Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

• A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

• An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

• A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

• Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

• Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

• Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

• Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

• alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

• a new depth model that may be used to the output of image-to-image generation.

• a revolutionary upscaling technique that can quadruple the resolution of an image.

• Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

• I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

• Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

• It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

• When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

The trick Notion could use is simply Facebook-ing the hell out of them.

*Time travel to fifteen years ago.* Future-Me: “Hey! What are you up to?” Old-Me: “I am proofreading an article. It’s taking a few hours, but I will be done soon.” Future-Me: “You know, in the future, you will be using a google chrome plugin called Grammarly that will help you easily proofread articles in half that time.” Old-Me: “What is… Google Chrome?” Future-Me: “Gosh…”

I love Grammarly. It’s one of those products that I personally feel the effects of. I mean, Space X is a great company. But I am not a rocket writing this article in space (or am I?)…

No, I’m not. So I don’t personally feel a connection to Space X. So, if a company collapse occurs in the morning, I might write about it. But I will have zero emotions regarding it.

Yet, if Grammarly fails tomorrow, I will feel 1% emotionally distressed. So looking at the title of this article, you’d realize that I am betting against them. This is how much I believe in the critical business model that’s taking over the world, the one of Notion.

Notion How frequently do you go through your notes?

Grammarly is everywhere, which helps its success. Grammarly is available when you update LinkedIn on Chrome. Grammarly prevents errors in Google Docs.

My internal concentration isn't apparent in the previous paragraph. Not Grammarly. I should have used Chrome to make a Google doc and LinkedIn update. Without this base, Grammarly will be useless.

So, welcome to this business essay.

• Grammarly provides a solution.

• Another issue is resolved by Jasper.

• Your entire existence is supposed to be contained within Notion.

New Google Chrome is offline. It's an all-purpose notepad (in the near future.)

• How should I start my blog? Enter it in Note.

• an update on LinkedIn? If you mention it, it might be automatically uploaded there (with little help from another app.)

• An advanced thesis? You can brainstorm it with your coworkers.

This ad sounds great! I won't cry if Notion dies tomorrow.

I'll reread the following passages to illustrate why I think Notion could kill Grammarly and Jasper.

Notion is a fantastic app that incubates your work.

Smartly, they began with note-taking.

Hopefully, your work will be on Notion. Grammarly and Jasper are still must-haves.

Grammarly will proofread your typing while Jasper helps with copywriting and AI picture development.

They're the best, therefore you'll need them. Correct? Nah.

Notion might bombard them with Facebook posts.

Notion: “Hi Grammarly, do you want to sell your product to us?” Grammarly: “Dude, we are more valuable than you are. We’ve even raised $400m, while you raised $342m. Our last valuation round put us at $13 billion, while yours put you at $10 billion. Go to hell.” Notion: “Okay, we’ll speak again in five years.”

Notion: “Jasper, wanna sell?” Jasper: “Nah, we’re deep into AI and the field. You can’t compete with our people.” Notion: “How about you either sell or you turn into a Snapchat case?” Jasper: “…”

Notion is your home. Grammarly is your neighbor. Your track is Jasper.

What if you grew enough vegetables in your backyard to avoid the supermarket? No more visits.

What if your home had a beautiful treadmill? You won't rush outside as much (I disagree with my own metaphor). (You get it.)

It's Facebooking. Instagram Stories reduced your Snapchat usage. Notion will reduce your need to use Grammarly.

The Final Piece of the AI Puzzle

Let's talk about Notion first, since you've probably read about it everywhere.

• They raised $343 million, as I previously reported, and bought four businesses

• According to Forbes, Notion will have more than 20 million users by 2022. The number of users is up from 4 million in 2020.

If raising $1.8 billion was impressive, FTX wouldn't have fallen.

This article compares the basic product to two others. Notion is a day-long app.

Notion has released Notion AI to support writers. It's early, so it's not as good as Jasper. Then-Jasper isn't now-Jasper. In five years, Notion AI will be different.

With hard work, they may construct a Jasper-like writing assistant. They have resources and users.

At this point, it's all speculation. Jasper's copywriting is top-notch. Grammarly's proofreading is top-notch. Businesses are constrained by user activities.

If Notion's future business movements are strategic, they might become a blue ocean shark (or get acquired by an unbelievable amount.)

I love business mental teasers, so tell me:

• How do you feel? Are you a frequent Notion user?

