’s also tricky because of the very nature of recurring revenue itself: A SaaS company could show stable revenue for a long time — just by working off its billings backlog — which would make the business seem healthier than it truly is. This is something we, therefore, watch out for when evaluating the unit economics of such businesses.

#8CAC (Customer Acquisition Cost) … Blended Vs. Paid, Organic vs. Inorganic

Customer acquisition cost or CAC should be the full cost of acquiring users, stated on a per user basis. Unfortunately, CAC metrics come in all shapes and sizes. One common problem with CAC metrics is failing to include all the costs incurred in user acquisition such as referral fees, credits, or discounts. Another common problem is to calculate CAC as a “blended” cost (including users acquired organically) rather than isolating users acquired through “paid” marketing. While blended CAC [total acquisition cost / total new customers acquired across all channels] isn’t wrong, it doesn’t inform how well your paid campaigns are working and whether they’re profitable. This is why investors consider paid CAC [total acquisition cost/ new customers acquired through paid marketing] to be more important than blended CAC in evaluating the viability of a business — it informs whether a company can scale up its user acquisition budget profitably. While an argument can be made in some cases that paid acquisition contributes to organic acquisition, one would need to demonstrate proof of that effect to put weight on blended CAC. Many investors do like seeing both, however: the blended number as well as the CAC, broken out by paid/unpaid. We also like seeing the breakdown by dollars of paid customer acquisition channels: for example, how much does a paying customer cost if they were acquired via Facebook? Counterintuitively, it turns out that costs typically go up as you try and reach a larger audience. So it might cost you $1 to acquire your first 1,000 users, $2 to acquire your next 10,000, and $5 to $10 to acquire your next 100,000. That’s why you can’t afford to ignore the metrics about the volume of users acquired via each channel.

#9 Active Users

Different companies have almost unlimited definitions for what “active” means. Some charts don’t even define what that activity is, while others include inadvertent activity — such as having a high proportion of first-time users or accidental one-time users. Be clear on how you define “active.”

#10 Month-on-Month (MoM) Growth

Often this measured as the simple average of monthly growth rates. But investors often prefer to measure it as CMGR (Compounded Monthly Growth Rate) since CMGR measures the periodic growth, especially for a marketplace. Using CMGR [CMGR = (Latest Month/ First Month)^(1/# of Months) -1] also helps you benchmark growth rates with other companies. This would otherwise be difficult to compare due to volatility and other factors. The CMGR will be smaller than the simple average in a growing business.

#11 Churn

There are all kinds of churn — dollar churn, customer churn, net dollar churn — and there are varying definitions for how churn is measured. For example, some companies measure it on a revenue basis annually, which blends upsells with churn. Investors look at it the following way:

• Monthly unit churn = lost customers/prior month total

• Retention by cohort

• Month 1 = 100% of installed base

• Latest Month = % of the original installed base that are still transacting

It is also important to differentiate between gross churn and net revenue churn:

• Gross churn: MRR (Monthly recurring revenue) lost in a given month/MRR at the beginning of the month.

• Net churn: (MRR lost minus MRR from upsells) in a given month/MRR at the beginning of the month.

The difference between the two is significant. Gross churn estimates the actual loss to the business, while net revenue churn understates the losses (as it blends upsells with absolute churn).

#12 Burn Rate

Burn rate is the rate at which cash is decreasing. Especially in early-stage startups, it’s important to know and monitor burn rate as companies fail when they are running out of cash and don’t have enough time left to raise funds or reduce expenses. As a reminder, here’s a simple calculation:

• Monthly cash burn = cash balance at the beginning of the year minus cash balance end of the year / 12

It’s also important to measure net burn vs. gross burn:

• Net burn [revenues (including all incoming cash you have a high probability of receiving) — gross burn] is the true measure of the amount of cash your company is burning every month.

• Gross burn on the other hand only looks at your monthly expenses + any other cash outlays.

Investors tend to focus on the net burn to understand how long the money you have left in the bank will last for you to run the company. They will also take into account the rate at which your revenues and expenses grow as a monthly burn may not be a constant number.

#13 Downloads

Downloads (or a number of apps delivered by distribution deals) are really just a vanity metric. Investors want to see engagement, ideally expressed as cohort retention on metrics that matter for that business — for example, DAU (daily active users), MAU (monthly active users), photos shared, photos viewed, and so on.

#14 Cumulative Charts (vs. Growth Metrics)

Cumulative charts by definition always go up and to the right for any business that is showing any kind of activity. But they are not a valid measure of growth — they can go up-and-to-the-right even when a business is shrinking. Thus, the metric is not a useful indicator of a company’s health.

Investors like to look at monthly GMV, monthly revenue, or new users/customers per month to assess the growth in early-stage businesses. Quarterly charts can be used for later-stage businesses or businesses with a lot of month-to-month volatility in metrics.

#15 Chart Tricks

There a number of such tricks, but a few common ones include not labeling the Y-axis; shrinking scale to exaggerate growth; only presenting percentage gains without presenting the absolute numbers. (This last one is misleading since percentages can sound impressive off a small base, but are not an indicator of the future trajectory.)

#16 Order of Operations

It’s fine to present metrics in any order as you tell your story. When initially evaluating businesses, investors often look at GMV, revenue, and bookings first because they’re an indicator of the size of the business. Once investors have a sense of the size of the business, they’ll want to understand growth to see how well the company is performing. These basic metrics, if interesting, then compel us to look even further. It’s almost like doing a health check for your baby at the pediatrician’s office. Check weight and height, and then compare to previous estimates to make sure things look healthy before you go any deeper!

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet Marketing Solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business.

Last year, Facebook announced the launch of their new decentralized cryptocurrency Libra and their corresponding Facebook product, a wallet for this called Calibra. This statement exploded the mind of cryptofans worldwide. Having a monster like Facebook commit to the decentralized financial future through their own currency means a lot for the blockchain community, and for the global economy.

Facebook’s entire mission as a company is to connect the world. There are currently more than 1.8 billion individuals that are unbanked and don’t have great access to even basic financial resources (Africa, Asia, South America); but the care that financial transactions are one of the most fundamental ways that people connect with each other, it will bring the added value, sure for Facebook. Libra and Calibra help advance Facebook’s mission to connect all these people and increase facebook market value. Sincerely there no need for a bank for the unbanked 1.8 billion users if they can just run all their transactions and manage their funds through Libra and Calibra, tied to their Facebook account. The idea is to promote the global decentralized ecosystem and guarantee financial access to anyone who can access the Internet.

What’s even more critical is Facebook’s size and potentiality to accomplish this mission. If Calibra can easily integrate financial payments into Facebook’s product, the scale and value of Calibra itself would instantly destroy the needs for any other electronic banking system, as centralized or decentralized. Facebook’s goal for Libra and Calibra is good, but it has its fair share of criticism. Facebook isn’t exactly the most trustable company; it’s been in the headlines for a while now with scandals of user privacy leaks, undisclosed data collection, ethically questionable ad-based business models, etc.

It’s hard to imagine a company with the reputation of Facebook establish a decentralized, “trustable” financial system. Facebook claims that they will keep Calibra and Facebook completely distinct, and Libra itself is governed by a non-profit, but who believes it? Libra also plans to debut in 2020, but the US Senate has already scheduled a hearing for July 16 to discuss Libra and evaluate its compliance with US and global financial regulations and how it interacts with Facebook’s unsettled business with the US government and antitrust regulations. Libra’s a technically strong cryptocurrency that provides a promising solution to the problem of billions of unbanked individuals globally; it’s one of the first steps towards a financial future of decentralization, trust, and most importantly, high accessibility. Some kind of social entrepreneurship goal. Its integration of the most advanced and sound blockchain technologies like BFT and two-token systems helps make Libra a decentralized, low-volatility option for financial transactions. And with Facebook’s scale, Libra truly has the potential to reach billions worldwide and become a highly used product through Calibra native integration with Facebook, Instagram, and WhatsApp accounts.

