Best practices for getting high ROI?

2021.11.30 12:17 EselSchwanz Best practices for getting high ROI?

I know that things are hard to predict, but I'm about to get into the REI game and have ~$500k cash to throw down. I'm debating on my options, and want to know your opinions on the best ways to maximize ROI. Leverage with debt? Single families? Multi-family? Use cash only?
I've heard that the benchmark is to make 20% every year from your money, so it doubles every 5 years. That sounds pretty ambitious to me. How is this achieved through REI?
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2021.11.30 12:17 Quackquack1337 Why I am bullish on Vectorspace AI (VXV). The first AI dataset aggregator company in the crypto market.

Note: I’m sorry if this post is a drag but it is still the tip of the iceberg of what The company is doing so, I would look beyond this post for clarity. If I’ve made any mistakes please correct me, I’m still learning.
Market cap of Vectorspace AI: $300-400 million.
Price per token: $8
The reason I mentioned the MC first is to remind you as you read in my post if the company achievement/progression is representative of this market cap. Also, the price per token will be of relevance as you read on.
What is Vectorspace AI?
Vectorspace AI is a dataset aggregator company that uses a correlation matrix to generate datasets based on any event, trend, etc, on any topic, subject, using Natural Language Processing/Understanding (NLP/NLU) and Machine Learning (ML). These datasets can be generated in the millions in real-time (billions of different possible datasets that can be generated) which allows for the customers to identify hidden relationships with the data allowing for information arbitrage and generation or protection of alpha. This would be referred to as “smart baskets” (basket of information to be used to find hidden connections/correlations) E.g. Hedge funds would use smart baskets containing stocks to monitor stock prices - if Vectorspace AI finds that when X stock price rises/falls, Y and Z also fall. The fund manager can then react before that happens to avoid losing money. The company puts more emphasis on its use in the space bioscience and financial market sectors.
Vectorspace AI leverages blockchain for on-chain data lineage, provenance, governance, validation, and security. The tech behind how Vectorspace AI uses NLP/NLU and AI to generate these datasets is patented. The VXV token is central to all the datasets.
Tokenomics
Total Supply: 50 million VXV
Circulating Supply: 35.2 million
No ICO. Trying to be as SEC compliant as possible, since they ultimately wanted be listed on NYSE and NASDAQ.
The company has stated they want to buy back the VXV from the open market to control float or also known as market manipulation to ensure whales who do not serve the companies best interest do not influence the token price. The consensus is that they will buy back tokens up until there is only 10 million left on the open market to allow for the company to use it for data set access. The Potential Value of VXV is now multiplied by 3.5X if you consider the supply after Buybacks. They have already initiated buybacks but are going to be full-fledged once consequential revenue starts flowing.
If you’re wondering how the buybacks will be funded, the team has statedta part of their revenue model which is 50% of the subscription value will be used to buy back the VXV from the open market. E.g. if a company pays 100K per month for certain datasets that are updated at a certain frequency, 50K will be allocated for buybacks.
The CEO has stated the VXV token price one day will be in the quadruple digits naturally and organically. He has also stated that they plan to retain the value of the locked-up VXV and part of this is to sell some of it in the quadruple digits.
what is the Utility of the Token?
I am going to copy and paste the Utility of the token by the admin of the official Vectorspace Telegram since the explanation is the best, one that I cannot rephrase.
“The utility is vast, let me drop you the technical wall of text:
1. Design and generate datasets
2. Machine-to-machine transactions using VXV inside a specialised exchange for datasets where datasets are bought and sold similar to a crypto or stock exchange.
3. Access and distribution of datasets based on the VXV wallet-enabled API key used by customers to access Vectorspace AI datasets from revenue distribution partners such as S&P Global Market Intelligence, Bloomberg, Neudata, CloudQuant, or Microsoft Azure.
4. On-chain data provenance, lineage, validation, and governance: If you don't know where your data came from, you're flying blind. The VXV token tracks all changes to datasets by assigning immutable ownership to each change in the dataset.
5. Retail subscriptions: Access to thematic baskets, smart baskets for stocks and cryptos
6. VXV wallet-enable API:
A. Based on the amount of VXV in a wallet, access to hundreds of tiered service levels offered by the VXV dataset platform for greater accuracy, precision, or profit.
B. Based on the amount of VXV in a wallet, access to hundreds of API parameters enables greater accuracy, precision, or profit.
7. Optionally, the VXV wallet can be enabled as a wrapped asset that can be traded on a separate exchange
Basically, the value of datasets is directly tied to the token. It is built to be scarce.”
-Cooper
A single High-value dataset can be worth millions and this is all tied to the Token.
Partners/Collaborators