• Do you dispute my position? I enjoy hearing opposing viewpoints.

Ironically, I proofread this with Grammarly.

Is iPhone's Dynamic Island invention or a marketing ploy?

First of all, why the notch?

When Apple debuted the iPhone X with the notch, some were surprised, confused, and amused by the goof. Let the Brits keep the new meaning of top-notch.

Apple removed the bottom home button to enhance screen space. The tides couldn't overtake part of the top. This section contained sensors, a speaker, a microphone, and cameras for facial recognition. A town resisted Apple's new iPhone design.

From iPhone X to 13, the notch has gotten smaller. We expected this as technology and engineering progressed, but we hated the notch. Apple approved. They attached it to their other gadgets.

Apple accepted, owned, and ran with the iPhone notch, it has become iconic (or infamous); and that’s intentional.

The Island Where Apple Is

Apple needs to separate itself, but they know how to do it well. The iPhone 14 Pro finally has us oohing and aahing. Life-changing, not just higher pixel density or longer battery.

Dynamic Island turned a visual differentiation into great usefulness, which may not be life-changing. Apple always welcomes the controversy, whether it's $700 for iMac wheels, no charging block with a new phone, or removing the headphone jack.

Apple knows its customers will be loyal, even if they're irritated. Their odd design choices often cause controversy. It's calculated that people blog, review, and criticize Apple's products. We accept what works for them.

While the competition zigs, Apple zags. Sometimes they zag too hard and smash into a wall, but we talk about it anyways, and that’s great publicity for them.

Getting Dependent on the drug

The notch became a crop. Dynamic Island's design is helpful, intuitive, elegant, and useful. It increases iPhone usability, productivity (slightly), and joy. No longer unsightly.

The medication helps with multitasking. It's a compact version of the iPhone's Live Activities lock screen function. Dynamic Island enhances apps and activities with visual effects and animations whether you engage with it or not. As you use the pill, its usefulness lessens. It lowers user notifications and consolidates them with live and permanent feeds, delivering quick app statuses. It uses the black pixels on the iPhone 14's display, which looked like a poor haircut.

The pill may be a gimmick to entice customers to use more Apple products and services. Apps may promote to their users like a live billboard.

Be prepared to get a huge dose of Dynamic Island’s “pill” like you never had before with the notch. It might become so satisfying and addicting to use, that every interaction with it will become habit-forming, and you’re going to forget that it ever existed.

WARNING: A Few Potential Side Effects

Vision blurred Dynamic Island's proximity to the front-facing camera may leave behind grease that blurs photos. Before taking a selfie, wipe the camera clean.

Strained thumb To fully use Dynamic Island, extend your thumb's reach 6.7 inches beyond your typical, comfortable range.

Happiness, contentment The Dynamic Island may enhance Endorphins and Dopamine. Multitasking, interactions, animations, and haptic feedback make you want to use this function again and again.

Motion-sickness Dynamic Island's motions and effects may make some people dizzy. If you can disable animations, you can avoid motion sickness.

I'm not a doctor, therefore they aren't established adverse effects.

Does Dynamic Island Include Multiple Tasks?

Dynamic Islands is a placebo for multitasking. Apple might have compromised on iPhone multitasking. It won't make you super productive, but it's a step up.

iPhone is primarily for personal use, like watching videos, messaging friends, sending money to friends, calling friends about the money you were supposed to send them, taking 50 photos of the same leaf, investing in crypto, driving for Uber because you lost all your money investing in crypto, listening to music and hailing an Uber from a deserted crop field because while you were driving for Uber your passenger stole your car and left you stranded, so you used Apple’s new SOS satellite feature to message your friend, who still didn’t receive their money, to hail you an Uber; now you owe them more money… karma?

We won't be watching videos on iPhones while perusing 10,000-row spreadsheets anytime soon. True multitasking and productivity aren't priorities for Apple's iPhone. Apple doesn't to preserve the iPhone's experience. Like why there's no iPad calculator. Apple doesn't want iPad users to do math, but isn't essential for productivity?

Digressing.

Apple will block certain functions so you must buy and use their gadgets and services, immersing yourself in their ecosystem and dictating how to use their goods.