But at the same time, Facebook’s history and reputation opens up a lot of questions for what the future of Libra be. First, it’s launching as a permissioned ecosystem, preventing it from achieving its mission of connecting the financial world. Additionally, Facebook’s faced immense flack for poor diligence on user privacy and data collection — to the extent that even the US government is worried about Facebook’s new cryptocurrency. Altogether, Libra presents a promising, but questionable. The launch of the testnet in 2020 will inevitably create huge waves in the blockchain community and will help expose some of the critical flaws in Libra’s architecture and governance and better inform Libra’s potential as the global financial connector. Nevertheless, the scale of Facebook enables the possibility of a more decentralized payments network that can enable low-cost remittances and payments over a global low-volatility currency for billions of people (especially for the emerging markets that have a much higher penetration of Facebook users than the United States). As an investor, there would be investment opportunities in companies and applications that build on top of the Libra infrastructure, while for consumers, imagine a global venmo and the ability to make payments without the friction of high fees and different currencies. We can aspire that Libra will be born and developed, possibly in time, according to the Milestones.

How LIBRA differs from Bitcoins and PayPal

In fact, a new digital payment system of a well-known social network is much closer to traditional payment systems of web commerce than it seems at first glance. This is a crypto currency and like Bitcoin, it is also based on the blockchain. However, Libra will basically have real assets and will be pegged to stable currencies, and in the future to government securities. The partner list is impressive. They are financial companies like MasterCard, Visa, PayPal Holdings, as well as Coinbase EXC. The project partners contribute a $ 10 million membership fee. The goal of Facebook is to consolidate at least 100 of these corporate partners and form assets of $ 1 billion. So what are the differences?

Libra

The Facebook database has 2.4 billion active users (but not all accounts are real). It gives global access. However, the social network should guarantee its users that the convenience of Libra payments is not a compromise of confidentiality. The blockchains of Libra and Bitcoin have fundamental differences. On the Bitcoin network, transaction verification is decentralized among all system participants. Libra will have a centralized governing body that will monitor and verify all transactions. That, in principle, violates the philosophy of the DLT. The history of such operations will be stored in one place, and the Libra Association will be responsible for maintaining and verifying the digital “ledger”. And who will have access to it — will rule the world and information.

BITCOIN and LIBRA

Bitcoin can be used as a payment system, but mainly it attracts a different group of participants. For a number of reasons, the most popular cryptocurrency has never moved in the direction of the payment network. Only 1% of transactions in the Bitcoin network are made for payments (say experts from Chainalysis). Bitcoins are more often used as a stock asset, and for its main users it represents a profit. Bitcoin is a very high volatile, but due to this, sometimes a very profitable asset. Bitcoin miners are forced to sell part of the money they are receiving for their activities in the form of coin remuneration in order to recoup the real costs of purchasing equipment and electricity. As a fact, by the end of 2017, Bitcoin rapidly soared in price to $ 20,000, but a year later, it crashed to $ 3,500. On June 27, 2019, BTC sharply increased again to $ 14,000. Such volatility makes it difficult to use this cryptocurrency as a means of payment. Moreover, it is a fact.

PAYPAL and LIBRA

The company is one of the founders of Libra. Between the two payment systems in theory a lot in common. The key difference: the Libra network uses cryptocurrency, and PayPal uses Fiat. Fintech-company works by analogy with an online banking. The user may have a PayPal account that is not tied to a credit card or bank account. This payment system has financial information centralized. Users do not need to enter credit card numbers or bank accounts on various websites, which, thanks to fishing and other hacker tricks, can become the property of intruders. All financial information goes through PayPal. To receive money, you must provide an email address, not a bank account. However, PayPal, unlike Bitcoin, is not an anonymous system. The company transfers money based on a license and fulfills the requirements of AML standards. When creating an account, the user will be required to provide personal data. PayPal accepts a wide network of merchants. However, such a major player as Amazon with PayPal does not work, because it has a competing Amazon Pay application.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet Marketing solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business

And it seems to many if one will pronounce the ‘’tokenomics’’ aloud for no reason at all — one immediately becomes the king or queen of the party. So what is it? There are many definitions and interpretations, here are just a few. Tokenomics — the system of formatting pricing policy for tokens. Nevertheless, Chris Snook (the founder of the World Tokenomics Forum) is sure that the blockchain will be the main reason for the transformation of the standard market economy into “tokenomics”. He promotes this term, which he interprets as transferring business interactions into the sphere of digital contracts and settlements. Physical assets will also become digitized over time.

However, there is one larger definition of this process — “Tokenomics” means the fundamental economics of the token:

· First, among other qualities, tokens should have value, scalability, ability to withstand inflation and incentives to use them. Without a basic tokenomics, any token will not be funded.

· Another part of the definition of “tokenomics” means a financially sustainable business model. Every company needs a business model that needs to be logical and scalable. You need to know how your company’s future revenue flows (including the platform) work, and how these revenues can grow or change over time in the digital space. And if you cannot explain all this to professional (accredited) investors, they will not turn their investment attention to your project. We are talking about big money.

It is clear that tokenomics is a brand-new and growing field of financial new age literacy. Knowing the pros and cons of this exciting area will help you navigate the search for fundamental value in such projects. Without this knowledge, no way.

Only 10 years have passed since the beginning of the practical application of the blockchain technology, but during this time, there has been a significant technological and information leap in the development of the crypto industry. Terminology and many concepts in this rapidly developing industry are still at the stage of formation and refinement; absolutely new definitions appear as a result of the formation of a technological, informational and legal field. Therefore, the concepts introduced in various countries or by various experts are gradually modified, translated differently and do not always find an affirmative application. This is exactly the case of the mentioned above example. As an example, when using the concepts of cryptocurrency and token, mutual “confusion” often occurs, which is related to their diversity and the lack of “hard” established concepts and terminology. The unregulated release of cryptocurrencies or tokens has led to the emergence of cryptocurrency/tokens with a variety of properties, mechanics and functions that they perform, which also makes some confusion in the definition of tokenomics processes. And yet, despite the differences, I believe that the basics of economic relations and the coordination of interests between stakeholders and tokenholders of a crypto project are determined by the tokenomics of the project and nothing else.

Therefore, I will take courage and give a definition that, in my opinion, more accurately describes the essence of tokenomics. Tokenomics — so far at this present stage of development of the blockchain, are the rules for the functioning of a token within an ecosystem (a system of economic relations between project users, tokens holders and project tokens — as means of accessing or receiving certain bonuses prescribed in the matrix of such an ecosystem of the project) created by the crypto project . At the same time, the token itself is a digital conditional virtual unit with a certain value, utility, and linkage to something that is issued by any issuer in order to simplify/reduce the exchange process in the project’s ecosystem of the issuer. When issuing a token, each project can provide it with a unique utility by linking to it a number of pre-defined semantic, economic and technically guaranteed functions used in the project ecosystem. An example of such functions can be: token as a means of accessing a service, as a means of payment, as a means of accounting for an asset, a means of exchange or hodling, as a means of earning income, and so on, before a rationale. Increasingly, in modern projects, a token can simultaneously perform several functions. Each project can create its own model of token circulation and allocation in the ecosystem. The main thing is to preserve the logic of turnover and the use of tokens. Therefore, there is an opportunity for tokenization (partial tokenization) of almost any business. But this question is most likely a topic for another conversation.

In the process of transferring the business model of a project to a token, a lot of legal, economic and technical subtleties arise that need to be taken into account, namely: assess the risks for all project participants; to assess the possibilities of applying designed tokens in various jurisdictions, to determine the compliance of the process of their creation and registration with the laws of various countries; estimate the costs associated with the turnover of tokens, and the technical needs for speed, scalability, security and the level of decentralization of certain types of blockchain. The process of business tokenization allows you to attract additional funds for the development of the project’s ecosystem, but it is advisable to do this only on condition that the potential business development opportunities increase and the costs and risks associated with its tokenization are significantly lower than under the condition of using other models of raising funds. In this case, the purpose of tokenomics is to consider possible options for creating a token business model and answer the question about the feasibility of tokenizing a business. If the project’s tokenomics business model increases reasonable costs for the sake of tokenization, then there is no point without a profit and tangible profitability just to invest and develop a project for the sake of hype. The development of the token allows for the transfer of key aspects of the business model of the project to the token, laying into it maximum opportunities for use in the project ecosystem and thereby creating and increasing its value and usefulness in the eyes of both potential users and investors.

