Dark Web activity? Is it as horrible as they say?

We peruse our phones for hours. Internet has improved our worldview.

However, the world's harshest realities remain buried on the internet and unattainable by everyone.

Browsers cannot access the Dark Web. Browse it with high-security authentication and exclusive access. There are compelling reasons to avoid the dark web at all costs.

1. The Dark Web and I

Darius wrote My Dark Web Story on reddit two years ago. The user claimed to have shared his dark web experience. DaRealEddyYT wanted to surf the dark web after hearing several stories.

He curiously downloaded Tor Browser, which provides anonymity and security.

In the Dark Room, bound

As Darius logged in, a text popped up: “Want a surprise? Click on this link.”

The link opened to a room with a chair. Only one light source illuminated the room. The chair held a female tied.

As the screen read "Let the game begin," a man entered the room and was paid in bitcoins to torment the girl.

The man dragged and tortured the woman.

A danger to safety

Leaving so soon, Darius, disgusted Darius tried to leave the stream. The anonymous user then sent Darius his personal information, including his address, which frightened him because he didn't know Tor was insecure.

After deleting the app, his phone camera was compromised.

He also stated that he left his residence and returned to find it unlocked and a letter saying, Thought we wouldn't find you? Reddit never updated the story.

The story may have been a fake, but a much scarier true story about the dark side of the internet exists.

2. The Silk Road Market

The dark web is restricted for a reason. The dark web has everything illicit imaginable. It's awful central.

The dark web has everything, from organ sales to drug trafficking to money laundering to human trafficking. Illegal drugs, pirated software, credit card, bank, and personal information can be found in seconds.

The dark web has reserved websites like Google. The Silk Road Website, which operated from 2011 to 2013, was a leading digital black market.

The FBI grew obsessed with site founder and processor Ross William Ulbricht.

The site became a criminal organization as money laundering and black enterprises increased. Bitcoin was utilized for credit card payment.

The FBI was close to arresting the site's administrator. Ross was detained after the agency closed Silk Road in 2013.

Two years later, in 2015, he was convicted and sentenced to two consecutive life terms and forty years. He appealed in 2016 but was denied, thus he is currently serving time.

The hefty sentence was for more than running a black marketing site. He was also convicted of murder-for-hire, earning about $730,000 in a short time.

3. Person-buying auctions

Bidding on individuals is another weird internet activity. After a Milan photo shoot, 20-year-old British model Chloe Ayling was kidnapped.

An ad agency in Milan made a bogus offer to shoot with the mother of a two-year-old boy. Four men gave her anesthetic and put her in a duffel bag when she arrived.

She was held captive for several days, and her images and $300,000 price were posted on the dark web. Black Death Trafficking Group kidnapped her to sell her for sex.

She was told two black death foot warriors abducted her. The captors released her when they found she was a mother because mothers were less desirable to sex slave buyers.

In July 2018, Lukasz Pawel Herba was arrested and sentenced to 16 years and nine months in prison. Being a young mother saved Chloe from creepy bidding.

However, it exceeds expectations of how many more would be in such danger daily without their knowledge.

4. Organ sales

Many are unaware of dark web organ sales. Patients who cannot acquire organs often turn to dark web brokers.

Brokers handle all transactions between donors and customers.

Bitcoins are used for dark web transactions, and the Tor server permits personal data on the web.

The WHO reports approximately 10,000 unlawful organ transplants annually. The black web sells kidneys, hearts, even eyes.

To protect our lives and privacy, we should manage our curiosity and never look up dangerous stuff.

While it's fascinating and appealing to know what's going on in the world we don't know about, it's best to prioritize our well-being because one never knows how bad it might get.

Sources

Reddit.com

The Daily Beast

PYMNTS

Commons.erau.edu

The Sun

Investopedia

Startup Talky

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.

Apocodes helps entrepreneurs create SaaS products without writing code. This post introduces micro-SaaS and outlines its basic strategy.

Strategy

Vision and strategy differ when starting a startup.

• The company's long-term future state is outlined in the vision. It establishes the overarching objectives the organization aims to achieve while also justifying its existence. The company's future is outlined in the vision.

• The strategy consists of a collection of short- to mid-term objectives, the accomplishment of which will move the business closer to its vision. The company gets there through its strategy.

The vision should be stable, but the strategy must be adjusted based on customer input, market conditions, or previous experiments.

