While we played checkers, we thought billionaires played 3D chess. They're playing the same game on a fancier board.

Every medium has nuances and norms. Texting is authentic and casual. A smaller circle has access, creating intimacy and immediacy. Most people read all their texts, but not all their email and mail. Many of us no longer listen to our voicemails, and calling your kids ages you.

Live interviews and testimony under oath inspire real moments, rare in a world where communications departments sanitize everything powerful people say. When (some of) Elon's text messages became public in Twitter v. Musk, we got a glimpse into tech power. It's bowels.

These texts illuminate the tech community's upper caste.

Checkers, Not Chess

Elon texts with Larry Ellison, Joe Rogan, Sam Bankman-Fried, Satya Nadella, and Jack Dorsey. They reveal astounding logic, prose, and discourse. The world's richest man and his followers are unsophisticated, obtuse, and petty. Possibly. While we played checkers, we thought billionaires played 3D chess. They're playing the same game on a fancier board.

They fumble with their computers.

They lean on others to get jobs for their kids (no surprise).

No matter how rich, they always could use more (money).

Differences A social hierarchy exists. Among this circle, the currency of deference is... currency. Money increases sycophantry. Oculus and Elon's "friends'" texts induce nausea.

Autocorrect frustrates everyone.

Elon doesn't stand out to me in these texts; he comes off mostly OK in my view. It’s the people around him. It seems our idolatry of innovators has infected the uber-wealthy, giving them an uncontrollable urge to kill the cool kid for a seat at his cafeteria table. "I'd grenade for you." If someone says this and they're not fighting you, they're a fan, not a friend.

Many powerful people are undone by their fake friends. Facilitators, not well-wishers. When Elon-Twitter started, I wrote about power. Unchecked power is intoxicating. This is a scientific fact, not a thesis. Power causes us to downplay risk, magnify rewards, and act on instincts more quickly. You lose self-control and must rely on others.

You'd hope the world's richest person has advisers who push back when necessary (i.e., not yes men). Elon's reckless, childish behavior and these texts show there is no truth-teller. I found just one pushback in the 151-page document. It came from Twitter CEO Parag Agrawal, who, in response to Elon’s unhelpful “Is Twitter dying?” tweet, let Elon know what he thought: It was unhelpful. Elon’s response? A childish, terse insult.

Scale

The texts are mostly unremarkable. There are some, however, that do remind us the (super-)rich are different. Specifically, the discussions of possible equity investments from crypto-billionaire Sam Bankman-Fried (“Does he have huge amounts of money?”) and this exchange with Larry Ellison:

Ellison, who co-founded $175 billion Oracle, is wealthy. Less clear is whether he can text a billion dollars. Who hasn't been texted $1 billion? Ellison offered 8,000 times the median American's net worth, enough to buy 3,000 Ferraris or the Chicago Blackhawks. It's a bedrock principle of capitalism to have incredibly successful people who are exponentially wealthier than the rest of us. It creates an incentive structure that inspires productivity and prosperity. When people offer billions over text to help a billionaire's vanity project in a country where 1 in 5 children are food insecure, isn't America messed up?

Elon's Morgan Stanley banker, Michael Grimes, tells him that Web3 ventures investor Bankman-Fried can invest $5 billion in the deal: “could do $5bn if everything vision lock... Believes in your mission." The message bothers Elon. In Elon's world, $5 billion doesn't warrant a worded response. $5 billion is more than many small nations' GDP, twice the SEC budget, and five times the NRC budget.

If income inequality worries you after reading this, trust your gut.

Billionaires aren't like the rich.

As an entrepreneur, academic, and investor, I've met modest-income people, rich people, and billionaires. Rich people seem different to me. They're smarter and harder working than most Americans. Monty Burns from The Simpsons is a cartoon about rich people. Rich people have character and know how to make friends. Success requires supporters.

I've never noticed a talent or intelligence gap between wealthy and ultra-wealthy people. Conflating talent and luck infects the tech elite. Timing is more important than incremental intelligence when going from millions to hundreds of millions or billions. Proof? Elon's texting. Any man who electrifies the auto industry and lands two rockets on barges is a genius. His mega-billions come from a well-regulated capital market, enforceable contracts, thousands of workers, and billions of dollars in government subsidies, including a $465 million DOE loan that allowed Tesla to produce the Model S. So, is Mr. Musk a genius or an impressive man in a unique time and place?