If you noticed, several times we mentioned theses about the presence of a value in the project, which conveys a similar property to the tokens emanating from the product of the project. After studying “public opinion and blockchain project accounts” on Twitter, one research group found (Chinese Academy of Information and Communication Technologies CAICT) that by the fifth month of its existence, only 44% of the projects continued to operate. Unfortunately, there are many reasons for such dismal statistics, but one of the main factors is the use value of the product, or rather, its absence in the project. Value, in this case, means satisfaction from the consumption of a good or service. Often there are products that do not have any significant utility. This means that we should not expect a high demand for them. Without a comprehensive understanding of the needs of your target market, you also cannot receive funding. Having developed a minimally viable product (MVP), one can judge its actual usefulness. Smart investors want to see the attractive force of the product, otherwise, they will not attract. But at the beginning of the project life cycle, attention is attracted by annoying marketing, but in the next phase of project implementation, marketing should be replaced by the already growing demand for your real tested (beta version), at least, project product.

Another feature of tokenomics is the possibility of communication not only between people but also between robots or other automatic systems (IoT). Under such conditions, tokenomics turns into robonomy (robotic tokenomics). Thanks to this, the concept of smart cities has become possible. Since programmable smart contracts allow to establish mutual settlements in cryptocurrency between different system objects. So far, tokenomics is a little-studied and rather abstract concept with many facets, which allows untested information to be dominant and becomes a fake. Of course, tokenomics allows you to raise capital with the help of ICO/IEO/STO, and also makes it possible to transmit value via the Internet, bypassing intermediaries and reducing transaction costs. Again, all this is also possible with a guaranteed minimum availability of elements of value in the product of the project and in the token, as a provider of such digital, as if not perceived ephemeral value. The economic model of the token allows you to create a certain value in the context of the crypto market by supply and demand for it (the old classic basis). Also, thanks to the blockchain-architecture, not only humans, but also robots/devices can communicate with each other, which makes the concept of smart cities, not a future, but a reality. However, to be able to earn money at the junction of the fiat system and the crypto market, it is necessary to study thoroughly the ways of creating value in economic models of the token. In turn, the process of tokenomics maturing requires the involvement of specialists of different profiles and having the different sectoral experience to work out from scratch new concepts, terms, and provisions for the functioning of tokenomics. What we are now is the formation of a myth. What should be the result of the coordinated work of such specialists is the birth of the reality of the functioning of tokenomics.

There is no single method for doing this. Some things need to be developed from the beginning, having not only basic practical knowledge but also imagination and fantasy.

In general, the skills of the following specialists may be useful in:

· People with a broad outlook to build the architecture of tokenomics (that is, business models, as discussed above);

· Mathematics for the development of mathematical models created by business models;

· Macroeconomists who can understand the functioning of the entire system and offer their options for the modernization and cyclic development of the ecosystem in your project;

· Game designers who have already developed similar tokenomics in games. Their experience needs to be transferred to digital assets in order to adapt the world of real needs into the world of virtual digital being;

· IT specialists, blockchain experts — people who can transfer the meaning and logic of tokenomics to the project code (smart contract, and so on)

· Marketers, how can we be without them, despite all the distortions of marketing dominance in tokenomics, as you need specialists who are able to demonstrate the effectiveness of your business model of tokenomics with clear and accessible tools .

Then with the involvement of such specialists, what is tokenomics in general? And this is the whole set of possibilities for encrypting information into blocks, distributing them and then decrypting them, which allows managing and controlling the economy within the project ecosystem. Money has already fulfilled its role as the equivalent of the value of goods and services in the historical development of civilization, now they are becoming obsolete. Fiat money is already working on the inertia of thinking, traditions and, in general, the overall slowness of the already constructed structures. The next step in the development of civilization in this regard will be a gradual and smooth departure from the presence of an equivalent, to the direct accounting of everything that is produced and distributed. And a token is best suited for this: not only as a unit of account but also as a block of information containing all the diversity of information saturation. No wonder the transition from smart contracts to tokens took zero time. Compressing important information into a token and storing tokens in immutability and communication with other tokens in an environment that tends to universality (loss of value expressed in units of equivalent values) is taking into account what is happening at so many levels and in such a range of functionality that is possible and necessary to people. For example, many ecosystem participants are concerned with the obligation to fulfill their commitments. Tokenomics gives the answer: information is recorded, encrypted, inserted into the indestructible environment. Then, as needed, it is extracted from there and injected into the ecosystem through a token. The other participants are concerned with counting the necessary resources. There are no problems either: a chain of token blocks is taken, which statistical bodies will soon begin to create, the necessary blocks are decoded and implemented into the ecosystem.

And so on. Information goes into the category of necessary and not very necessary. The necessary is encoded, encrypted, packed and stored in digital storage. As needed in it, this information is extracted from there. Unnecessary, as now, hang out until someone needs it tomorrow. Tokenomics do not at all claim the laurels of the economy, does not seek to occupy its niche, it rather displaces the managerial aspect of the economy, replacing it with itself. When tokenomics reaches the level of replacing more than half of the functions of existing management and control (in the field of information), this will mark the transition to the era that is called digital (after post-industrial). Its distinctive features will be a drop in the overall required level of management and control. It is quite natural and very objective (by the nature of things) phenomenon. It will be enough for a person to press ENTER at the beginning of the work of the management structure of the tokenomics, which will then itself give commands to the real economy of production, really assessing the resources of materials, energy resources, resources of necessary machine processing, creating a product, transporting it and subsequent redistribution. It is important to understand that the “known” and “familiar” needs of a person include only a few areas: food, transport, housing, clothes and shoes, everything else that may not be adjustable tokenomics due to the lack of standardized justification.

What prevents the development of tokenomics now?

What are the Myths and Problems? Four Things Interfere:

· the first is the inertia of people who are accustomed to living in certain conditions, where tokenomics is perceived as being either overly simplistic or cautious

· the second — the difference in the psyche of people in which one is better to exist in the hierarchy, while others are drawn to freedoms

· The third is the “false” path of Artificial Intelligence, which will lead to nowhere or will lead nowhere.

· There is another big “obstacle” — the existence of national jurisdictions with their different rules and restrictions. And tokenomics contradicts some of the “rules” and “laws”, as well as provisions and structures. Mainly in the field of management, of course, and a little in the redistribution of resources.

Therefore, the following conclusion becomes obvious: we need a clear systematic process to design a token and form a tokenomics in a project. The following algorithm may be such a process:

1. Make a description of the project and product. We determine their value and consumer characteristics. We try to describe as accurately as possible all the results of the project’s activities and what part of these results we plan to tokenize.

2. We identify all stakeholders, i.e., persons potentially interested in the project, as well as parties that may have an impact on the project (near and far surroundings): investors (tokenholders), product consumers, customers, developers, partners (logisticians, suppliers, and intermediaries), regulatory bodies.

3. The interests of the parties are determined. Tokenomics should allow maximum consideration for the interests of all parties to the project through the introduced mechanics. The influence of the mechanic can have a different impact on the organizers and other project participants. At the same time, it is always necessary to seek a compromise in reconciling conflicting interests. For example, by giving large token holders additional bonuses or discounts on the purchase of tokens, we can partially compensate for this by freezing them for sale. The task of tokenomics is to take into account the usefulness and value of the various results of the project’s activities for each of the parties, as well as to ensure the greatest intersection of the project’s audiences with each other.

4. We determine the type of token, the functions of which to a greater extent can ensure the interrelation of interests of all stakeholders, meet legal standards, and its technical characteristics (the same dilemma — utility, security, cryptocurrency, equity, hybrid)

5. Determine the blockchain platform on which the token will function: Ethereum, Stellar, NEO, NEM, Waves, BitShares, Omni, etc.

