Category: Future

Understanding Cryptocurrencies: Restoring fiscal discipline and growing economic freedom

Background

Friends and family will know that I’d gone down the ‘crypto rabbit hole’ in 2020, and I acknowledge I have positive bias for cryptocurrencies.

When we’d discuss topics like cryptocurrency, Bitcoin or Ethereum many times the comments were quite negative:

  • Cryptocurrencies are scams and many projects are fraudulent
  • Cryptocurrencies are pure speculation and promoted by individuals with vested interests.
  • The user experience is clunky, it’s ‘tech by techies’
  • As a Western person, it’s difficult to see what cryptocurrencies are for..

Meanwhile, while I see much validity in the criticisms, I was also disappointed, as it felt like a chasm of understanding.

My perception when I look at the current financial system is not flattering:

  • how the current financial system is skewed towards already rich asset owners
  • how wage earners have been basically shafted for the past 50 years
  • Every adult KNOWS that government fiat money is printed out of thin air, and many just accept the price gyrations and inflation that follow
  • Powerful intermediaries like banks, Wall Street use their power to cozy up to government and regulators, while curtailing innovation.

Just two brief charts here to illustrate the scope of the problems:

Chart 1: Networth of the top 0.1% of society tracking money printing:

Chart 2: Net Productivity and Average compensation trending closely 1948 to 1971. After 1971 productivity kept increasing, but Average compensation lagged badly.

Since 1971 we have seen the link between productivity growth and wages get severed. Productivity growth occurs when on an aggregate level in society, in companies we become more efficient, we can do more with less, whether it’s from working smarter , automating more processes etc. As we see from the chart this relationship used to track almost 100%. So some-one other than wage-earners must then reap the benefits of productivity growth.

I believe there is something fundamentally wrong in the current economic, financial system, and I intend to show in the book how what we consider ‘money’ is at the root of the problems.

While cryptocurrencies are no panacea, I see tremendous promise for cryptocurrencies:

  • how crypto can be part of solution to help curtail money printing, restore fiscal discipline
  • how crypto can bring economic freedom and opportunities around the world
  • how crypto can help decentralize and guide AI
  • how crypto can help skew the incentives towards wage earners again
  • how crypto can finally reduce the power of intermediaries with non-custodial, peer-to-peer technology.

‘Understanding Crypto’ book

So in March 2024 I set out to help bridge this divide with a book that covers the main aspects of these issues. The goal of the book is to try to give a smart person an overview of the entire space. If some-one is very familiar with cryptocurrencies they should recognize a lot material, but hopefully also gain new perspectives.

Right now the book is split into four parts:

Part 1: Exploring the history of money, history of financial systems and defining what is money. Included are definitions of relevant concepts such as fractional reserve banking, quantitative easing and inflation.

Part 2: A critique of the current financial system arguing it benefits asset owners and governments at the expense of wage earners and savers. Included are charts and statistics from around the world to support the arguments.

Part 3: Covers the foundational cryptocurrencies – Bitcoin and Ethereum – in detail, and provides example use cases in Finance, Social, Culture etc. It also covers the moral case for cryptocurrencies, and explains new concepts such as NFTs, DAOs and what the hell are LPs?

Part 4: Explores future scenarios of adoption and how cryptocurrencies play into the global currency arena, with some possible future scenarios.

I would be thrilled if you gave your email and joined me on this journey to ‘Understanding Crypto’ better.

Lessons from “GenAI for developers” course by Google

I just finished the Google Cloud Skills path called “Generative AI for developers” and I wanted to write down some of the main lessons and impressions I had.

Overall I think the lessons were well-thought out, aimed at an intermediate audience like me, and I learned a lot about the various Google tools (here’s my cert). However there were also some technology snags along the way that detracted from the experience.

Main topics of the course

The topics in the course are the following:

  1. Introduction to Image Generation – with an Intro video + quiz re: how diffusion models are used for image generation
  2. Attention Mechanism – Intro video + quiz re: how attention mechanisms work, and can be used for a variety of tasks from text summarization to translation
  3. Encoder – Decoder Architecture – Intro video, Jupyter Lab (Python code) walkthrough + quiz re: how encoder – decoder works, and can be used in sequence to sequence tasks such as text summarization, Q&A, translation etc
  4. Transformer models and BERT – Intro video, Jupyter Lab walkthrough + quiz re: main components of the Transformer architecture and the self-attention mechanism
  5. Create Image Captioning models – Intro video, Jupyter Lab walkthrough + quiz re: how do use deep learning to create Image captions
  6. Introduction to Generative AI Studio – Intro video + quiz regarding Vertex AI for customizing and prototyping Generative AI models.
  7. Generative AI explorer – A set of labs using Jupyter Notebooks to explore different GenAI models, and try out prompting tools and various parameters (see below)
  8. Explore and Evaluate Models using Model Garden – Using Vertex AI to try out different Foundation Models, tools and parameters (see below)
  9. Prompt Design using Palm – How to design good prompts, setting restrictions and interacting with Palm 2, that has detailed reasoning, language and coding capabilities (see below).

Generative AI explorer – with Vertex AI

The lessons that I liked the most were using different examples to teach how to use the models in different use cases. Everything was run on Google  Vertex AI – which is a unified AI platform, that allows you to:

  • Select from 100+ foundation models in the Model Garden
  • Try out code snippets with JupyterLab in the Workbench
  • Access every model with a simple API call
  • Train and deploy models to Production

Text generation examples

You can test any of the Text generation models by selecting the model in the top right dropdown. You specify the context (prompt) for the model, and provide input/ output examples if you prefer. Giving no examples is called zero-shot prompting, giving a few examples is called few shot prompting, which for most use cases is the better alternative.