Dynamic Island is a poor man’s multi-task for iPhone, and that’s fine it works for most iPhone users. For substantial productivity Apple prefers you to get an iPad or a MacBook. That’s part of the reason for restrictive features on certain Apple devices, but sometimes it’s based on principles to preserve the integrity of the product, according to Apple’s definition.

Is Apple using deception?

Dynamic Island may be distracting you from a design decision. The answer is kind of. Elegant distraction

When you pull down a smartphone webpage to refresh it or minimize an app, you get seamless animations. It's not simply because it appears better; it's due to iPhone and smartphone processing speeds. Such limits reduce the system's response to your activity, slowing the experience. Designers and developers use animations and effects to distract us from the time lag (most of the time) and sometimes because it looks cooler and smoother.

Dynamic Island makes apps more useable and interactive. It shows system states visually. Turn signal audio and visual cues, voice assistance, physical and digital haptic feedbacks, heads-up displays, fuel and battery level gauges, and gear shift indicators helped us overcome vehicle design problems.

Dynamic Island is a wonderfully delightful (and temporary) solution to a design “problem” until Apple or other companies can figure out a way to sink the cameras under the smartphone screen.

Apple Has Returned to Being an Innovative & Exciting Company

Now Apple's products are exciting. Next, bring back real Apple events, not pre-recorded demos.

Dynamic Island integrates hardware and software. What will this new tech do? How would this affect device use? Or is it just hype?

Dynamic Island may be an insignificant improvement to the iPhone, but it sure is promising for the future of bridging the human and computer interaction gap.

Christopher Janz of Point Nine Capital created the SaaS napkin in 2016. This post shows how founders have raised cash in the last 6 years. View raw data.

Round size

Unsurprisingly, round sizes have expanded and will taper down in 2022. In 2016, pre-seed rounds were $200k to $500k; currently, they're $1-$2m. Despite the macroeconomic scenario, Series A have expanded from $3m to $12m in 2016 to $6m and $18m in 2022.

Valuation

There are hints that valuations are rebounding this year. Pre-seed valuations in 2022 are $12m from $3m in 2016, and Series B prices are $270m from $100m in 2016.

Compared to public SaaS multiples, Series B valuations more closely reflect the market, but Seed and Series A prices seem to be inflated regardless of the market.

I'd like to know how each annual cohort performed for investors, based on the year they invested and the valuations. I can't access this information.

ARR

Seed firms' ARR forecasts have risen from $0 to $0.6m to $0 to $1m. 2016 expected $1.2m to $3m, 2021 $0.5m to $4m, and this year $0.5m to $2.5m, suggesting that Series A firms may raise with less ARR today. Series B minutes fell from $4.2m to $3m.

Capitalization Rate

2022 is the year that VCs start discussing capital efficiency in portfolio meetings. Given the economic shift in the markets and the stealthy VC meltdown, it's not surprising. Christopher Janz added capital efficiency to the SaaS Napkin as a new statistic for Series A (3.5x) and Series B. (2.5x). Your investors must live under a rock if they haven't asked about capital efficiency. If you're unsure:

The Capital Efficiency Ratio is the ratio of how much a company has spent growing revenue and how much they’re receiving in return. It is the broadest measure of company effectiveness in generating ARR

What next?

No one knows what's next, including me. All startup and growing enterprises around me are tightening their belts and extending their runways in anticipation of a difficult fundraising ride. If you're wanting to raise money but can wait, wait till the market is more stable and access to money is easier.

(The author's opinions should not be used to make investment decisions or as a recommendation to invest.)

The pandemic and social media pseudoscience have made us all epidemiologists, for better or worse. Flattening the curve, social distancing, lockdowns—remember? Some of you may remember R0 (R naught), the number of healthy humans the average COVID-infected person infects. Thankfully, the world has moved on from Greater China's nightmare. Politicians have refocused their talent for misdirection on getting their constituents invested in the war for Russian Reunification or Russian Aggression, depending on your side of the iron curtain.

Humanity battles two fronts. A war against an invisible virus (I know your Commander in Chief might have told you COVID is over, but viruses don't follow election cycles and their economic impacts linger long after the last rapid-test clinic has closed); and an undeclared World War between US/NATO and Eurasia/Russia/China. The fiscal and monetary authorities' current policies aim to mitigate these two conflicts' economic effects.