6. We form models of token turnover within the ecosystem. We define the basic mechanics that will provide the motivation of token holders and stakeholders. We list some of them: “burning”, cashback, a discount when buying, voting, “freezing”, holding and charging interest, encouraging active actions. Then the token model is checked for compliance with the jurisdiction of the countries of its circulation.

7. Determine the real costs of the project and its structure. Consider options for business development with different amounts of attracted invested funds. The structure and volume of expenses should correspond to the roadmap of the project.

8. Distribution of tokens, i.e. token distribution structure. Due to the issue of tokens, the project has two sources of funds for business development. These are collected funds from the sale of tokens, as well as the project tokens themselves, which are partially distributed among the tokens holders, and partially distributed among the team, advisors (adviser) and/or the project community.

9. Formation of a predictive financial model of tokenomics. A good tone for the project is the elaboration of the financial model. This suggests a detailed calculation of preliminary costs, an understanding of the functions and mechanics of an ecosystem. However, it should be noted that the general trend of capitalization of the crypto market has a significant impact on financial calculations. Moreover, we must understand that not one financial model will be perfect and accurate in terms of the presentation and argumentation of forecasts.

Usually, a detailed description of tokenomics is presented in white papers of the projects. With the design and issue of tokens, each project has the ability to create unique tokenomics that can ensure the success of the project. If until 2016, when emitting tokens, all possible functions and mechanics were fairly freely used in the process of tokenomics formation, then after the rapid growth of 2017 and the increased interest of regulators to the crypto market, more attention was paid to legal issues, and after a noticeable drop in the crypto market in 2018, more attention is paid to the formation of a correct working business model. Now, experts have developed more than 3,000 models of tokens, but far from, not all of them comply with the laws of different jurisdictions and are cost-effective, since the above algorithm was initially ignored. Making a good tokenomics model is extremely difficult. However, you should pay attention to:

· High growth (or high growth potential).

· Measures to encourage ecosystem participants who are willing to hold tokens, or vice versa — enter a long transaction processing time so that there is an incentive to hold tokens. Alternatively, it is possible to encourage those who hold tokens, as well as impose commissions for their elimination.

· Acceptable transaction turnover (or the possibility thereof) in relation to market capitalization

The true goal of any ecosystem is to make all parties at the same time buyers and sellers. On the marketplace, it is necessary for sellers to find out buyers of tokens themselves, and not just exchange them for fiat or other cryptocurrencies. Therefore, the main conclusion is the development of a business model of tokenomics is a question necessary for solving, but the model will not always be able to guarantee the expected results. Tokenomics is first a business model that should be clear and understandable, but at the same time adaptive for the mandatory correction. At this stage, the tokenomics is still under the shadow of myths, for reality — you need more practice, experience and be prepared for failure, otherwise, the implementation and application will not come close to reality.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet marketing Solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business

What is behind Artificial Intelligence?

Artificial intelligence can be defined as a science that modeling intelligent human behavior. This definition may have one significant drawback — the concept of intelligence is difficult to explain in principle. The problem of defining artificial intelligence comes down to the problem of defining intelligence in general: is it something in common, or does this term combine a set of disparate abilities, and even more as individual or even collective abilities? To what dimension can intelligence be created? What is creativity? What is intuition? Is it possible to judge the presence of intelligence only by the observed behavior? What is human intelligence and especially the intelligence of a machine or program? Is it possible to reduce human intelligence to an algorithm? And here, the question is more likely even philosophical than scientific. To be honest, no answers have yet been found to these questions, but they all helped formulate the tasks that forming the basis of modern artificial intelligence as a scientific approach. Part of the attractiveness of AI lies in the fact that it is an original and powerful weapon for researching these very problems. AI provides a tool and test model for theories of intelligence: these theories can be formulated in the language of computer programs, and then tested.

The problem of finding the exact definition of artificial intelligence is understandable. The study of AI is still a young discipline, the structure of this phenomenon in science is still being formed, so only with time, a clear thesis will be generated in the public mind about what AI is. However, it is already visible today that AI is designed to expand the capabilities of primarily computer sciences, and not to determine their boundaries. The next step may be the expansion of the intelligence of human being himself. One of the important tasks facing researchers is to maintain these efforts with clear theoretical principles that are currently a problem.

The most optimal definition for today is the following: AI is a field of science and engineering that creates machines and computer programs that have intelligence, or AI is a field of computer science that develops intelligent computer systems, that is, systems that have capabilities that we traditionally associate with the human mind — understanding of the language, learning, the ability to reason, solve problems. As a result, AI should become a unique product of technological progress, which will allow machines to learn, using human and their own experience, adapt to new conditions within the framework of their application, perform diverse tasks that for a long time were only possible for humans, predict events and optimize resources of various character. Most of the examples of using AI known today — from computers playing chess to autonomous robotic systems — still depend on the human factor and require deep training. However, even at the stage of their current progress, they globally affect the life of the whole society, forming new ideas about the future and prospects for the development of modern technologies. So far, AI has not become the ability to come to a final decision through calculations, the human factor in monitoring the results of applying AI still dominates decision-making, as long as there is access to the algorithm code, the results of calculations and observations / conclusions AI can be changed and influenced. However, how the person of the future wants to let go free and forget about access to such an algorithm code is a rhetorical and manipulative question.

Today I repeat once again, while there is no understanding what types of computational procedures we want to call intelligent, we know far from all the mechanisms of our intellect to talk about the artificial. Moreover, the concept is still fabulous, in which futurists scare us with the fact that in the near future, AI will completely replace human intelligence. After all, as long as researchers using algorithms that are not observed in humans or require much larger computing resources, artificial attempts to replace us are too fantastic. But this is only so far, at this stage of the development of technology and the youngest science. Arthur R. Jensen, a leading researcher in the field of human intelligence, as a “heuristic hypothesis” claims that people have the same mechanisms of intelligence and intellectual differences are associated with “quantitative biochemical and physiological conditions”. These include speed of thinking, short-term memory, and the ability to form accurate and retrievable long-term memories.

The situation in AI, as I said, is the opposite. Computer programs have a large margin of speed and memory, but their abilities are corresponding to intellectual mechanisms that program/algorithm developers well understand and can invest in them, that is, the result tends to be the way researchers still see and program it, to the point of initiative AI itself is still far away, and Turing’s tests, even if it is successfully completed by the machine/AI, in fact, will not mean victory in the simulation of human intelligence. This will most likely be another achievement that will only bring us a little closer to an idealistic result. The ultimate goal is to create computer programs that can solve problems and achieve goals in the same way as a human. Again, the quality of human intelligence is flexibility and mobility, the admissibility of recognizing one’s mistakes, the use of experience, both positive and negative, as soon as AI machines will possess such qualities, even if they can pass the Turing test and solve certain problems much faster tasks instead of man. I think not soon. The main mistake of scientists here is in the desire to replace human with AI, and it should be according to the ideation, not a replacement, but an addition. As long as there is such a mistake in setting the results, there will undoubtedly be a threat of ‘’victory of the machines over humans’’. For AI, it is important that when solving problems, the algorithms are as effective as the human mind. The determination of subdomains in which good algorithms exist is important, but many programs that solve AI problems are not related to easily identifiable subdomains.

By the way, the computing power of the machine is greatly exaggerated. Yes, as a calculator, a human cannot compete with a computer. But what most consumes a machine resource? Any gamer will say — processing video information. However, the human has no problems with this. The processing and analysis of video information by humans are still an order of magnitude superior to the capabilities of the machine, and when you consider that both auditory information, olfactory, tactile, and coordination of movements are processed simultaneously — and all this online, then there is nothing to be afraid of. Pattern recognition for a machine is a very difficult task, the task of developing that is very intellectual. Besides intelligence, can a machine be intelligent? After all, the main diversity of the human minds is the will to irrational actions. For example, the desire to unknown. Therefore, perfection is still a long way to go.

What about perspectives?