Pressing the ‘Get code’ button also top right gives you the code snippets you need to connect to the selected model in your Google Cloud project:

Chat model examples

Under the Language models you can customize & test the foundation models e.g for customer service or support use cases. Some key parameters include:

  • Temperature: A lower temperature of say 0, 0.1 are better when factual, true or false responses are required. A higher temperature say 0.6 to 1 will give more imaginative answers, but will also increase the risk of the model hallucinating.
  • Token limit: Determines the maximum number of text output from one prompt, with one token equal to roughly four characters.
  • Safety Settings: You can define how strict the model responses are regarding potentially harmful content, such as hate speech, sexual content or harassment.

Codey – code generation, chat and completion

You can select between different code engines based on your use case:

  • Codey for Code Generation (6k or 32k token limit). Takes your natural language input and generates the requested code – example below:
  • Codey for Code Chat (6k or 32k token limit) – model chatbot fine tuned for helping with code related questions.
  • Codey for  Code Completion – model fine tuned to suggest code based on the context, code already written. 

Exploring the Model Garden

In the Model Garden you can try out different Foundation models such as:

  • Gemini Pro – e.g. for text summarization, text generation, entity recognition, sentiment analysis and more.
  • Gemini Pro Vision (Multimodal) – e.g. for visual understanding, classification, summarization and processing visual inputs such as photos, video, documents, infographics etc.
  • Claude 2: a leading LLM from Anthropic – similar to Gemini Pro
  • Llama 2: Open Source LLM released by Facebook / Meta that is fine-tunable to your use case/ domain.

There are over 100+ models to date, however the main drawback of note is that the Fine Tunable models (except for LLAMA2 and Falcon) are mostly just related to classification or vision detection/ classification.

Prompt Design

Some of the prompting lessons here:

  • Be concise
  • Be specific and well-defined
  • Ask for one task at a time 
  • Improve response quality by including examples (few shot)
  • Use a Dare prompt  – Determine Appropriate Response (DARE) prompt, which uses the LLM itself to decide whether it should answer a question based on what its mission is. Meaning that you send the regular prompt / context first, followed by the Dare prompt to verify that the output generated matches the mission.

Most of the example Jupyter Notebooks in the course can be found on Github here.

Conclusion

Overall I think the course teaches the material well – I liked the hands on Jupyter Notebooks better than the video + quiz sections as it’s easier to learn with concrete examples.

I’m most impressed by is the completeness of the Google Vertex AI platform, and I feel that I have a good basis to use the platform independently today.

Generative Agents: Five Bold Examples of AI Revolutionizing Product Development

Image created with Canva, no generative agent used here :-)
Image created with Canva, no generative agent used here 🙂

(first published on Linkedin)

As we are seeing ChatGPT become more widely used, companies of all sizes must ask themselves how do they adapt their products and their competitive strategies in this new world? 

To recap – ChatGPT by OpenAI is a generative agent that is designed specifically for generating text by predicting what comes next in a given sequence. As a generative agent, ChatGPT can create new content, write code, carry out conversations, and even provide assistance in various tasks, depending on the context and the data it has been trained on.

Generative agents are poised to redefine product development, offering unmatched creativity, efficiency, and innovation. Here are five compelling examples of how these AI-powered systems are transforming the way we create and consume products:

Personalized Products: AI-Driven Sneaker Revolution

1.Generative agents will enable brands like Nike or Adidas to analyze user preferences and create customized sneaker designs tailored to individual tastes. These one-of-a-kind shoes will foster deep connections between consumers and brands.

Rapid Prototyping: Hyper-Iterative Rocket Design

2. Companies like SpaceX can leverage generative agents to rapidly generate multiple rocket designs, streamlining the prototyping process, and pulling our sci-fi dreams closer to the present.

Sustainable Design: Eco-Friendly Furniture Evolution

3. Generative agents can help IKEA analyze material data and environmental impact, creating innovative designs that minimize waste and promote sustainability. These eco-friendly products will resonate with environmentally conscious consumers, bolstering IKEA’s brand reputation.

Democratization of Design: Small Business AI Explosion

4. As AI systems become more accessible, Etsy’s small business owners will harness the power of generative agents to create professional, high-quality products. This democratization will unleash a wave of innovation and competition, transforming the online marketplace.

Metaverse Product Sales: The Ultimate Autonomous Agent Experience

5. Generative agents will bring the metaverse to life, creating autonomous agents that interact believably with users for product sales. Imagine the next generation of virtual real estate, where AI-driven real estate agents engage with potential buyers, personalizing the experience and providing valuable feedback to sellers.

Generative agents are set to transform the product development landscape with their AI-powered capabilities. Do you agree, disagree? Pls let me know in the comments.

The bull case for Ethereum

As I’m getting frequent questions from my ‘non-crypto’ friends re: why I like crypto and ETH in particular, here is my take as it stands today (March 2022).

TLDR version

  • The coming POS upgrade will enable large institutions to invest as the Proof of Work / ESG complication is removed.
  • The supply of ETH will drop in June from 12,000 ETH per year to 1,200 ETH per year which will all go to the holders of ETH.
  • Staking rewards will rise from the current 5% pa to 10-15% pa
  • ETH is growing like a rocket, with usage up 10x just in 2021
  • Companies like Consensys will hold ETH on their balance sheet
  • In the aftermath of Canadian truckers and Russian sanctions, the importance of de-centralization will increase, working against ETH challengers like SOL, FTM, Terra etc.

Background

Ethereum is the first ‘programmable blockchain’ with the whitepaper written by Vitalik Buterin in 2014, and the blockchain was launched in 2015. The genesis block was mined, so Ethereum today is a Proof of Work (PoW) blockchain. 