Since all politicians are short-sighted, they usually print money to solve most problems. Printing money is the easiest and fastest way to solve most problems because it can be done immediately without much discussion. The alternative—long-term restructuring of our global economy—would hurt stakeholders and require an honest discussion about our civilization's state. Both of those requirements are non-starters for our short-sighted political friends, so whether your government practices capitalism, communism, socialism, or fascism, they all turn to printing money-ism to solve all problems.

Free money stimulates demand, so people buy crap. Overbuying shit raises prices. Inflation. Every nation has food, energy, or goods inflation. The once-docile plebes demand action when the latter two subsets of inflation rise rapidly. They will be heard at the polls or in the streets. What would you do to feed your crying hungry child?

Global central banks During the pandemic, the Fed, PBOC, BOJ, ECB, and BOE printed money to aid their governments. They worried about inflation and promised to remove fiat liquidity and tighten monetary conditions.

Imagine Nate Diaz's round-house kick to the face. The financial markets probably felt that way when the US and a few others withdrew fiat wampum. Sovereign debt markets suffered a near-record bond market rout.

The undeclared WW3 is intensifying, with recent gas pipeline attacks. The global economy is already struggling, and credit withdrawal will worsen the situation. The next pandemic, the Yield Curve Control (YCC) virus, is spreading as major central banks backtrack on inflation promises. All central banks eventually fail.

Here's a scorecard.

In order to save its financial system, BOE recently reverted to Quantitative Easing (QE).

BOJ Continuing YCC to save their banking system and enable affordable government borrowing.

ECB printing money to buy weak EU member bonds, but will soon start Quantitative Tightening (QT).

PBOC Restarting the money printer to give banks liquidity to support the falling residential property market.

Fed raising rates and QT-shrinking balance sheet.

80% of the world's biggest central banks are printing money again. Only the Fed has remained steadfast in the face of a financial market bloodbath, determined to end the inflation for which it is at least partially responsible—the culmination of decades of bad economic policies and a world war.

YCC printing is the worst for fiat currency and society. Because it necessitates central banks fixing a multi-trillion-dollar bond market. YCC central banks promise to infinitely expand their balance sheets to keep a certain interest rate metric below an unnatural ceiling. The market always wins, crushing humanity with inflation.

BOJ's YCC policy is longest-standing. The BOE joined them, and my essay this week argues that the ECB will follow. The ECB joining YCC would make 60% of major central banks follow this terrible policy. Since the PBOC is part of the Chinese financial system, the number could be 80%. The Chinese will lend any amount to meet their economic activity goals.

The BOE committed to a 13-week, GBP 65bn bond price-fixing operation. However, BOEs YCC may return. If you lose to the market, you're stuck. Since the BOE has announced that it will buy your Gilt at inflated prices, why would you not sell them all? Market participants taking advantage of this policy will only push the bank further into the hole it dug itself, so I expect the BOE to re-up this program and count them as YCC.

In a few trading days, the BOE went from a bank determined to slay inflation by raising interest rates and QT to buying an unlimited amount of UK Gilts. I expect the ECB to be dragged kicking and screaming into a similar policy. Spoiler alert: big daddy Fed will eventually die from the YCC virus.

Threadneedle St, London EC2R 8AH, UK

Before we discuss the BOE's recent missteps, a chatroom member called the British royal family the Kardashians with Crowns, which made me laugh. I'm sad about royal attention. If the public was as interested in energy and economic policies as they are in how the late Queen treated Meghan, Duchess of Sussex, UK politicians might not have been able to get away with energy and economic fairy tales.

The BOE printed money to recover from COVID, as all good central banks do. For historical context, this chart shows the BOE's total assets as a percentage of GDP since its founding in the 18th century.

The UK has had a rough three centuries. Pandemics, empire wars, civil wars, world wars. Even so, the BOE's recent money printing was its most aggressive ever!

BOE Total Assets as % of GDP (white) vs. UK CPI

Now, inflation responded slowly to the bank's most aggressive monetary loosening. King Charles wishes the gold line above showed his popularity, but it shows his subjects' suffering.

The BOE recognized early that its money printing caused runaway inflation. In its August 2022 report, the bank predicted that inflation would reach 13% by year end before aggressively tapering in 2023 and 2024.

Aug 2022 BOE Monetary Policy Report

The BOE was the first major central bank to reduce its balance sheet and raise its policy rate to help.

The BOE first raised rates in December 2021. Back then, JayPow wasn't even considering raising rates.