Humanity has made a powerful evolutionary breakthrough, leaving far behind other biological forms of life. Driven by the development of technology, the process of mastering the natural environment, the complexity of human social life, filled with artificial technical inventions, have reached their zenith in modern times. Previously, the development of technology focused on the design of devices that simulate with much higher performance than in their natural manifestation, external senses and organs of human action: instead of natural vision — a microscope or binoculars, instead of a hand — an excavator, instead of natural hearing — radio communication, instead of legs — car, etc. And then there appeared devices designed to imitate and replace, it would seem, the most important thing in human that which has long been recognized as its most significant attribute — rationality. AI systems were designed to reproduce and, possibly, in the future replace at a higher quality level the process of human thinking, its ability to rational intellectual actions. Despite the alarming prophecies of Elon Musk, the “strong” intellect, “uprising of machines” is certainly far away, but the “weak” AI has already firmly entered our lives and has found wide practical application. Hype of recent years in machine learning has fundamental reasons and is quite justified — business has become very attractive for these “smart” technologies, and this is not only for image or for a tribute to “fashion”. They give a specific economic effect. For example, McKinsey analysts estimate the AI ​​market by 2025 to $126 billion, while spending per year up to $30 billion by major players in recent years. In addition, the numbers will only increase over time. In many respects, the increased interest in AI on the part of specialists is caused by a new stage in the development of neural network technologies, as deep neural networks, but the revolution in working with data played a decisive role in this. We can digitize that countless amount of information that life itself generates every second, we can store it, process it and, most importantly, we want it, we try, and we can analyze it in many ways. The combination of the development of Big Data, the possibilities of Data Engineering and, of course, Data Science, against the background of global “Internetization” and the widespread dissemination of the IoT, led to an international conferences, where reports without mentioning AI are not included in the program, every startup threatens to revolutionize the world with AI, and every self-respecting company leader (in any field) considers having a machine learning department as mandatory. However, quantity is not always the quality of all of this. Most mathematical models have long been known, but it is the big data and the hardware capabilities of their processing in the “more” real-time mode that led to such a boom and the emergence of new specialties that are still not very professionally trained, but where they want to hire a lot — Data Engineer and Data Scientist.

If we talk about the main scientific and technical areas, AI today includes the following: machine /deep learning and predictive analytics, Natural Language Processing (NLP), smart robots and computer vision. But it’s more practical to consider these areas in the context of their business applications, and this is what Data Scientist is thinking about. In the forefront of the application, AI began to use the trading sector, as well as fintech, manufacturing, healthcare, and sports actively use many AI models and, most importantly, invest in their development in the future. For example, retail trade — targeted, personalized interaction with customers, recognition of their behavior, virtual assistants and smarter by the training structured chatbots, optimization of the geolocation of retail outlets, layout of goods on the shelves of trading centers, smart contracts with suppliers, the use of robots for warehouse operations — all this led to lower costs and increased sales. The greatest practical application has now received computer vision and natural language processing (NLP). But NLP is perhaps of a larger and longer-running nature. Today, even such conservative industries as insurance and legal services are beginning to implement AI. There is a change in the familiar, as it seemed, already unshakable procedures. While we are not talking about the complete disappearance of professions, but, of course, the number of specialists required in these sectors will be steadily decreasing. It will be only highly qualified professionals who will have to keep up with technology in order to remain in demand. Nevertheless, what, in principle, can AI do today despite of criticism, skepticism and revolutionary hype? In principle, if you are systematizing your merits, you can do many things. Today AI can:

· automate the continuous learning process and search using data (For this type of automation, the human factor is still necessary in order to ensure an effective and correct system for processing key requests and making appropriate decisions)

· perhaps intellectualize the product (AI turns standard automated systems into an intelligent product that works on user requests)

· Trying to adapt (AI develops using progressive learning algorithms and generates data for further programming)

· Analyze deep data (a thorough analysis brings to the surface all potential risks, generates forecasts and warnings, eliminates the adoption of erroneous decisions, prevents unsafe situations when playing a specific technical process or events)

· Strive for accuracy (in all spheres of human activity — medicine, agro, trade, engineering, entertainment, construction and so on)

· Operate already large data

In addition, where it is already actually used:

· In the military — defense complex

· In education, where there are great prospects for the development of AI products

· In business, basically in the fight against fraud, in the electric power industry, in the manufacturing sector, in banking and financial services, in transport and logistics, in trade and in the art and luxury goods market

· In public administration, as in forensic science, in the judicial system (the state program of China), in sports and medicine, analysis of citizens’ behavior (again the program of China’s Social Rating) — see my article on this topic — www.medium.com/@sergiygolubyev/digital-dystopia-as-a-model-of-panopticon-in-society-27d7dbcdab12

· In culture, in the media and literature, video, music, painting, special effects, games and photography

· In space exploration, in predicting solar storms and possible protection from asteroids, in the discovery of exoplanets, today we are actually reporting from the International Space Station (Int-Ball drone) (https://www.youtube.com/watch?time_continue=8&v=HMwdXrD8S3Y), already helps vehicles make landings and take-offs, track radiation and, in fact, for space expedition members to be a communication friend (CIMON project — Interactive Mobile Satellite Command Teams), as well as test development of space crew rescue systems ( FEDOR system Final Experimental Demonstration Object Research)

Are there any threats to humans?

My opinion, at this stage, taking into account the available results, of course there is no threat so far. All this is fantastic in the style of the Terminator and the fight against Skynet. However, if there is possible the biological synthesis (synthesis of the human mind and artificial intelligence), the danger of using the results obtained in malicious intent is possible. The danger is possible if AI is created on a biological basis, that is, not on modeling neural networks, but on growing DNA-based neural networks with simultaneous programming. However, the suspense is always scary. Whenever humanity was on the verge of new discoveries, innovative developments, or technical revolutions, people were afraid: what would bring these radical changes? Therefore, it was in the era of the transition from horses to cars, and at the dawn of the development of electricity, and during the development of the World Wide Web. Some saw changes in perspective, others saw a threat. What are alarmists talking about today, which include well-known scientists and businessmen (Elon Musk, Hawking):

1. Many people think that it will take a very short time before the AI ​​learns to improve, and use the accumulated experience and knowledge, which will make it over-developed. The short-term effect of using AI depends on who controls it, and the long-term effect on whether it will be possible to control it in the future. Such devices will be able to independently analyze situations and make the necessary decisions, and perhaps these decisions will not be in the human’s favor.

2. Many developments are being carried out almost uncontrollably today, which means that anyone can design a device that could potentially harm human or entire countries. The United Nations is already pushing for legislation to create AI based laws that can be used in military conflicts.

3. Scientists saw a threat to AI of a social nature. In their view, the widespread use of technology with AI will cause people to stop making decisions on their own and rely on the decision of the machine. As if having a chance to relieve oneself of responsibility. People will become too dependent on devices. Smart devices will be able to manipulate the opinions and decisions of their owners

4. The threat of future Super Nova Mind. According to Nick Bostrom, the evolution of AI will take place with human evolution. From the blind evolution in which we are now, to the conscious. In the future, the master of the planet will be only the superintelligence, and today, we are inexorably moving in this direction, the only question is whether the superintelligence, built on the basis of the human, will the primacy go to the artificially created device or will it be the result of synthesis (human and artificial)

5. In addition, of course, the Hollywood’s ‘’machine uprising’’ is the most common fear on this subject. So far, all these threats of a mythical nature, which most likely pose a reputation risk and risks at the legislative level, control the development processes of AI, as a science.

Nevertheless, the more real threat today is the practical implementation of the theory of ‘’Big Brother’’ by the state and special services using AI technology. Moreover, here it is already worth fearing today!!!

AI at the service of ‘’Big Brother’’

The expression about Big Brother is well known, both to lovers of social dystopias and to fans of literature in general. The phrase “Big Brother is watching you” gained fame after the release of the novel by the famous British writer J. Orwell “1984”, which continued the theme of the “faithful” revolution, which began in his work “Animal Farm”, which was an allegory for the October Revolution of 1917. In modern society, the term “Big Brother” is used to denote totalitarianism, anti-democracy and surveillance. The theory is that intelligence agencies of all developed countries organized a mechanism for total surveillance of citizens, including surveillance of Internet users. First of all, this concerns the work of American intelligence agencies, which are likely to process more data than leading technology companies.