Ethereum use cases

Ethereum enables a vast array of ‘decentralized applications’ or ‘dapps’ to be launched on top of it. The dapps are essentially smart contracts (programs) that run on Ethereum, and fees denominated in Ether (the native Ethereum currency) are paid (currently to PoW miners) to record the transactions on the Ethereum blockchain.

Dapps cover a wide array of use cases:

Decentralized Finance / DeFi – where the dapps today provide almost all of the services you can find in traditional finance (trad fi) – such as lending, staking, trading, options, etc. Yields on crypto assets in DeFi can often be very lucrative, with ‘stablecoins’ (e.g USDC – that is US Dollars on a Ethereum) you can earn yields of 8% to 19% p.a. with no fluctuating crypto prices. What is the interest you are getting in your current bank account?

The most popular DeFi dapps include Uniswap, Sushiswap, MakerDao etc. Just to be crystal clear here these are all autonomous programs that “live” on the Ethereum chain, and carry out all those functions with no human intervention. This is why banks are worried about ‘disintermediation’ from DeFi- meaning that banks would be put out of business.

NFTs – or NonFungible Tokens enable creators or communities to prove digital ownership of an asset, membership or status. The use cases range from:

  • Music – e.g. music artists being able to offer their fans different levels of interaction – where die-hard fans could buy access to a VIP lounge after a concert, or access to a discord channel with early releases of the artists more experimental music. This enables a ‘tiered pricing model’ which is much more favorable for the artists and fans.
  • Sports – sites like NBA Top shots allow fans to collect, share & trade ‘moments’ of their favorite stars (like digital trading cards).
  • Community via POAPs – Proof of Attendance Protocols enable you to show on your profile attendance to physical events, making it easier to connect with likeminded people.
  • On-chain ownership: eventually the ownership of your house or your car will be an NFT that you hold in your digital wallet. You will be able to borrow from your house (home equity loan), rent out your car for a weekend (like Turo) or even create an NFT out of the blog post or video you created.

Gaming – the fundamental shift of being able to own the artifacts, loot in the games give players a stake, a reward that was not possible before. Then being able to trade those artifacts for ‘money’ has given rise to the ‘play to earn’ phenomenon – shown eg by Axie Infinity. Granted there is pushback in the gaming community against cheap monetization, so as always the formats will be iterated on.

DAO’s – decentralized autonomous organizations are new ways for communities to organize themselves, and distribute decision making to the community. Quoting from the Bankless DAO article:

“What sets DAOs apart from all previous organizational forms is their flat, decentralized structures and absence of central planning. DAOs share a treasury and raise equity capital through the issuance of their own token, attracting anonymous investors and workers who believe in its mission.

The transparent nature of the blockchain means that all organization’s activities are managed on-chain and anyone can audit its smart contract codes, giving both investors and workers greater transparency into the inner workings of the organization.”

Bankless, Ryan Sean Adams

IMO DAOs are at the earliest stage as they have the promise of re-organizing how we work, how we form communities, how we deliver public goods (see GitCoin), how we do politics (see Andrew Yang on Lobby3).

Move from Proof of Work to Proof of Stake

Ethereum will move to from the current Proof of Work consensus mechanism to “Proof of Stake” within the next 3 months in a process called the ‘Merge”. This will bring the following benefits:

  • Energy consumption to secure the chain will drop around 99.8%. Instead the chain is secured by having ‘node validators’ stake their ETH (in a smart contract). 
  • The cost of attacking the network will increase, and enable further de-centralization down the road.
  • In POS – for every new transaction validators can be called randomly to validate whether a particular transaction is ‘valid’ or ‘fraudulent’. If a malicious actor were to send in fraudulent transactions and your node were to validate the transactions as ‘valid’ – the Eth staked on that node would be ‘burned’ (rendered useless). Instead the nodes which validate transactions correctly are rewarded in ETH – currently on the order of around 5% per annum.

Ethereum Scaling 

TLDR on scaling: Combined the POS / Layer 2 and Sharding solutions could take Ethereum from the current 15 tps (transactions per second) to 100k tps.

Scaling Ethereum will happen mainly via so called ‘zero knowledge’ roll-ups, or Layer 2 solutions. There are multiple Layer 2 solutions that are built today – but not yet in wide-spread use which enable faster, cheaper transactions.

Different variations exist such as:

  • Side chains like Polygon or Gnosis chain
  • ZK-roll-ups – such as ZkSync or Starkware enable batching of transactions so that the cost /speed of execution can be reduced by 100x or so. The key is that the code is ‘EVM / Ethereum Virtual Machine’ compatible so it can be deployed very quickly / instantly by a ‘dapp’. If the Dapp has been developed for Ethereum, it would in many cases work ‘out of the box’ with the Zk roll-up as well.
  • Optimistic Roll-ups such as Arbitrum, Optimism, Metis – bundle transactions as well, and reduce transaction fees by about 50x-100x as well. They have been in use for about one year, but are still experimental. The optimistic roll-ups “optimistically” assume that all transactions are valid during processing, but only validate each transaction later on. This means that it takes up to 7 days to move your funds off an Optimistic Roll-up.

On-chain scaling of the Ethereum main-net will happen via a process called Sharding, which splits the main-net into 64 different ‘shards’ to spread the load. This will happen sometime after the “Merge” – I’m not going to venture a date here… :-).