UK policymakers, like most developed nations, believe in energy fairy tales. Namely, that the developed world, which grew in lockstep with hydrocarbon use, could switch to wind and solar by 2050. The UK's energy import bill has grown while coal, North Sea oil, and possibly stranded shale oil have been ignored.

WW3 is an economic war that is balkanizing energy markets, which will continue to inflate. A nation that imports energy and has printed the most money in its history cannot avoid inflation.

The chart above shows that energy inflation is a major cause of plebe pain.

The UK is hit by a double whammy: the BOE must remove credit to reduce demand, and energy prices must rise due to WW3 inflation. That's not economic growth.

Boris Johnson was knocked out by his country's poor economic performance, not his lockdown at 10 Downing St. Prime Minister Truss and her merry band of fools arrived with the tried-and-true government remedy: goodies for everyone.

She released a budget full of economic stimulants. She cut corporate and individual taxes for the rich. She plans to give poor people vouchers for higher energy bills. Woohoo! Margret Thatcher's new pants suit.

My buddy Jim Bianco said Truss budget's problem is that it works. It will boost activity at a time when inflation is over 10%. Truss' budget didn't include austerity measures like tax increases or spending cuts, which the bond market wanted. The bond market protested.

30-year Gilt yield chart. Yields spiked the most ever after Truss announced her budget, as shown. The Gilt market is the longest-running bond market in the world.

The Gilt market showed the pole who's boss with Cardi B.

Before this, the BOE was super-committed to fighting inflation. To their credit, they raised short-term rates and shrank their balance sheet. However, rapid yield rises threatened to destroy the entire highly leveraged UK financial system overnight, forcing them to change course.

Accounting gimmicks allowed by regulators for pension funds posed a systemic threat to the UK banking system. UK pension funds could use interest rate market levered derivatives to match liabilities. When rates rise, short rate derivatives require more margin. The pension funds spent all their money trying to pick stonks and whatever else their sell side banker could stuff them with, so the historic rate spike would have bankrupted them overnight. The FT describes BOE-supervised chicanery well.

To avoid a financial apocalypse, the BOE in one morning abandoned all their hard work and started buying unlimited long-dated Gilts to drive prices down.

Another reminder to never fight a central bank. The 30-year Gilt is shown above. After the BOE restarted the money printer on September 28, this bond rose 30%. Thirty-fucking-percent! Developed market sovereign bonds rarely move daily. You're invested in His Majesty's government obligations, not a Chinese property developer's offshore USD bond.

The political need to give people goodies to help them fight the terrible economy ran into a financial reality. The central bank protected the UK financial system from asset-price deflation because, like all modern economies, it is debt-based and highly levered. As bad as it is, inflation is not their top priority. The BOE example demonstrated that. To save the financial system, they abandoned almost a year of prudent monetary policy in a few hours. They also started the endgame.

Let's play Central Bankers Say the Darndest Things before we go to the continent (and sorry if you live on a continent other than Europe, but you're not culturally relevant).

Pre-meltdown BOE output:

FT, October 17, 2021 On Sunday, the Bank of England governor warned that it must act to curb inflationary pressure, ignoring financial market moves that have priced in the first interest rate increase before the end of the year.

On July 19, 2022, Gov. Andrew Bailey spoke. Our 2% inflation target is unwavering. We'll do our job.

August 4th 2022 MPC monetary policy announcement According to its mandate, the MPC will sustainably return inflation to 2% in the medium term.

Catherine Mann, MPC member, September 5, 2022 speech. Fast and forceful monetary tightening, possibly followed by a hold or reversal, is better than gradualism because it promotes inflation expectations' role in bringing inflation back to 2% over the medium term.

When their financial system nearly collapsed in one trading session, they said:

The Bank of England's Financial Policy Committee warned on 28 September that gilt market dysfunction threatened UK financial stability. It advised action and supported the Bank's urgent gilt market purchases for financial stability.

It works when the price goes up but not down. Is my crypto portfolio dysfunctional enough to get a BOE bailout?

Next, the EU and ECB. The ECB is also fighting inflation, but it will also succumb to the YCC virus for the same reasons as the BOE.

Frankfurt am Main, ECB Tower, Sonnemannstraße 20, 60314

Only France and Germany matter economically in the EU. Modern European history has focused on keeping Germany and Russia apart. German manufacturing and cheap Russian goods could change geopolitics.