In September 2017, Dawn Meyerriecks, deputy director of the Central Intelligence Agency for Technology Development, said her agency was working with 137 AI projects, many of which were related to Silicon Valley companies. This is an impressive figure. Apparently, the CIA is going to make AI technology the main tool for working with information, which means significant cash injections in this area. US intelligence agencies are already working with a gigantic amount of data, which, in fact, includes the entire Internet. The CIA is currently working on creating predictive algorithms with AI elements that could find non-obvious causal relationships in disparate data sets. Such systems should alert intelligence analysts to important events that slip out of sight of conventional tools. Decisions made on the basis of machine analysis will be used to make political and military (operational) decisions.

It should be noted that now the special services, delivering daily reports to the country’s leadership, are not able to assess the situation. Only tracking social networks requires gigantic resources, not to mention the analysis of satellite images, statements by local media and news messages in various social media and chat rooms. Therefore, special services will be forced to use developments in the field of AI. The beneficiaries have already become large technology companies that can be as data collectors, such as Alphabet Inc. (GOOGL) and Facebook Inc. (FB), and vendors of flexible AI platforms to perform various operational tasks, such as IBM Watson from International Business Machines Corp. (IBM). The Chinese Government, which has already done a lot to implement the policy of the theory of the ``big brother ‘’ (https://medium.com/@sergiygolubyev/digital-dystopia-as-a-model-of-panopticon-in- society-27d7dbcdab12). So there are AI threats to humans, but again, these same threats come from the human and the state itself, as a form of organization of control of human society. Another more real threat than the Rise of the Machines may be the same human factor, or rather, a Human interest in the easy money associated with the hype around AI.

Hype and scammers!!!

Unfortunately, the hysteria around AI generates numerous pseudo-revolutionary startups that can only wash away an investor’s wallet and not create a revolutionary product. Under the cover of AI technology and its development, ordinary routine processes are hidden that have nothing to do with AI. For example, former Engineer.ai employees told the Wall Street Journal that the company was tricking investors and users into claiming to use artificial intelligence to develop applications. In fact, this work is done by cheap programmers (https://www.wsj.com/articles/ai-startup-boom-raises-questions-of-exaggerated-tech-savvy-11565775004). In 2018, Engineer.ai raised $ 29.5 million.

Among its investors: Deepcore Inc., a subsidiary of the Japanese conglomerate SoftBank, as well as Zurich venture company Lakestar (the first investor in Facebook and Airbnb) and Singaporean company Jungle Ventures. A company becomes more attractive to investors when it claims to use AI in its work. Since the operation of this technology is difficult to track, experts cannot always determine whether it is really used in creating a product. However, the former and current employees of the company, as well as the documentation that came to the Wall Street Journal journalists, saying that Engineer.ai does not use AI to build application code — this is done by engineers from India. And there are many such cases around the world. The reason for all this is hype and money. According to PitchBook, venture capital firms almost doubled funding for AI startups in 2018 compared to 2017. To attract the attention of investors, it is enough to have only “ai” in the domain name of the company. The story repeats itself with blockchain. Earlier, a report by London-based venture capital firm MMC Ventures showed that technology companies, even in Europe, which call themselves AI, startups, do not actually use artificial intelligence in their products. In total, there are 2,830 such companies — about 40% of all startups. Some novice technology developers use the fancy phrase “artificial intelligence” to draw attention to themselves and their products in order to get more funding. According to MMC, companies claiming to work on AI solutions attract an average of 15–50% more investment. At the same time, startups themselves do not always declare the use of artificial intelligence. Therefore, the AI ​​sector is a potential bubble that, due to hype, can burst, harming real market participants, not fakes. Another threat is that AI could potentially be used to wreck the same person. Distinguishing truth from fake is becoming increasingly difficult. Artificial intelligence masters the natural languages ​​of human culture as fraud and propaganda.

In the summer of 2016, ZeroFOX, an information security company, discovered a new kind of Twitter bot called SNAP_R. It tricked users clicking on links, redirecting them to questionable sites. It served as an automated phishing system that analyzed the behavior patterns of users of a social network and found out their interests and needs. At that moment, when an unsuspecting user flips through the news feed, the bot throws him some kind of entry like “Archaeologists have discovered the grave of Alexander the Great in the United States — for details, click on the link’’. SNAP_R did not pursue any malicious purpose, since it was only a working concept. But the very fact of its existence warns us once again how careful we should be in the world of fake information, which is already being played by AI (https://www.blackhat.com/docs/us-16/materials/us-16-Seymour-Tully-Weaponizing-Data-Science-For-Social-Engineering-Automated-E2E-Spear-Phishing-On-Twitter-wp.pdf). At the same time, two researchers, thanks to SNAP_R, built a neural network that can learn from the analysis of large amounts of data. For example, it learned to recognize images by analyzing thousands of other images. It was able to recognize spoken language by learning from a database of conversation records with technical support. And, of course, can already generate phishing messages by analyzing Twitter and Reddit posts and known cases of online attacks. The mathematical powers of AI are used today everywhere in many areas — from speech recognition to text translation. The same power can perfectly work to deceive thousands of Internet users. I think it will be strange if the technology is not used for fraudulent purposes. Everything indicates it already possible today.

Many technology experts have serious questions about the AI that Deepfakes generates — fabricated visual content that is very similar to real. (https://www.nytimes.com/2018/03/04/technology/fake-videos-deepfakes.html?module=inline) In addition, many other examples that have far from positive uses. Therefore, the dilemma of the relationship between AI and humanity depends on humanity itself and the goals for which AI will be used. A big plus of a human in this matter is the fact that he can manage the whole process, yet. The main thing is to contribute to the creation of robots/algorithms that will bring only benefit to human existence, and not harm. An army of technology experts should start their work today and create all the necessary precautions. There are no serious studies on this issue; therefore, it is necessary to promote the emergence of special research institutes for the study of machine intelligence and life in the future. Is it possible to completely control all the processes of AI development, only time will tell.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet Marketing Solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business

Concept

Recently, plunging into the trend of innovative news, along with high-profile headlines about artificial intelligence, machine learning, ICO, crypto-tales about blockchain, more and more research and analytics on the issue of Business Process Automation have come across. I think the question is worth our attention as well.

Business automation is a partial or complete transition of stereotypical operations and business tasks under the control of a specialized information system, or hardware and software complex. As a result, the release of human and financial resources to increase labor productivity and the effectiveness of strategic management. This is the introduction of a software system that performs standard procedures using modern actual algorithms. We are talking about drawing up and writing out documents, controlling the execution of accounting, warehouse operations. Thanks to such innovations, the level of the whole enterprise’s work is improving. The key to the success of a business process are certain indispensable components. First, data and information are required for each step of the process. Secondly, an algorithm is needed — a process — which people or software (in the case of automating a business process) could follow. Finally, a result must be defined in order to achieve which efforts are made, time is wasted and data is used. It is important to recognize the importance of all these components of business process automation and take the time to select and formalize each of them; since only by founding the proper basics one can count on the correct and excellent result.

Trending AI Articles:

1. Six AI Subscriptions to keep you Informed

2. Bursting the Jargon bubbles — Deep Learning

3. How Can We Improve the Quality of Our Data?

4. Machine Learning using Logistic Regression in Python with Code

For the first time this term was used by Frederick Taylor and Henry Gantt — mechanical engineers and management consultants — in the 20s of the twentieth century. Initially, this term was tied to production. In the early 80s, based on the work of W. Edwards Deming and Joseph M. Juran, a new concept of the business process was developed and the theory found support with technology. For example, completely new ways of interacting, setting goals and achieving results have emerged thanks to the advent of typewriters, copiers and personal computers. The Internet has also become one of the main factors for developing new approaches to business process management (BPM) and developing the practice of automating business processes. The theory of business process management has been around for almost a hundred years, and it continues to evolve, so it can be useful to every business to achieve relevant goals by organizing and dividing processes into different stages performed by people and solutions for automating business processes. Since the industrial revolution and the emergence of manufactories, business automation has been leapfrogging in every industry, direction, and task. Mechanisms and button controls instead of manual labor, forms for filling out documents and telephone communications — all for the good of progress. In 2016, they put a completely different meaning into the concept of automation than 100 years ago. Globalization and virtual platforms, accessible even from a mobile, like Worksection turned “mechanization of production” into “digital enterprise management” (Digitalization of business).