Ethereum valuation

With staking earning ETH holders ‘dividends’ – Ethereum can be evaluated using business valuation methods. Taking into consideration Ethereum’s growth rate in the past few years and the growing network revenue that will accrue to ETH holders – analysts have estimated ETH to be valued:

And yes there are risks to these investment theses – competitors such as Solana, Polkadot, Terra, there are geopolitical issues, technical unknowns, malicious actors etc so DYOR. (***This is not investment advice***)

The recap

IMO Ethereum is setup for multiple events that I do not think ‘the market’ has fully priced in, such as:

  • The coming POS upgrade will enable large institutions to invest as the Proof of Work / ESG complication is removed.
  • The supply of ETH will drop in June from 12,000 ETH per year to 1,200 ETH per year which will all go to the holders of ETH.
  • Staking rewards will rise from the current 5% pa to 10-15% pa
  • ETH is growing like a rocket, with usage up 10x just in 2021
  • Companies like Consensys will hold ETH on their balance sheet
  • In the aftermath of Canadian truckers and Russian sanctions, the importance of de-centralization will increase, working against ETH challengers like SOL, FTM, Terra etc.

Thanks for reading,

Oskar

Future of money in 2022 and beyond

Reading the very good series and predictions about the future of money on Coindesk inspired me to put some of my own reflections down.

These points are drawn from sources like Balaji Srinivasan (here and here), Raoul Pal (here), Robert Breedlove (here), Alex Gladstein (here ) and many others.

If you peruse those talks and videos you will notice that a lot of them are about history, as in order to understand the present you have to understand your history. In order to predict the future, you have to understand the present. Underlying themes driving these predictions include A) Demographics, eg Fourth Turning B) Things can stay the same much longer than what any of us expect (see Japan) and C) Software is eating the world (ref Marc Andreessen).

There will be a monetary battle, a test of wills, between the following forces – Authoritarian capital (Eg China), Western Fiat (Fed, ECB) and Crypto capital. The Authoritarian capital requires the citizens to Submit as it is the Legal authority. Fiat capital / central banks claim to know what is best for the citizens, and the message is ‘sympathize’ so the CBs will spend greatly in order to keep the current house of cards going. Crypto capital is wild, volatile and offers get rich schemes interwoven with real world utility.

Fiat money central banks will, due to the economics of the fiat system, need to continue their QE infinity, but they will mask the fact that the system is broken in a language of social spending, of taking care of the inequalities in society. My prediction is that we will have some type of UBI (Universal Basic Income) or monthly Tax Credit scheme in most western countries within 5 years.

Unfortunately this will lead to further asset price inflation (as it has for the past 20 years) and secondarily to commodity and wage price inflation, so the main effect will be that asset prices (stocks, houses) continue to compound at 10-15% per year in fiat money terms, but measured against M2 money supply staying flat. Houses will cost double from now in 5 years and equities will have doubled as well, and we will still wonder who the hell buys any at those nose-bleed levels. The answer is TINA – There Is No Alternative.

The UBI / Social spending will be carried out via CBDC – Central Bank Digital Currencies that can be spent via your phone / device, as this will be cheaper for merchants than credit cards. Government can track the spending in real-time, can tax your income and purchases (via VAT) in real-time and merchants can provide discounts in real time based on your location, purchase history, memberships etc. Consumers will as usual have chosen convenience over privacy.

There will be some wage pressure due to the “Great Resignation” as especially Baby Boomers are feeling better about their finances while uncertain about their health, meaning there will be fewer job applicants for many open positions. My prediction is that wage inflation will outpace government CPIs (CPIs say 3-4% annually, wages 4-5%) while both will lag asset price inflation. But since asset price inflation ‘doesn’t really count’, government economists will still be wondering why the rich get richer and the poor get poorer. How much actually in ‘real terms’ will be spent to equalize the income/wealth divides will still depend on election results and politics.

Today (Dec 12th, 2021) the combined market size of Crypto is around 2.3 Trillion, Global Equities are around 120 Trillion, Global Government, Household and Corporate Debt around 300 Trillion, and Global Real Estate around 330 Trillion, so we can call the ‘real world’ financial markets a nice round 750 Trillion.

The ‘Defi Matrix’ will be the Crypto markets ‘eating the financial’ world in the 2020s.

In the words of Balaji Srinivasan :

The DeFi matrix may be to the 2020s what the social graph was to the 2010s. Once every asset can be represented in a digital wallet – bitcoin and ethereum, yes, but also CBDCs , stocks, loans, bonds, etc. – all these billions of assets will trade against each other every second of every day around the world.

This is not inevitable because I love crypto, and blockchains will create some utopian society ruled by empathy, code and crypto bros / babes. This is because I think most main-stream thinkers, politicians, businesspeople etc severely underestimate the power of:

A) how blockchains can enable Trust, an objective Truth to flourish in business and personal transactions. This is because Blockchains are a revolution in Accounting – Triple Entry Accounting (Debit, Credit, Public). Imagine for example every invoice sent, on a chain where the payer / payee can instantaneously verify its status, pay the invoice, confirm receipt and dispute it if needed.

B) Linux systems today run about 50% of the worlds webservers, and Linux is about 99% built on free labor, open source. Now imagine a similar system except that the people building the system can be rewarded in the token of the system, they gain as the token appreciates in value and the gain in the token allows creation of more utility, which brings more users, more value – creating a virtuous cycle. * How some-one like Charlie Munger who’s a student of human incentives can not see the benefits in this is beyond me..

C) The utility and financial rewards available in the Crypto / Defi markets will slowly drain away capital and resources from the TradFi world, and while the market size, number of participants in Crypto increases, the volatility and insane rewards fade away slowly.

So in the end some of the value in the 750 Trillion real world value will accrue to the Crypto layer, but obviously not all of it (as the real utility will still be in the actual houses, companies, factories etc). Raoul Pal has thrown around a number of 200 Trillion for Crypto markets, I’d be more ‘cautious’ with guesstimate of a nice 10x (25T-30T) in about 5 years time.