France created the EU to keep Germany down, and the Germans only cooperated because of WWII guilt. France's interests are shared by the US, which lurks in the shadows to prevent a Germany-Russia alliance. A weak EU benefits US politics. Avoid unification of Eurasia. (I paraphrased daddy Felix because I thought quoting a large part of his most recent missive would get me spanked.)

As with everything, understanding Germany's energy policy is the best way to understand why the German economy is fundamentally fucked and why that spells doom for the EU. Germany, the EU's main economic engine, is being crippled by high energy prices, threatening a depression. This economic downturn threatens the union. The ECB may have to abandon plans to shrink its balance sheet and switch to YCC to save the EU's unholy political union.

France did the smart thing and went all in on nuclear energy, which is rare in geopolitics. 70% of electricity is nuclear-powered. Their manufacturing base can survive Russian gas cuts. Germany cannot.

My boy Zoltan made this great graphic showing how screwed Germany is as cheap Russian gas leaves the industrial economy.

$27 billion of Russian gas powers almost $2 trillion of German economic output, a 75x energy leverage. The German public was duped into believing the same energy fairy tales as their politicians, and they overwhelmingly allowed the Green party to dismantle any efforts to build a nuclear energy ecosystem over the past several decades. Germany, unlike France, must import expensive American and Qatari LNG via supertankers due to Nordstream I and II pipeline sabotage.

American gas exports to Europe are touted by the media. Gas is cheap because America isn't the Western world's swing producer. If gas prices rise domestically in America, the plebes would demand the end of imports to avoid paying more to heat their homes.

German goods would cost much more in this scenario. German producer prices rose 46% YoY in August. The German current account is rapidly approaching zero and will soon be negative.

German PPI Change YoY

German Current Account

The reason this matters is a curious construction called TARGET2. Let’s hear from the horse’s mouth what exactly this beat is:

TARGET2 is the real-time gross settlement (RTGS) system owned and operated by the Eurosystem. Central banks and commercial banks can submit payment orders in euro to TARGET2, where they are processed and settled in central bank money, i.e. money held in an account with a central bank.

Source: ECB

Let me explain this in plain English for those unfamiliar with economic dogma.

This chart shows intra-EU credits and debits. TARGET2. Germany, Europe's powerhouse, is owed money. IOU-buying Greeks buy G-wagons. The G-wagon pickup truck is badass.

If all EU countries had fiat currencies, the Deutsche Mark would be stronger than the Italian Lira, according to the chart above. If Europe had to buy goods from non-EU countries, the Euro would be much weaker. Credits and debits between smaller political units smooth out imbalances in other federal-provincial-state political systems. Financial and fiscal unions allow this. The EU is financial, so the centre cannot force the periphery to settle their imbalances.

Greece has never had to buy Fords or Kias instead of BMWs, but what if Germany had to shut down its auto manufacturing plants due to energy shortages?

Italians have done well buying ammonia from Germany rather than China, but what if BASF had to close its Ludwigshafen facility due to a lack of affordable natural gas?

I think you're seeing the issue.

Instead of Germany, EU countries would owe foreign producers like America, China, South Korea, Japan, etc. Since these countries aren't tied into an uneconomic union for politics, they'll demand hard fiat currency like USD instead of Euros, which have become toilet paper (or toilet plastic).

Keynesian economists have a simple solution for politicians who can't afford market prices. Government debt can maintain production. The debt covers the difference between what a business can afford and the international energy market price.

Germans are monetary policy conservative because of the Weimar Republic's hyperinflation. The Bundesbank is the only thing preventing ECB profligacy. Germany must print its way out without cheap energy. Like other nations, they will issue more bonds for fiscal transfers.

More Bunds mean lower prices. Without German monetary discipline, the Euro would have become a trash currency like any other emerging market that imports energy and food and has uncompetitive labor.

Bunds price all EU country bonds. The ECB's money printing is designed to keep the spread of weak EU member bonds vs. Bunds low. Everyone falls with Bunds.

Like the UK, German politicians seeking re-election will likely cause a Bunds selloff. Bond investors will understandably reject their promises of goodies for industry and individuals to offset the lack of cheap Russian gas. Long-dated Bunds will be smoked like UK Gilts. The ECB will face a wave of ultra-levered financial players who will go bankrupt if they mark to market their fixed income derivatives books at higher Bund yields.