The Point

Elon's texts taught us more? He can't "fix" Twitter. For two weeks in April, he was all in on blockchain Twitter, brainstorming Dogecoin payments for tweets with his brother — i.e., paid speech — while telling Twitter's board he was going to make a hostile tender offer. Kimbal approved. By May, he was over crypto and "laborious blockchain debates." (Mood.)

Elon asked the Twitter CEO for "an update from the Twitter engineering team" No record shows if he got the meeting. It doesn't "fix" Twitter either. And this is Elon's problem. He's a grown-up child with all the toys and no boundaries. His yes-men encourage his most facile thoughts, and shitposts and errant behavior diminish his genius and ours.

Post-Apocalyptic

The universe's titans have a sense of humor.

Every day, we must ask: Who keeps me real? Who will disagree with me? Who will save me from my psychosis, which has brought down so many successful people? Elon Musk doesn't need anyone to jump on a grenade for him; he needs to stop throwing them because one will explode in his hand.

Economic mutual destruction.

Multibillion-dollar corporations never bothered with an independent audit, and they all lied about their balance sheets.

It's easy to forget that the Big Four accounting firms are lousy fraud enablers. Just because they sign off on your books doesn't mean you're not a hoax waiting to erupt.

This is *crazy* Capitalism depends on independent auditors. Rich folks need to know their financial advisers aren't lying. Rich folks usually succeed.

No accounting. EY, KPMG, PWC, and Deloitte make more money consulting firms than signing off on their accounts.

The Big Four sign off on phony books because failing to make friends with unscrupulous corporations may cost them consulting contracts.

The Big Four are the only firms big enough to oversee bankruptcy when they sign off on fraudulent books, as they did for Carillion in 2018. All four profited from Carillion's bankruptcy.

The Big Four are corrupt without any consequences for misconduct. Who can forget when KPMG's top management was fined millions for helping auditors cheat on ethics exams?

Consulting and auditing conflict. Consultants help a firm cover its evil activities, such as tax fraud or wage theft, whereas auditors add clarity to a company's finances. The Big Four make more money from cooking books than from uncooking them, thus they are constantly embroiled in scandals.

If a major scandal breaks, it may bring down the entire sector and substantial parts of the economy. Jim Peterson explains system risk for The Dig.

The Big Four are voluntary private partnerships where accountants invest their time, reputations, and money. If a controversy threatens the business, partners who depart may avoid scandal and financial disaster.

When disaster looms, each partner should bolt for the door, even if a disciplined stay-and-hold posture could weather the storm. This happened to Arthur Andersen during Enron's collapse, and a 2006 EU report recognized the risk to other corporations.

Each partner at a huge firm knows how much dirty laundry they've buried in the company's garden, and they have well-founded suspicions about what other partners have buried, too. When someone digs, everyone runs.

If a firm confronts substantial litigation damages or enforcement penalties, it could trigger the collapse of one of the Big Four. That would be bad news for the firm's clients, who would have trouble finding another big auditor.

Most of the world's auditing capacity is concentrated in four enormous, brittle, opaque, compromised organizations. If one of them goes bankrupt, the other three won't be able to take on its clients.

Peterson: Another collapse would strand many of the world's large public businesses, leaving them unable to obtain audit views for their securities listings and regulatory compliance.

Count Down: The Past, Present, and Uncertain Future of the Big Four Accounting Firms is in its second edition.

https://www.emerald.com/insight/publication/doi/10.1108/9781787147003

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.

No Paid Ads Required

I had an online store. After a year of running the company alongside my 9-to-5, I made enough to resign.

That day was amazing.

This Instagram marketing plan helped the store succeed.

How did I increase my sales to five figures a month without using any paid advertising?

I used customer event marketing.

I'm not sure this term exists. I invented it to describe what I was doing.

Instagram word-of-mouth, fan engagement, and interaction drove sales.

If a customer liked or disliked a product, the buzz would drive attention to the store.

I used customer-based events to increase engagement and store sales.

Success!

Here are the weekly Instagram customer events I coordinated while running my business:

• Be the Buyer Days

• Flash sales

• Mystery boxes

Be the Buyer Days: How do they work?

Be the Buyer Days are exactly that.

You choose a day to share stock selections with social media followers.

This is an easy approach to engaging customers and getting fans enthusiastic about new releases.

First, pick a handful of items you’re considering ordering. I’d usually pick around 3 for Be the Buyer Day.

Then I'd poll the crowd on Instagram to vote on their favorites.