These predictions are basically based on a continuation of ‘more of the same’ as today, and could be upended of course if there were severe financial / monetary system crashes, wars etc. I hope not.. That’s all I got for now, any comments / feedback is always welcome.

Thanks for reading,

Oskar

Review of “the Bitcoin Standard”

The book “the Bitcoin Standard” by Saifedean Ammous was really influential in convincing me there is value in Bitcoin. Money is a complex, emotionally laden topic, with a rich history, and this book definitively deserves a read. Here is a summary of the 10 key points from my perspective.

#1 Money is a concept


Any value in a currency is an agreement between humans that there actually IS value there. I.e. no chimpanzee will agree there is value in the USD, Yuan, gold, seashells etc. Money – starting out as a medium of exchange – is a concept, such as the nation state or a company and does not ‘exist’ in the physical world.

#2 – Money Transfers Value over Time and Space

For ‘money’ the value primarily exists due to its salability – to transfer Value over Time and Space. In a free market humans have over time selected gold over millennia to be used as money, mainly due to criteria – such as being scarce, it’s divisible, it’s recognizable, other humans agree there is value there and you can’t easily find/create more of it – i.e. it has a high ‘stock to flow’ ratio. (stock to flow ratio means how much exists ‘in stock’ – eg above ground gold – compared to how much new flow – eg gold can be dug up – annually). The high stock to flow ratio is key as it means that the money can’t be inflated by market actors.

#3 – Money is a Network

Currency / money is a network between humans, and desirable qualities of money resemble those of gold. However should a better money emerge (say Bitcoin), in a free market humans would choose the money which best fulfills the criteria of money (ie that Best transfer Value over Time and Space). We have learned the hard way in some countries like Venezuela, Argentina what happens if Money doesn’t transfer Value over time.

#4 Bitcoin is open source Money


The number of Bitcoin is set algorithmically –there will be 21M bitcoins by 2140, the stock to flow ratio of Bitcoin will in 2025 be lower than that of gold, and the flow will be halved every four years.

It is also a remarkable innovation in terms of solving in code the ‘Byzantine Generals Problem’, that is how to co-ordinate distributed forces (think nodes) where some nodes might be traitors or corrupt. This solution establishes a consensus in the network about which transactions are legitimate, so it resolves the double-spending problem.

That is “Bitcoin can be best understood as distributed software that allows for transfer of value using a currency protected from unexpected inflation without relying on third parties”.

#5 Bitcoin network value

You can track the number of humans who agree there is Value in Bitcoin by tracking different metrics – eg market cap, number of wallets, hash rate, price etc. As more people consider it a store of value, that is the price grows higher, it also incentivizes more miners to secure the network – which in turn makes the network safer.

The value in Bitcoin is there due to network effects, and there are real switching costs involved- eg a fork won’t do any good. It’s the difference between an open source library that is copied, waiting to be executed, and a Live network that is running with transactions, users, data etc.

#6 Money printer go “Brrr”


Recent actions by central banks to print money – eg 5 Trillion in the US in response to the Covid pandemic – alter the perception humans have of their national currencies, and as central banks are inflating the supply, they are increasing the flow compared to the stock, and eroding the value in the currency.

While most of the world trusts markets for the pricing, allocation of capital goods – nonetheless there is a central planning board in every country of the world for the most important market – the market of capital.

#7 – Keynesian economics

JM Keynes was an influential economist in the 1930s who has influenced governments around the world that in a recession, governments should ‘stimulate’ the economy to make up for the slack from the private sector. Keynesian economics are the mainstream economics that are taught in Economics schools, opposed to classical/Austrian economics, with one of the main tenets of Keynesianism being that inflation is good, and should be ‘managed’ to about 2% per year. What is then not often mentioned is that the money in your wallet declines by 2% per year.

Ammous points out the example of the ‘depression that never happened’ – in 1921, where the government did not take ANY action, wages initially dropped 10%, but within 9 months the economy was strongly growing again leading to the ‘roaring 1920s’.

This is in opposition to the Depression in 1933 where the government froze wages, stimulated with public works programs and by confiscating US gold reserves, and eventually devalued the dollar 70% from $20/oz of gold to $35/oz.

#8 – A deflationary currency leads to lower time-preferences

An individual with a low time preference chooses to defer gratification, and work on items where the pay-off is further out in the future. We know from psychology this is good for individuals, and economics tells as investment in the future is beneficial. Therefore a money that is deflationary, that retains or increases in value, should be preferred by society and individuals alike.

However the opposite is taught today as beneficial – more consumer spending to satisfy cravings, wants, and less saving is good. Less capital therefore available for investment and growth, and pressure on companies to perform in the short term.

#9 – Bitcoin as a concept is many things

A concept has the ability to be multiple things at the same time – that is Bitcoin was initially planned to be a digital currency to be used for day to day purchases, however at the moment due to the price volatility and transaction speeds it is not feasible for that at the moment. There are eg second layer solutions (eg Lightning network or Strike ) that are working to resolve the issues. Taxation issues would have to be resolved as well.

#10 – Bitcoin as an option / hedge

Currently Bitcoin can more appropriately be thought of as

  • an option (hedge) towards central banks eroding the value in national currencies
  • an option on a true global money
  • a volatile investment with a lot of possible upside
  • but also a chance of going to zero.

If you’ve made it this far – I will again recommend you check out this book – “the Bitcoin Standard” by Saifedean Ammous. It can change how you think about money, as it did for me.