Some treats People: Germany will spend 200B to help consumers and businesses cope with energy prices, including promoting renewable energy.

That, ladies and germs, is why the ECB will immediately abandon QT, move to a stop-gap QE program to normalize the Bund and every other EU bond market, and eventually graduate to YCC as the market vomits bonds of all stripes into Christine Lagarde's loving hands. She probably has soft hands.

The 30-year Bund market has noticed Germany's economic collapse. 2021 yields skyrocketed.

30-year Bund Yield

ECB Says the Darndest Things:

Because inflation is too high and likely to stay above our target for a long time, we took today's decision and expect to raise interest rates further.- Christine Lagarde, ECB Press Conference, Sept 8.

The Governing Council will adjust all of its instruments to stabilize inflation at 2% over the medium term. July 21 ECB Monetary Decision

Everyone struggles with high inflation. The Governing Council will ensure medium-term inflation returns to two percent. June 9th ECB Press Conference

I'm excited to read the after. Like the BOE, the ECB may abandon their plans to shrink their balance sheet and resume QE due to debt market dysfunction.

Eighty Percent

I like YCC like dark chocolate over 80%. ;).

Can 80% of the world's major central banks' QE and/or YCC overcome Sir Powell's toughness on fungible risky asset prices?

Gold and crypto are fungible global risky assets. Satoshis and gold bars are the same in New York, London, Frankfurt, Tokyo, and Shanghai.

As more Euros, Yen, Renminbi, and Pounds are printed, people will move their savings into Dollars or other stores of value. As the Fed raises rates and reduces its balance sheet, the USD will strengthen. Gold/EUR and BTC/JPY may also attract buyers.

Gold and crypto markets are much smaller than the trillions in fiat money that will be printed, so they will appreciate in non-USD currencies. These flows only matter in one instance because we trade the global or USD price. Arbitrage occurs when BTC/EUR rises faster than EUR/USD. Here is how it works:

1. An investor based in the USD notices that BTC is expensive in EUR terms.

2. Instead of buying BTC, this investor borrows USD and then sells it.

3. After that, they sell BTC and buy EUR.

4. Then they choose to sell EUR and buy USD.

5. The investor receives their profit after repaying the USD loan.

This triangular FX arbitrage will align the global/USD BTC price with the elevated EUR, JPY, CNY, and GBP prices.

Even if the Fed continues QT, which I doubt they can do past early 2023, small stores of value like gold and Bitcoin may rise as non-Fed central banks get serious about printing money.

“Arthur, this is just more copium,” you might retort.

Patience. This takes time. Economic and political forcing functions take time. The BOE example shows that bond markets will reject politicians' policies to appease voters. Decades of bad energy policy have no immediate fix. Money printing is the only politically viable option. Bond yields will rise as bond markets see more stimulative budgets, and the over-leveraged fiat debt-based financial system will collapse quickly, followed by a monetary bailout.

America has enough food, fuel, and people. China, Europe, Japan, and the UK suffer. America can be autonomous. Thus, the Fed can prioritize domestic political inflation concerns over supplying the world (and most of its allies) with dollars. A steady flow of dollars allows other nations to print their currencies and buy energy in USD. If the strongest player wins, everyone else loses.

I'm making a GDP-weighted index of these five central banks' money printing. When ready, I'll share its rate of change. This will show when the 80%'s money printing exceeds the Fed's tightening.

Better technology and lower launch costs revive science-fiction tech.

Airbus engineers showed off sustainable energy's future in Munich last month. They captured sunlight with solar panels, turned it into microwaves, and beamed it into an airplane hangar, where it lighted a city model. The test delivered 2 kW across 36 meters, but it posed a serious question: Should we send enormous satellites to capture solar energy in space? In orbit, free of clouds and nighttime, they could create power 24/7 and send it to Earth.

Airbus engineer Jean-Dominique Coste calls it an engineering problem. “But it’s never been done at [large] scale.”

Proponents of space solar power say the demand for green energy, cheaper space access, and improved technology might change that. Once someone invests commercially, it will grow. Former NASA researcher John Mankins says it might be a trillion-dollar industry.