Begin modestly and aim high.

Be truthful. It's impossible to automate SaaS product creation from scratch. It's like climbing Everest without running a 5K. Physical rules don't prohibit it, but it would be suicide.

Apocodes 5K equivalent? Two options:

• (A) Create a feature that includes every setting option conceivable. then query potential clients “Would you choose us to build your SaaS solution if we offered 99 additional features of the same caliber?” After that, decide which major feature to implement next.

• (B) Build a few straightforward features with just one or two configuration options. Then query potential clients “Will this suffice to make your product?” What's missing if not? Finally, tweak the final result a bit before starting over.

(A) is an all-or-nothing approach. It's like training your left arm to climb Mount Everest. My right foot is next.

(B) is a better method because it's iterative and provides value to customers throughout.

Focus on a small market sector, meet its needs, and expand gradually. Micro-SaaS is Apocode's first market.

What is micro-SaaS.

Micro-SaaS enterprises have these characteristics:

• A limited range: They address a specific problem with a small number of features.

• A small group of one to five individuals.

• Low external funding: The majority of micro-SaaS companies have Total Addressable Markets (TAM) under $100 million. Investors find them unattractive as a result. As a result, the majority of micro-SaaS companies are self-funded or bootstrapped.

• Low competition: Because they solve problems that larger firms would rather not spend time on, micro-SaaS enterprises have little rivalry.

• Low upkeep: Because of their simplicity, they require little care.

• Huge profitability: Because providing more clients incurs such a small incremental cost, high profit margins are possible.

Micro-SaaS enterprises created with no-code are Apocode's ideal first market niche.

We'll create our own micro-SaaS solutions to better understand their needs. Although not required, we believe this will improve community discussions.

The challenge

In 100 days (September 12–December 20, 2022), we plan to build 10 micro-SaaS enterprises using Apocode.

They will be:

• Self-serve: Customers will be able to use the entire product experience without our manual assistance.

• Real: They'll deal with actual issues. They won't be isolated proofs of concept because we'll keep up with them after the challenge.

• Both free and paid options: including a free plan and a free trial period. Although financial success would be a good result, the challenge's stated objective is not financial success.

This will let us design Apocodes features, showcase them, and talk to customers.

(Edit: The first micro-SaaS was launched!)

Follow along

If you want to follow the story of Apocode or our progress in this challenge, you can subscribe here.

If you are interested in using Apocode, sign up here.

If you want to provide feedback, discuss the idea further or get involved, email me at nicolas.tresegnie@gmail.com

He built a $40M hip-hop empire from street drug dealing.

50 Cent was nearly killed by 9mm bullets.

Before 50 Cent, Curtis Jackson sold drugs.

He sold coke to worried addicts after being orphaned at 8.

Pursuing police. Murderous hustlers and gangs. Unwitting informers.

Despite his hard life, his hip-hop career was a success.

An assassination attempt ended his career at the start.

What sane producer would want to deal with a man entrenched in crime?

Most would have drowned in self-pity and drank themselves to death.

But 50 Cent isn't most people. Life on the streets had given him fearlessness.

“Having a brush with death, or being reminded in a dramatic way of the shortness of our lives, can have a positive, therapeutic effect. So it is best to make every moment count, to have a sense of urgency about life.” ― 50 Cent, The 50th Law

50 released a series of mixtapes that caught Eminem's attention and earned him a $50 million deal!

50 Cents turned death into life.

Things happen; that is life.

We want problems solved.

Every human has problems, whether it's Jeff Bezos swimming in his billions, Obama in his comfortable retirement home, or Dan Bilzerian with his hired bikini models.

All problems.

Problems churn through life. solve one, another appears.

It's harsh. Life's unfair. We can face reality or run from it.

The latter will worsen your issues.

“The firmer your grasp on reality, the more power you will have to alter it for your purposes.” — 50 Cent, The 50th Law

In a fantasy-obsessed world, 50 Cent loves reality.

Wish for better problem-solving skills rather than problem-free living.

Don't wish, work.

We All Have the True Power of Alchemy

Humans are arrogant enough to think the universe cares about them.

That things happen as if the universe notices our nanosecond existences.

Things simply happen. Period.

By changing our perspective, we can turn good things bad.

The alchemists' search for the philosopher's stone may have symbolized the ability to turn our lead-like perceptions into gold.

Negativity bias tints our perceptions.