Thanks for reading,

Oskar

Thoughts on Cryptocurrency and Bitcoin

Hello there,

I’ve been thinking about what money is , what it will be in the future for a while now. I own some minimal stakes in BTC, ETH – for now to be ‘along for the ride’ and partially as a hedge against central banker mistakes. Central bankers may have PhDs, but we’re all human, and I don’t think they know the future. (Before we jump in  -if you need a primer on cryptocurrencies, you can for example read Mark Susters’ great piece on cryptocurrencies  – the cases for and against.)  

Writing this in August 2020, we are in the throes of the COVID-19 pandemic, and the US Federal Reserve has just deemed it necessary to expand their balance sheet by about 5 Trillion USD.

My main premise in this post is that every-one should seriously learn about cryptocurrency today, and potentially invest a small stake to better understand the ‘nature of this particular beast’. It could be very important.

So why now?

The 5 Trillion in new money from the Federal Reserve will flow out into the economy via the commercial banks, from there to the larger companies, regional banks and from there on down to Main Street. Unfortunately the current financial system is leading to greater concentrations of wealth  – the 0.1% getting richer and richer, while the 80% struggle with the cost of basic goods (food, health care, education, housing etc) increasing. We can already see it in that the stock market as of now is back to all-time highs, while on Main street the unemployment rate is high and many small businesses are turning off the lights.

Most people don’t think about the role of our country’s currency – and one of the key points which argue against cryptos becoming mainstream is the level of ignorance most people have re: what money/ currency – really is.

So understanding the different characteristics of money is a good starting point:

  • store of value – it needs to keep it’s value, be relatively stable
  • medium of exchange – you need to be able to transact with it
  • fungibility – one ‘dollar bill’ needs to be exactly the same as the next ‘dollar bill’ 
  • recognizability – you need to know it when you see it
  • unit of account – you need to be able to split it and record it in myriad ways
  • Fiat money vs commodity money – and finally – it will be either ‘fiat’ – that is credit money, or it will have some backing to it (like gold).

How many people you associate with understand these characteristics, and have questioned these for your country’s currency? In most developed countries most people just ‘use’ money, like clean water coming from the taps. We don’t have to understand how it got there.

We don’t use the Dollars, the Euro’s because they are excellent mediums of exchange or stores of value. I’ve lived in three developed countries (Finland, Singapore, US) and I can tell personally it didn’t really matter whether I was transacting in SGD, EUR or USD. The features were the same, you’d earn very little interest and lose out on inflation if you held cash in those. Today’s money is primarily a ‘unit of account’ that you use to track your income, spending, loans and savings – and for most people they don’t question WHAT that base unit actually IS.

Another main reason we use currencies today is because the government in the country where we reside are legally requiring (‘forcing’ to use the libertarian lingo) us to use that currency – at least for paying taxes. I fully agree that governments will NOT cede monetary control easily – since the whole government & country apparatus is transacting in that particular currency, and the government benefits from what’s called ‘Seigniorage‘ – that  is where sovereign-issued securities are exchanged for newly-printed banknotes by a central bank, allowing the sovereign to “borrow” without needing to repay.

Understanding that modern money is based on the electronic deposit system controlled by the banking system, and that this Fiat money is created as credit through the loan creation process, is crucial. In today’s electronic money system money exists largely as a record of account in databases as a result of the money being created via loan generation. Meaning that all of the ‘money’ in today’s world are actually just IOU’s. This also means that the USD’s , EURs of the world need to have inflation, as you and I need oxygen. We saw in 2009 what happened when even small amounts of deflation threatened to bring down the whole deck of cards.

So why is a deflationary currency better? Glad you asked.

  1. Because with a deflationary currency (eg Bitcoin) it would not be possible to ‘bail out’ Wall Street / speculators who have used too much money on stock buy-backs over the last 10 years, and kept nothing in reserves. We are rewarding stupidity – the bankruptcy process is needed to dis-incentivize speculation.
  2. An inflationary currency allows us to inflate and borrow almost limitless amounts, and spend money we don’t have – sacrificing not only our own futures, but our children’s future. A deflationary currency focuses you to spend on what you truly need, and some wants. It’s better for the environment, because you will not buy unnecessary ‘stuff’ because your money will be worth more in the future.
  3. A gold-backed currency is inherently deflationary – the US Dollar was gold-backed until 1971, and folks back then didn’t die of a ‘deflationary death spiral’ as some would want you to believe. Some of the greatest ‘real’ – (i.e. growth in earnings / wealth, accounting for inflation) –  growth periods in US history happened in the 1870s-1880s with rail-roads being built, industrialization getting started.

  4. With eg Bitcoin there is a fixed limit to the number of Bitcoin that will ever be released, and the mining process is ardous and expensive, so there is a mathematical guarantee that only a certain number of Bitcoin will ever be created (21 million to be exact, by 2140). The Bitcoin Blockchain has not been hacked since it’s inception in 2009, and I would suppose the likely hood is extremely low.

So how could a transition to using more Crypto / Bitcoin then happen? One route is via the recognition of wider parts of the population  – eg because of a currency crisis – that a crypto can provide a better store of value – eg ‘Bitcoin as digital gold‘. Currently it’s more of an option / hedge as mentioned.

Also if there is a wider recognition, adoption, then it should naturally lead to Gresham’s law (bad money drives good money out of circulation) – people holding that currency (eg crypto) for it’s value preservation abilities, and transacting in some other medium of exchange. Basically if you’ve bought Bitcoin, you would hold them as you’d believe they will store value / appreciate in the future – and you would transact in your local fiat currency.

Eventually you would also want to start getting paid in crypto (more value) instead of the fiat, should the ball start rolling this way.