Myriad uncertainties remain, including whether beaming gigawatts of power to Earth can be done efficiently and without burning birds or people. Concept papers are being replaced with ground and space testing. The European Space Agency (ESA), which supported the Munich demo, will propose ground tests to member nations next month. The U.K. government offered £6 million to evaluate innovations this year. Chinese, Japanese, South Korean, and U.S. agencies are working. NASA policy analyst Nikolai Joseph, author of an upcoming assessment, thinks the conversation's tone has altered. What formerly appeared unattainable may now be a matter of "bringing it all together"

NASA studied space solar power during the mid-1970s fuel crunch. A projected space demonstration trip using 1970s technology would have cost $1 trillion. According to Mankins, the idea is taboo in the agency.

Space and solar power technology have evolved. Photovoltaic (PV) solar cell efficiency has increased 25% over the past decade, Jones claims. Telecoms use microwave transmitters and receivers. Robots designed to repair and refuel spacecraft might create solar panels.

Falling launch costs have boosted the idea. A solar power satellite large enough to replace a nuclear or coal plant would require hundreds of launches. ESA scientist Sanjay Vijendran: "It would require a massive construction complex in orbit."

SpaceX has made the idea more plausible. A SpaceX Falcon 9 rocket costs $2600 per kilogram, less than 5% of what the Space Shuttle did, and the company promised $10 per kilogram for its giant Starship, slated to launch this year. Jones: "It changes the equation." "Economics rules"

Mass production reduces space hardware costs. Satellites are one-offs made with pricey space-rated parts. Mars rover Perseverance cost $2 million per kilogram. SpaceX's Starlink satellites cost less than $1000 per kilogram. This strategy may work for massive space buildings consisting of many identical low-cost components, Mankins has long contended. Low-cost launches and "hypermodularity" make space solar power economical, he claims.

Better engineering can improve economics. Coste says Airbus's Munich trial was 5% efficient, comparing solar input to electricity production. When the Sun shines, ground-based solar arrays perform better. Studies show space solar might compete with existing energy sources on price if it reaches 20% efficiency.

Lighter parts reduce costs. "Sandwich panels" with PV cells on one side, electronics in the middle, and a microwave transmitter on the other could help. Thousands of them build a solar satellite without heavy wiring to move power. In 2020, a team from the U.S. Naval Research Laboratory (NRL) flew on the Air Force's X-37B space plane.

NRL project head Paul Jaffe said the satellite is still providing data. The panel converts solar power into microwaves at 8% efficiency, but not to Earth. The Air Force expects to test a beaming sandwich panel next year. MIT will launch its prototype panel with SpaceX in December.

As a satellite orbits, the PV side of sandwich panels sometimes faces away from the Sun since the microwave side must always face Earth. To maintain 24-hour power, a satellite needs mirrors to keep that side illuminated and focus light on the PV. In a 2012 NASA study by Mankins, a bowl-shaped device with thousands of thin-film mirrors focuses light onto the PV array.

International Electric Company's Ian Cash has a new strategy. His proposed satellite uses enormous, fixed mirrors to redirect light onto a PV and microwave array while the structure spins (see graphic, above). 1 billion minuscule perpendicular antennas act as a "phased array" to electronically guide the beam toward Earth, regardless of the satellite's orientation. This design, argues Cash, is "the most competitive economically"

If a space-based power plant ever flies, its power must be delivered securely and efficiently. Jaffe's team at NRL just beamed 1.6 kW over 1 km, and teams in Japan, China, and South Korea have comparable attempts. Transmitters and receivers lose half their input power. Vijendran says space solar beaming needs 75% efficiency, "preferably 90%."

Beaming gigawatts through the atmosphere demands testing. Most designs aim to produce a beam kilometers wide so every ship, plane, human, or bird that strays into it only receives a tiny—hopefully harmless—portion of the 2-gigawatt transmission. Receiving antennas are cheap to build but require a lot of land, adds Jones. You could grow crops under them or place them offshore.

Europe's public agencies currently prioritize space solar power. Jones: "There's a devotion you don't see in the U.S." ESA commissioned two solar cost/benefit studies last year. Vijendran claims it might match ground-based renewables' cost. Even at a higher price, equivalent to nuclear, its 24/7 availability would make it competitive.

ESA will urge member states in November to fund a technical assessment. If the news is good, the agency will plan for 2025. With €15 billion to €20 billion, ESA may launch a megawatt-scale demonstration facility by 2030 and a gigawatt-scale facility by 2040. "Moonshot"