This was before Instagram stories, polls, and all the other cool features Instagram offers today. I think using these tools now would make this event even better.

I'd ask customers their favorite back then.

The growing comments excited customers.

Then I'd declare the winner, acquire the products, and start selling it.

How do flash sales work?

I mostly ran flash sales.

You choose a limited number of itemsdd for a few-hour sale.

We wanted most sales to result in sold-out items.

When an item sells out, it contributes to the sensation of scarcity and can inspire customers to visit your store to buy a comparable product, join your email list, become a fan, etc.

We hoped they'd act quickly.

I'd hold flash deals twice a week, which generated scarcity and boosted sales.

The store had a few thousand Instagram followers when I started flash deals.

Each flash sale item would make $400 to $600.

$400 x 3= $1,200

That's $1,200 on social media!

Twice a week, you'll make roughly $10K a month from Instagram.

$1,200/day x 8 events/month=$9,600

Flash sales did great.

We held weekly flash deals and sent social media and email reminders. That’s about it!

How are mystery boxes put together?

All you do is package a box of store products and sell it as a mystery box on TikTok or retail websites.

A $100 mystery box would cost $30.

You're discounting high-value boxes.

This is a clever approach to get rid of excess inventory and makes customers happy.

It worked!

Be the Buyer Days, flash deals, and mystery boxes helped build my company without paid advertisements.

All companies can use customer event marketing. Involving customers and providing an engaging environment can boost sales.

Try it!

I've been pondering product strategy and how to articulate it. Frameworks helped guide our thinking.

If your teams aren't working together or there's no clear path to victory, your product strategy may not be well-articulated or communicated (if you have one).

Before diving into a product strategy's details, it's important to understand its role in the bigger picture — the pieces that move your organization forward.

the overall picture

A product strategy is crucial, in my opinion. It's part of a successful product or business. It's the showpiece.

To simplify, we'll discuss four main components:

1. Vision

2. Product Management

3. Goals

4. Roadmap

Vision

Your company's mission? Your company/product in 35 years? Which headlines?

The vision defines everything your organization will do in the long term. It shows how your company impacted the world. It's your organization's rallying cry.

An ambitious but realistic vision is needed.

Without a clear vision, your product strategy may be inconsistent.

Product Management

Our main subject. Product strategy connects everything. It fulfills the vision.

In Part 2, we'll discuss product strategy.

Goals

This component can be goals, objectives, key results, targets, milestones, or whatever goal-tracking framework works best for your organization.

These product strategy metrics will help your team prioritize strategies and roadmaps.

Your company's goals should be unified. This fuels success.

Roadmap

The roadmap is your product strategy's timeline. It provides a prioritized view of your team's upcoming deliverables.

A roadmap is time-bound and includes measurable goals for your company. Your team's steps and capabilities for executing product strategy.

If your team has trouble prioritizing or defining a roadmap, your product strategy or vision is likely unclear.

Formulation of a Product Strategy

Now that we've discussed where your product strategy fits in the big picture, let's look at a framework.

A product strategy should include challenges, an approach, and actions.

Challenges

First, analyze the problems/situations you're solving. It can be customer- or company-focused.

The analysis should explain the problems and why they're important. Try to simplify the situation and identify critical aspects.

Some questions:

• What issues are we attempting to resolve?

• What obstacles—internal or otherwise—are we attempting to overcome?

• What is the opportunity, and why should we pursue it, in your opinion?

Decided Method

Second, describe your approach. This can be a set of company policies for handling the challenge. It's the overall approach to the first part's analysis.

The approach can be your company's bets, the solutions you've found, or how you'll solve the problems you've identified.

Again, these questions can help:

• What is the value that we hope to offer to our clients?

• Which market are we focusing on first?

• What makes us stand out? Our benefit over rivals?

Actions

Third, identify actions that result from your approach. Second-part actions should be these.

Coordinate these actions. You may need to add products or features to your roadmap, acquire new capabilities through partnerships, or launch new marketing campaigns. Whatever fits your challenges and strategy.

Final questions:

• What skills do we need to develop or obtain?

• What is the chosen remedy? What are the main outputs?

• What else ought to be added to our road map?

Put everything together

… and iterate!

Strategy isn't one-and-done. Changes occur. Economies change. Competitors emerge. Customer expectations change.

One unexpected event can make strategies obsolete quickly. Muscle it. Review, evaluate, and course-correct your strategies with your teams. Quarterly works. In a new or unstable industry, more often.