To sum it up – I don’t have a crystal ball, and can’t predict the future – and have yet to find any-one that can. So pls consider these points:

  • The current monetary system seems to reward a very small portion of humanity disproportionately – why not try to find a better tool in this arena?
  • As humans we are experimenting with different technologies and tools all the time, so shouldn’t we try to discuss, experiment and improve also on how money works, rather than leaving it to a select committee? 
  • Given what we know about human fallibility and central banker prediction capabilities – is it not prudent to have a small portion of ones net-worth invested in alternative vehicles (like crypto, gold) that are not tied to the current monetary systems?

Thanks for reading,

Oskar

Stay at Home Dad

Hi there,

For the past year one of my roles has been as a ‘stay at home dad’. It started with our ‘road-schooling experiment‘ August to October 2016, then once our kids started ‘regular school’ in November 2016, it gave me some time to learn Python programming, Machine learning , chatbots and more recently about blockchain technologies. Now that I’ve started a full-time role in a new company (here) – I wanted to jot down my thoughts related to being a ‘stay at home Dad’, with a simple totally subjective scoring – A to F – about how this experiment has worked out.

Financial – C

Not having a consistent, solid income has been stressful and the heart ache of not having your own income can be strong. It has made me feel like a ‘kept man’, useless – for not providing more monetary value to my family, to society. I know the days / stereotypes of men being the ‘primary bread-winner’ are gone, but stereotypes die hard and the fact is that we all want to ‘get ahead’ financially -not just tread water.

While giving us the time / flexibility to take road trips, for example with Sam’s soccer – we’ve missed travel – as longer, bigger trips would tap into savings.

On the other hand I feel energised by the break, I’m really enjoying the new gig and I know that the new technologies / methods I’ve learned will be very valuable. Already in my new role the things I’ve learned about machine learning, chatbots etc have become useful.

Parenting – A

The best part of my day has been meeting my kids after school. Walking home with Kate, with her holding my hand. Sam always cool and enthusiastic when he gets home, wanting to discuss my day.

It’s given me time to find out how new technology is impacting kids and adults alike, as Simon Sinek discusses here re: millenials. And not only to tell them ‘get off the phone’ – but to have the time to sit down and play a board game, take a swim or just chat. Really it comes down to having the TIME to support the kids fully – listening to them, discussing with them, supporting them.

It’s given us time to cook dinner as a family – with ‘Hellofresh‘ we’ve been getting two meals per week. The packages include all the ingredients you need to fix something a bit more fancy than what a ‘normal parent’ (ie me) would be able to pull off. The easy to follow recipes have given the kids a good start in their cooking skills.

Daily routines – B

Any ‘non-work’ tasks in the home that need to be done – those have largely fallen to me the past year – picking up kids, groceries, fixes around house, drive to practice, etc etc. Of course Jolene did a bunch too, but with her work being a priority, it’s been my work that gets interrupted, takes second place – which hasn’t felt great. However it’s also taken a load off Jolene so she’s relatively less stressed.

Health  – A

It’s given me TIME to focus on my health, try out Crossfit. Health = Wealth in some sense, and I’m happy to have found CrossFit. I’ve been training about 6 times per week during this year, and I feel really great about having found a ‘practice’ that will keep me strong, energetic and mobile for many years to come… Three hard workouts per week, three easy (stretching, mobilizing, getting blood going) workouts is just good for staving off mid-life bloat and identity crises..

Conclusion

Overall (one C, two A’s, one B ) – a B+ average, which is not too bad. Would I take this type of a ‘creative / family’ break again if given the opportunity? Right now I’m not sure. However, give me another 3-4 years in technology (project management / innovation / HR) and I just might need it 🙂

Thanks for reading,

Oskar

10 Machine Learning use cases for HR

Hi there,

These days we seem to be bombarded daily with articles about Artificial Intelligence, Machine Learning and some version of ‘robots will take your job’… However as of 2016, machine learning is a buzzword, and according to the Gartner hype cycle of 2016, at its peak of inflated expectations.[12] This is because finding patterns is hard, often we don’t have enough training data available, and also because of the high expectations it often fails to deliver – so hold your expectations of having a ‘Jarvis‘-like presence guiding you along – we’re not quite there yet 🙂

 

With that in mind, I thought it would be interesting as an HR/IT professional to try to identify possible use cases in HR for machine learning, and then identify where the most value can be gained from using it. And by HR /Human Resources here I mean all the ‘branches’ of it – including Recruitment, Personnel Administration, Talent & Performance Management, Payroll, Time management, Benefits, Compensation etc etc. I’d be happy to have comments & additions be added to the list, so leave your suggestion/s in the comments section below. With that said, here we go:

Use cases for Machine Learning

1/ Recruitment, candidate attraction – explore your social media data / mentions, and dig down where traffic – with a focus on target market (say front-end engineers) are coming from, in order to achieve more predictable candidate flow. And remember positive impression matter a lot, as Chobani has shown with HR as PR. Then use blog post writing software for example using Narrative Language Generation, in order to improve social media presence.

2/ Recruitment to Talent Management –  explore your data to classify from which industries / areas successful candidates have come. Can you find commonalities between solid performers? You can use for example K-means clustering to find the commonalities between the good performers. However as this article explains, this can lead to bias in your selection (say as most CS graduates are male), so it’s important to back test your algorithm to avoid issues down the road.

3/ Recruitment, candidate selection – use natural language understanding or algorithmic selection tools on candidate resume pools to find suitable candidates.

4/ HR customer service – as explored here, with Natural Language Understanding getting better and better it’s possible to create chatbots (or conversational agents) that help answer employee questions, route questions to human agents, perhaps even do simple updates. Companies in this space include eg. Talla, Kylie,ai. Custom implementations could be done with Slack, Microsoft’s botframework, or IBM Watson conversations etc. I’m working on a ‘Benefits’ chatbot using Watson Conversations and SAP Hana Cloud Platform, welcome to ping me here if you are interested in this space.