Understanding what business automation is and what it gives, the following positive aspects of use should be noted:

· the exclusion of routine and monotonous manual labor;

· information is processed and transmitted much faster;

· in a single information space, individual units or the entire enterprise are serviced;

· programmed reports and documents are compiled automatically;

· the convenience of operating with databases increases, and the work becomes more accurate, complete and consistent;

· Units or the whole organization is reduced to a single complex, which combines regular standardized workflow and information and regulatory framework.

It is advisable to carry out automation if there are prerequisites for the transition, or if this process is unavoidable for internal or external reasons. It can occur at different levels:

· Supporting procedures. The procedure for performing standard operations is changing, for example, creating accounting and warehouse documentation, reports, statements, introducing amendments and updates in accounting.

· Functions. It is about the formation of solutions to which uniform rules apply: the creation of warehouse requests and supply chains.

· Business. Implementation of electronic document management. A system of analytical indicators is used. In one information space for documents, executors, deadlines and control actions are assigned. Events relating to various products and projects are connected in traceable logical chains.

Thanks to this, control can be carried out at any time in order to obtain detailed information regarding the planned or done work. Available analytics, which judge the profitability and effectiveness of transactions.

Already today there are many examples of processes for the successful execution where the use of automation of business processes is appropriate. Activities in areas such as finance, human resources and marketing can be divided into various processes, some of which can be automated. For example, due to the automation of business processes for hiring employees and their adaptation at the workplace, processes for purchasing requests and orders from customers, excellent results can be achieved in improving the quality of work performed and reducing transaction costs. To get a positive effect, it is necessary not only to spend money on new equipment, but also to introduce the right changes into the workflow. In addition to hardware and software adjustment, organizational reforms are purely underway. In many ways, their nature is influenced by the goals and objectives of the company.

The management should appoint a responsible person who will cope on their behalf with the following tasks:

· analysis and formulation of tasks in a special headquarters;

· project budgeting;

· implementation of the developed measures;

· drawing up job descriptions for the team;

· transfer of information to the databases of the new platform;

· elimination of deficiencies identified during implementation;

· training.

These are just general responsibilities. There is no single list suitable for each company. In some cases, non-standard actions arise that contribute to the achievement of the intended result. Automating business processes also helps groups of people better coordinate their efforts, so disorganization can be excluded from the equation. Finally, business process automation can guarantee higher accuracy. All these advantages are the reason that more and more entrepreneurs, department heads, as well as small and large companies are using automation of business processes in their work.

All these advantages are relevant for every organization that wants to remain competitive and developed. Every business in mind has competitors striving to conquer the current market, and the approach to managing work in the company can be a decisive factor in the competition. Often, thanks to automation of processes business will succeed and will balance on the brink of existence. As already mentioned, business process management systems are designed for people who want to fulfill their role in the company as efficiently as possible and feel their contribution to the process of achieving both local goals and key goals of the company.

The point is not only that the system provides visualization of the process of achieving the goal and coordination of work, but thanks to the automation of business processes, manual repetitive and tedious work is significantly reduced. Therefore, the use of technologies to automate business processes and the new advantages that they offer to business are one of the key factors for business success in the modern rival world. Companies that rely on traditional business management styles are not mistaken; but these traditional approaches are often not effective enough to cover the vast business environment that surrounds companies. It is here that a conflict of limitations of the selected technology and new business requirements arises in connection with the need to adapt to a changing business environment. Those who want to succeed should consider overcoming this dilemma by changing the approach to business management and, if necessary, replacing the technology used. Automation of business processes is undoubtedly the right step towards growth and success in the digital age and the introduction of a business process automation system will mean positive changes and positive results.

Despite the huge number of advantages, this approach has its weaknesses. The production system will become much more complicated and there will be a lot of new elements that company employees may not be familiar with, so you will have to spend time training them. In addition, the equipment will require greater reliability. Difficulties may arise when transferring information to a new database. This procedure requires increased attention, since the smallest error distorts accounting. The constant support of specialists is important. The staff should have a programmer with special knowledge. For example, if bookkeeping is automated, then he should be aware of the current legislation in this area. It is worth noting that these inevitable difficulties are temporary and more than pay off for the benefits that automation ultimately brings. Over time, the imperfections in the information and financial bases are smoothed out, and employees become accustomed to the new work order. The main tasks of business automation are the following:

· effective support for the operational activities of the enterprise, organization of accounting and control;

· preparation of any documents for partners, including invoices, accounts, reconciliation statements and business proposals;

· quick receipt of reports on the state of affairs in the company for any period of time;

· optimization of staff costs, increasing the efficiency of the use of working time by freeing employees from routine work;

· minimizing the negative impact of the “human factor” on the most important business processes;

· safe storage of information;

· Improving the quality of customer service.

Automation can significantly improve the quality of management in a company and the quality of its product. For the enterprise as a whole, it provides a number of significant advantages:

· Increasing the speed of information processing and solving repetitive tasks

· Improving the transparency of the business and its technological effectiveness

· Increased coordination of staff actions and the quality of their work

· Ability to control large amounts of information

· Automation of manual labor

· Reduced errors and improved control accuracy

· Parallel solution of several tasks

· Quick decision making in stereotyped situations

As a result of automation of management, the head of the enterprise receives more information for the analysis of business processes in the form of detailed analytical reports and is able to manage the company efficiently taking into account external and internal indicators. Today, many sectors of the economy pay attention to efficiency from the introduction of automation of business processes:

1. Industrial enterprises. Orders, procurement of materials, production itself, packaging, delivery and reporting. Template and cyclic tasks.

2. Hazardous production and mining. Robotization and improvement of conveyor belts. Control of environmental pollution and consumption of natural resources.

3. IT companies. Task management between programmers, testers and sales people. Technical customer support and bug trackers. Admin panel of the Internet provider. 24/7 online resources.

4. Service companies. Individual services and telephone consultations requiring the implementation of a CRM system. Document flow.

5. The banking system. Maintenance of the customer database, an unbiased credit solution mechanism, online banking and protection against fraudulent transactions.

6. Analytical, legal and research centers. Monitoring and analysis of large updated databases. Statistics and sociology, search for precedents of law and insurance, meteorology and disaster prevention.

7. Security and movement tracking applications. Immediate notification of violations.

Amazon Web Services is the leader in the frequency of application — comprehensive business automation with a virtual infrastructure: from server generation to mobile applications. The second in applicability is a public offer or a virtual digital contract. You no longer need to send a fax with a signed document, scan and send copies by email. Access to the document and the history of its revisions around the 24 hours a day. Next come applications and programs for managing finances, analytics and company management. Interaction with clients is also with them: interfaces, weekly newsletters, notifications and other goodies.

Market trends

According to research and consulting company Gartner, in 2018 the RPA (Robotic process automation) market grew by more than 63%, making it the fastest growing category of enterprise software. However, it is worth noting that the total market value of $ 846.2 million remains quite modest compared to other categories of corporate software with estimates of several billion dollars. As Gartner points out, RPA is increasingly used in companies and structures with a lot of obsolete infrastructure, such as banks, insurance companies, telecommunications companies, and utilities. The largest player in this fast-growing market is UIPath, which received $ 568 million in investments last year, valued at $ 7 billion. One of the reasons it attracts so much attention is her incredible growth trajectory. In 2017, UIPath generated $ 15.7 million in revenue and increased it by as much as 629.5%, to $ 114.8 million last year. According to Gartner, thanks to this, the company took 13.6% of the market share and rose from fifth to first place. Last year, an equally significant Automation Anywhere market player attracted $ 300 million from SoftBank with an estimated $ 2.6 billion. At Automation Anywhere, revenue grew 46.5% from $ 74 million to $ 108.4 million, and the company’s market share in the RPA was 12.8%.