Vectorspace AI has one of the most established collaborators and partners in the crypto field. They are partnered and or in collaboration with Microsoft azure/SpaceX, CERN, S&P500, Morningstar, U.S. National Library of Medicine, U.S departments of Energy, University of City London (UCL), Imperial College London (ICL) (Classed to be in the Top 20 Universities in the world) and recently are looking to collaborate with NASA in language modeling relating to their gene lab platform and their biospecimen sharing program.
These are billion-dollar businesses in the pipeline for Vectorspace AI so they aren’t following ordinary revenue, rather consequential revenue from these partners/collaborators which is the quickest path for a profit-driven company. This can be further attested by the CEO’s statement “We are not focused on interactions with multi-million-dollar companies, rather billion - and trillion-dollar companies. This also includes trader bases interested in trading VXV.” You may be wondering why nobody has heard of These billion-dollar companies talking about Vectorspace AI. Well, there has been no PR from them as of yet. PR from ICL and then UCL after are the first to come as stated by the CEO which will start the chain reaction with the other partners/collaborators, such as Morningstar and S&P500, (who are both revenue distribution partners) waiting for a write up from ICL regarding the use case of datasets in accelerating the discoveries in the context of space bioscience to verify Vectorspace AI is achieving progress with their scientific collaborators.
Kasian Frank conducted a giveaway of 100k in VXV or ETH for a crypto with the same or higher level of scientific collaboration in the field of human longevity and bioscience compared to Vectorspace AI. Nobody won.
Competitors
There are no competitors in the crypto market. But, in the traditional market, the closest and largest competitor is Palantir that also uses NLP/NLU for its datasets. But it doesn’t produce NLU correlation matrix datasets used to generate clusters from unsupervised data using the Vector space methods. They also do not use blockchain for data lineaage, security and province. The market cap of Palantir is around 40bn. The fact that there are no competitors in the crypto market should raise some eyebrows.
The team
You’re probably wondering who is behind this company. They are a team comprising of people with a deep background in science, technology, and the financial markets, and are veteran software engineers, scientific and technical founders who work in the area of specialized algorithms in ML/AI for Life Sciences and the financial markets.
The CEO is Kasian Franks. I'll paste his career history to show you his credentials and his capacity of being CEO.
“Franks worked as a software engineer and product developer specializing in pattern matching algorithms and tools for companies and organizations such as Sun Microsystems, Oracle, Motorola, Tivo, mPower (acquired by Morningstar), and X-Mine (Sold to NEC Japan, now called Biocompass) . From 2002-2005, Franks worked as a Genomic Research Scientist at the U.S. Department of Energy's Lawrence Berkeley National Laboratory (LBNL), Life Sciences Division, alongside Raf Podowski, and Connie Myers, where he developed the technology behind SeeqPod. Franks began to study biomimetics along with pattern matching in data and nature. This led to 5 patents in the area of search and discovery. Later he went on to start SeeqPod Inc. a music search, discovery, and pattern matching company in which the U.S. Department of Energy, along with Lawrence Berkeley National Laboratory, holds a 5% stake. Under Steven Chu and Lawrence Berkeley National Lab, Franks and SeeqPod won a 2008 R&D 100 Award for biomimetic search engines.”
He conducts weekly conference calls where all updated information about company progress and achievement is deliberated. This type of deliberation is unseen from many other crypto projects and shows the connection the CEO has with its retail investors. Kasian and his team are active on Telegram, answering questions on chat and in the conference calls for further clarity.
Helpful links:
VXV deep dive due-diligence
https://medium.com/@492727ZED/a-deep-dive-on-the-cryptocurrency-vectorspace-ai-vxv-5fae8f53c11d
VXV Deep dive simplified
https://twitter.com/dcrypto25/status/1378410576754450432?s=21
VXV Community FAQ
https://vxvfaq.readthedocs.io/en/latest/
Conference calls ( Conference call notes can be found on the telegram)
https://vectorspace.ai/conf-calls.html
Revenue Model
https://twitter.com/crmtka/status/1428495489264017408?s=12
TL;DR
A company that generates correlation matrix data sets for Hidden Relationship Detection in Data. Uses NLP/NLU and AI algorithms to generate them. Focus is in space bioscience and in the financial market
50 million supply, 35.2 million circulating and the company is in the process of conducting buybacks, drying the supply to 10 million VXV.
Token is central to the datasets and are designed to be scarce and correlates to the value of data sets.
They have a myriad of partners/collaborators, unmatched such as Microsoft azure/SpaceX, CERN, S&P500, Morningstar, U.S. National Library of Medicine, U.S departments of Energy, University of City London (UCL), Imperial College London (ICL) (Classed to be in the Top 20 Universities in the world) and recently are looking to collaborate with NASA in language modeling relating to their gene lab platform and their biospecimen sharing program.
They have no competitors in the crypto market, but some in the traditional, biggest being Palantir but Vectorspace method of dataset creation and culmination are different, as well as how the data is stored, on a blockchain.
The team is built of veterans from all aspects of science, technology and financial markets.
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