5/ New hire on-boarding – chatbots these days use NLP (Natural Language Processing) which ‘understands’ what the employee is intending to communicate. You could create a chatbot or ‘conversational agent’ with new hire guidance, FAQs, role documentation.

6/ Retaining valuable employees – Use salary data, performance, organizational data etc to explore correlations, and ultimately predict which factors predispose to voluntary terminations. You could do these predictions eg. with Ensemble methods / Random Forests, or use classification tools like support vector machines or a K-neighbors classifier to group the employees into ‘buckets’ of ‘high / medium / low’ chance of leaving.

7/ Performance management guidance – provide a chatbot to answer employee queries re: the performance process like this one by SAP/Successfactors.

8/Payroll & Time Management employee questions – provide a chatbot to answer employee queries re: payslips, overtime, absence policies etc, as explored here. The bot also handle repetitive tasks which can be demoralising and unfulfilling (e.g. pre-qualification (what’s this issue about?) or authorisation (how can you prove you are John Smith).

9/ Payroll data connection to talent management – highly structured data found in conventional databases tend to support traditional, highly analytical machine learning approaches. If you have 10+ years of transactional data, then you could use machine learning to find correlations between employee characteristics and pay raises for example.

10/ Career / role recommender system – If you have a large enough organisation with different departments, very varied roles and the data to track where people have previously transferred to or changed roles into, then you could also create a recommender system based on interests, skills, performance, where suitable new roles in your organization could be found. Here’s a comprehensive list of different recommender system – eg. with tools from Amazon, Microsoft Azure or IBM Watson.

Hope this whet your appetite a bit to explore Machine Learning for HR a bit further – and let me any comments or additions below!

Launch festival April 2017

Hi ya,

A few months ago I got a founder ticket to the Launch Festival in San Francisco for April 7th and 8th. After three whirlwind days in the Bay Area here are the things that stuck out to me. Personally for me / Move Correctly my best meeting was actually with Greg, the CEO of Fit3d – since he provided so much actionable, real advice. It’s just fantastic to exchange ideas with some-one in the same industry. Now here are my notes about Launch:

 

Machine Learning

1. The flywheel for Machine Learning – was articulated by Rob May, the CEO of Talla, is the way ML becomes better the more data there is, makes better algorithms, makes a better product, drives more use and more data … So it’s really about who can build the best/ quickest flywheel for your use case / industry, and in that sense (at least the ML companies want to give that impression) this is the time to make major investments in this area, as the first (successful) movers will have a big advantage.

2. Zorroa – are doing really impressive visual recognition from videos – where they can search inside videos / images just as if you were googling documents. So eg locating any scenes where ‘the Rock’ appears, then narrowing it down if it happens in a bank, and further where there is a Lambo in the scene.. Currently you need to plug into their REST API,  but they mentioned a SAAS app in a few months…

3. Corto – their demo of a chatbot analytics interface to a pharmaceutical genomics data was impressive, with hypergraphs / nodes flowing and a lot of complex words in the presentation, so I have no doubt the tech is solid. Their team is chock-full of smart guys, with eg one of the leading AGI guys – Ben Goertzel as Chief Scientist. What wasn’t clear for me is who will sell their product and what is their value prop?

4. The PAC framework by Rob May again, which essentially states that any company should evaluate how they want to use Machine Learning in these categories:

A) P for Predict, eg. in recruitment which candidates will perform best, in sales which products etc.

B) A for Automate, could be easing workflow, say for example NLP (natural language processing) transcribing recruitment interview notes.

C) C for Classify – say classifying best resumes into different buckets quickly

Now apply these questions across your customers, across Product, across Operations, and you should start to identify good opportunities where to apply ML.

5. Talla is a customer service bot either for IT or HR, that’s been trained to answer IT / HR questions, with a UI either in Slack or Microsoft teams. Their target market is in mid-size companies.

6. Kylie.ai –  created a customer service bot, integrated with eg ticketing systems like Zendesk. They’d ‘clone’ employee personalities and create a response integrated into existing UI’s eg on Zendesk, Salesforce, SAP etc, which the human customer service agent can review, modify, approve / send.

Cannabis

The Cannabis market is yuuge apparently as it warranted it’s own vertical, next to healthcare, drones, ML etc. Interesting companies included:

  1. Leaf – built a small growing unit looking like fridge, which automates home growing. Sold about 1M of them in advance and are taking orders for 2018..
  2. Alula Hydro, who have created a hydroponic, nutrient delivery system for industrial growers. The 20K industrial growing management system apparently can raise yields from a crappy 1K per pound to 5K-6K per pound.
  3. Baker, who are making a CRM / loyalty / online store SAAS for dispensaries.

 

Miscellaneous notes

  1.  How to get 1000 applicants for a job ad – by Tucker Max from Book in a box: 
    • Start with the hook – explain the why / the mission of the company, and if some-one doesn’t believe in that clearly they’re not a fit.
    • Sell the role – Talk like you would talk to a friend about the job. Ditch all the standard corporate lingo about ‘mission critical, pro-active go-getter with a nose for global synergies’. 
    • Pleasure and Pain – explain why the company / role is awesome, but clearly also the downsides of the job, no point in trying to sugar-coat stuff.
    • Testimonials / social proof – we use these in any ads (for soda, cars, books etc), why not for job ads?
    • Finally – the actual ‘boring’ details
  2. VR / AR – was actually a bit underwhelming – nothing really that stood out. Yes, it’s cool to get a new, different type of camera or get analytics off VR, but…eh.
  3. In hardware – Megabots was really cool, just in terms of lighting up any 6-year old inside all of us – Robots with fun big weapons (WEABOONS!) and picking up cars 😉