Normal sparring broke your elbow? Rest and rethink your training. Fired? You can improve your skills and get a better job.

Consider Curtis if he had fallen into despair.

The legend we call 50 Cent wouldn’t have existed.

The Best Lesson in Life Ever?

Neither avoid nor fear your reality.

That simple sentence contains every self-help tip and life lesson on Earth.

When reality is all there is, why fear it? avoidance?

Or worse, fleeing?

To accept reality, we must eliminate the words should be, could be, wish it were, and hope it will be.

It is. Period.

Only by accepting reality's chaos can you shape your life.

“Behind me is infinite power. Before me is endless possibility, around me is boundless opportunity. My strength is mental, physical and spiritual.” — 50 Cent

Scaling your startup requires answering customer demands, not growth tricks.

I cofounded Freedo Rentals in 2019. I reached 50 lakh+ ARR in 6 months before quitting owing to the epidemic.

Freedo aimed to solve 2 customer pain points:

• Users lacked a reliable last-mile transportation option.

• The amount that Auto walas charge for unmetered services

Solution?

Effectively simple.

Build ports at high-demand spots (colleges, residential societies, metros). Electric ride-sharing can meet demand.

We had many problems scaling. I'll explain using the AARRR model.

• Brand unfamiliarity or a novel product offering were the problems with awareness. Nobody knew what Freedo was or what it did.

• Problem with awareness: Content and advertisements did a poor job of communicating the task at hand. The advertisements clashed with the white-collar part because they were too cheesy.

• Retention Issue: We encountered issues, indicating that the product was insufficient. Problems with keyless entry, creating bills, stealing helmets, etc.

• Retention/Revenue Issue: Costly compared to established rivals. Shared cars were 1/3 of our cost.

• Referral Issue: Missing the opportunity to seize the AHA moment. After the ride, nobody remembered us.

Once you know where you're struggling with AARRR, iterative solutions are usually best.

Once you have nailed the AARRR model, most startups use paid channels to scale. This dependence, on paid channels, increases with scale unless you crack your organic/inbound game.

Over-index growth loops. Growth loops increase inflow and customers as you scale.

When considering growth, ask yourself:

• Who is the solution's ICP (Ideal Customer Profile)? (To whom are you selling)

• What are the most important messages I should convey to customers? (This is an A/B test.)

• Which marketing channels ought I prioritize? (Conduct analysis based on the startup's maturity/stage.)

• Choose the important metrics to monitor for your AARRR funnel (not all metrics are equal)

• Identify the Flywheel effect's growth loops (inertia matters)

My biggest mistakes:

• not paying attention to consumer comments or satisfaction. It is the main cause of problems with referrals, retention, and acquisition for startups. Beyond your NPS, you should consider second-order consequences.

• The tasks at hand should be quite clear.

Here's my scaling equation:

Growth = A x B x C

A = Funnel top (Traffic)

B = Product Valuation (Solving a real pain point)

C = Aha! (Emotional response)

Freedo's A, B, and C created a unique offering.

Freedo’s ABC:

A — Working or Studying population in NCR

B — Electric Vehicles provide last-mile mobility as a clean and affordable solution

C — One click booking with a no-noise scooter

Final outcome:

FWe scaled Freedo to Rs. 50 lakh MRR and were growing 60% month on month till the pandemic ceased our growth story.

How we did it?

We tried ambassadors and coupons. WhatsApp was our most successful A/B test.

We grew widespread adoption through college and society WhatsApp groups. We requested users for referrals in community groups.

What worked for us won't work for others. This scale underwent many revisions.

Every firm is different, thus you must know your customers. Needs to determine which channel to prioritize and when.

Users desired a safe, time-bound means to get there.

This (not mine) growth framework helped me a lot. You should follow suit.

Massive Layoffs, record low VC investment, debate over crash... why is it happening and what’s the endgame?

This article generalizes a diverse industry. For objectivity, specific tech company challenges like growing competition within named segments won't be considered. Please comment on the posts.

According to Layoffs.fyi, nearly 120.000 people have been fired from startups since March 2020. More than 700 startups have fired 1% to 100% of their workforce. "The tech market is crashing"

Venture capital investment dropped 19% QoQ in the first four months of 2022, a 2018 low. Since January 2022, Nasdaq has dropped 27%. Some believe the tech market is collapsing.