RPA Market Leaders

So far, all companies seem to be winning, as the RPA market is rapidly gaining momentum. In fact, the growth performance is impressive: NTT-ATT with 456% and Kofax with 256% are two striking examples. But even with these growth indicators, market share begins to break up into much smaller parts. While the RPA market is still in a development phase, at some point, fragmentation at the bottom of the market may lead to general consolidation as leading companies try to buy back an additional market share. Despite the fact that the RPA market is still small, it is growing steadily. By 2020, RPA spending will reach $ 1 billion (according to Gartner’s forecasts), and by then 40% of large organizations will already be using RPA tools. Add to this the demand for AI and ML that integrates with RPA solutions — and you will have an idea of ​​the growth prospects in this direction. They are endless. Automation of workflows will change the understanding of the next phase of digital transformation, as this automation provides other benefits for customers arising from higher quality and reliability of services, reducing the time of production and creating attractive offers. In addition, what does Gartner predict for the prospect of economic growth?

1. AI will turn RPA into IPA

The increasing use of artificial intelligence (AI) is already a reality, and this is happening, not least due to the RPA. AI is becoming increasingly important, and RPA will integrate more closely with AI, which will lead to the creation of a completely different technology — intelligent process automation (IPA). Estimates show that in the coming years more than 40% of enterprises will create high-tech digital workers based on IPA. The global RPA market is expected to reach $ 1.7 billion in 2019 and $ 2.9 billion in 2021.

2. Centralization will conquer chaos

In 2020 and in the coming years, enterprises will intensively use automation centers in order to comprehensively solve the problems of change and risk management, control, audit, and security. These trends will lead to increased requirements for corporate information regulation (IG, Information Governance), because of which enterprises will be forced to increase investments in the creation of automation centers built on the principles of unified software.

3. Sales of RPA licenses will fall, and consulting revenues will grow

In the coming years, RPA license sales will decline and license sales will not be the main source of revenue generation. An increase in the number of RPA manufacturers will lead to an increase in free pilots, which will affect the volume of licenses sold. On the other hand, it is expected that consulting companies will increase revenue, the main sources of which will be the design and documentation of processes, as well as pilot implementations.

4. Autonomous robots give way to non-autonomous

Non-autonomous (attended) robots, i.e., robots that operate with the participation of humans, will be used in 2019 more and more, and will replace autonomous ones, i.e., those that perform their work without operational interaction with humans. According to Guy Kirkwood, UiPath chief evangelist, the share of industrially used autonomous robots, previously 70%, will be 54% in 2019, and will go down to 50%. This forecast is confirmed by the experience of many industries. Thus, we have a clear signal that customers are ready to implement even more non-autonomous robots.

5. Government agencies will begin to apply RPA more widely

The days when applying RPA was a privilege for global and private companies have passed. In the coming years, the number of RPA applications in budgetary bodies will increase. The scope of application will expand more and more as government authorities see the impact of the RPA on the quality and responsiveness of their services to the public.

6. The involvement of employees in the use of RPA will increase

As companies adopt emerging technologies, the number of employees using RPA in their daily work will increase. This will lead to an increase in the involvement of employees in the digitalization of the processes of their enterprise and business.

7. Maximum attention to working with unstructured data

RPA providers will continue to enhance the functionality of working with unstructured data, since extracting information from them provides many advantages. Arrays of texts, images, e-mail — this is a gold mine containing important information, and many organizations are well aware of how to capitalize on this. This forces RPA vendors to incorporate OCR and AI technologies into their products in order to extract the necessary data from invoices and orders, and then transfer this data for robotic processing. It is believed that this is very effective and radically reduces the return on investment.

8. The labor market is awaiting change

Automation affects staffing requirements and employment. Forecasts confidently show that in the medium term at least half of the jobs can be eliminated thanks to the introduction of automated processes. Nevertheless, recent trends revealed that employees can interact with robots as part of automated processes, and that the digital economy will stimulate the creation of jobs for designers and other specialists in the field of RPA and AI, whose tasks will be to solve business tasks, improving user interfaces of robots, setting up chat bots, etc.

9. Investments required to establish centers of expertise

Experts predict that by 2020, more than 40% of enterprises will have automation centers and will invest in centers of expertise or centralized coordinating centers. Centers of expertise will be responsible for the analysis of automated technologies and identify from them the most relevant business tasks facing them, while introducing the best business practices, integration solutions and common technical policies.

10. Dissemination of chat bots

Interfaces for voice communication will become more common and accessible, and this will mean another wave of spreading RPA. In 2019 and in the coming years, we will witness that products that support voice communication with users will become the industry standard. The distribution of chatbots will stimulate the development of cognitive technologies, which, in turn, will actively influence the creation and development of the corporate knowledge bases needed by voice robots to formulate complete and informative answers to clients.

Prospects

More than 150 years ago, the French writer Victor Hugo briefly and accurately described the reality that today is faced by business in an attempt to assess the prospects that automation of business processes brings and how it affects the business. “The future has several names. For the weak, it is impossible; for the fainthearted, it is unknown; but for the valiant, it is ideal.” (Victor Hugo, Les Misérables). With the proliferation of smartphones and devices with the ability to access the Internet, new opportunities and new problems appear. There are ways to handle tasks that were previously performed manually. Automation of business processes based on artificial intelligence today uses data and analytical information for effective customer service. The issue of process automation is gaining increasing relevance. Technical and digital revolutions in the world affect the state of the labor market. Human resources specialists say that such an active innovative transformation can lead to the disappearance of a number of professions.

Indeed, in professional circles there is an opinion that the automation of processes will “eat up” some professions or even lead to mass unemployment due to the disappearance of some of them. However, there are statements that these are fictitious opinions that do not correspond to reality. It turns out that if we automate certain business processes, we will create a pool of unemployed people. People who are not just unable to get a more prestigious job, but who will not find application for their professional skills. It may seem that there is some truth in this, but this is only with a first, superficial look at the problem. Automation of business processes is a very correct way to improvement and development. Ask yourself what are we trying to upgrade? We are talking about tasks that are cyclical, routine, not requiring a creative and intellectual approach. Nevertheless, a person is not a robot; it is a creative person with his own experience and outlook on life. He wants to realize these creative needs, only then he can live in harmony with himself. Optimistic experts consider the automation of business processes as a way to prosperity, the evolutionary improvement of society and living standards. The fear of being left without work arises from the incompetence of a specialist, and uncertainty about their knowledge, skills and abilities. When a specialist is not able to think strategically, when he is used to acting like a robot, then yes, unfortunately, he will remain overboard. Automation of processes is a natural stage in the development of society. People who act and think in a stereotyped way will be forced to think about their own effectiveness, they may have to learn new professions and learn new skills.

If we talk about business, then automation will allow the most high quality and quick to perform routine actions. However, do not forget that doing business is also a creative process. Sometimes a specialist needs insight, the ability to study and analyze the situation, make the right decision based on his professional experience or even intuition. At such moments, no smart system or super-complex program can replace a human being. Do not forget that every system has a professional who monitors its condition. He prescribes and calculates the algorithms of the program and sets it up for a specific business. If you automated a business process, this does not mean that it will work without failures. You will have to monitor the performance of the system and constantly maintain it. Automation of business processes is a tool that helps to develop a business, rather than destroy it. Thus, in 5–10 years, we will become witnesses of business process execution scenarios in which robots (RPA programs) will independently receive source data, including, if required, directly communicating with a client, counterparty, colleague in a chat or voice . Having received the data for processing, the robot will be able to independently decide which process should start processing it, and if the robot does not know the data processing scenario, it will be able to ask its human colleague for clarification and receive an answer, remember how to develop such scenarios in the future. RPA will be primarily used in routine work. However, in the future, technology will be enhanced integration with artificial intelligence. This will allow robots to make independent decisions, which brings singularity perspectives closer.

Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg

Sergiy Golubyev (Сергей Голубев)

Crynet Marketing Solutions, EU structural funds, ICO/STO/IEO projects, NGO & investment projects, project management, comprehensive support for business