It's bad, but nothing has crashed yet. We're about to get super technical, so buckle up!

I've written a follow-up article about what's next. For a more optimistic view of the crisis' aftermath, see: Tech Diaspora and Silicon Valley crisis

What happened?

Insanity reigned. Last decade, everyone became a unicorn. Seed investments can be made without a product or team. While the "real world" economy suffered from the pandemic for three years, tech companies enjoyed the "new normal."

COVID sped up technology adoption on several fronts, but this "new normal" wasn't so new after many restrictions were lifted. Worse, it lived with disrupted logistics chains, high oil prices, and WW3. The consumer market has felt the industry's boom for almost 3 years. Inflation, unemployment, mental distress...what looked like a fast economic recovery now looks like unfulfilled promises.

People rethink everything they eat. Paying a Netflix subscription instead of buying beef is moronic if you can watch it for free on your cousin’s account. No matter how great your real estate app's UI is, buying a house can wait until mortgage rates drop. PLGProduct Led Growth (PLG) isn't the go-to strategy when consumers have more basic expense priorities.

Exponential growth and investment

Until recently, tech companies believed that non-exponential revenue growth was fatal. Exponential growth entails doing more with less. From Salim Ismail words:

An Exponential Organization (ExO) has 10x the impact of its peers.

Many tech companies' theories are far from reality.

Investors have funded (sometimes non-exponential) growth. Scale-driven companies throw people at problems until they're solved. Need an entire closing team because you’ve just bought a TV prime time add? Sure. Want gold-weight engineers to colorize buttons? Why not?

Tech companies don't need cash flow to do it; they can just show revenue growth and get funding. Even though it's hard to get funding, this was the market's momentum until recently.

The graph at the beginning of this section shows how industry heavyweights burned money until 2020, despite being far from their market-share seed stage. Being big and being sturdy are different things, and a lot of the tech startups out there are paper tigers. Without investor money, they have no foundation.

A little bit about interest rates

Inflation-driven high interest rates are said to be causing tough times. Investors would rather leave money in the bank than spend it (I myself said it some days ago). It’s not wrong, but it’s also not that simple.

The USA central bank (FED) is a good proxy of global economics. Dollar treasury bonds are the safest investment in the world. Buying U.S. debt, the only country that can print dollars, guarantees payment.

The graph above shows that FED interest rates are low and 10+ year bond yields are near 2018 levels. Nobody was firing at 2018. What’s with that then?

Full explanation is too technical for this article, so I'll just summarize: Bond yields rise due to lack of demand or market expectations of longer-lasting inflation. Safe assets aren't a "easy money" tactic for investors. If that were true, we'd have seen the current scenario before.

Long-term investors are protecting their capital from inflation.

Not a crash, a landing

I bombarded you with info... Let's review:

• Consumption is down, hurting revenue.

• Tech companies of all ages have been hiring to grow revenue at the expense of profit.

• Investors expect inflation to last longer, reducing future investment gains.

Inflation puts pressure on a wheel that was rolling full speed not long ago. Investment spurs hiring, growth, and more investment. Worried investors and consumers reduce the cycle, and hiring follows.

Long-term investors back startups. When the invested company goes public or is sold, it's ok to burn money. What happens when the payoff gets further away? What if all that money sinks? Investors want immediate returns.

Why isn't the market crashing? Technology is not losing capital. It’s expecting change. The market realizes it threw moderation out the window and is reversing course. Profitability is back on the menu.

People solve problems and make money, but they also cost money. Huge cost for the tech industry. Engineers, Product Managers, and Designers earn up to 100% more than similar roles. Businesses must be careful about who they keep and in what positions to avoid wasting money.

What the future holds

From here on, it's all speculation. I found many great articles while researching this piece. Some are cited, others aren't (like this and this). We're in an adjustment period that may or may not last long.

Big companies aren't laying off many workers. Netflix firing 100 people makes headlines, but it's only 1% of their workforce. The biggest seem to prefer not hiring over firing.

Smaller startups beyond the seeding stage may be hardest hit. Without structure or product maturity, many will die.

I expect layoffs to continue for some time, even at Meta or Amazon. I don't see any industry names falling like they did during the .com crisis, but the market will shrink.

If you are currently employed, think twice before moving out and where to.
If you've been fired, hurry, there are still many opportunities.
If you're considering a tech career, wait.
If you're starting a business, I respect you. Good luck.