I placed a link on HackerNews and got 300 signups in a week. This post explains what happened.

Product Concept

The product is DbSchemaLibrary. A library of Database Schema.

I'm not sure where this idea originated from. Very fast. Build fast, fail fast, test many ideas, and one will be a hit. I tried it. Let's try it anyway, even though it'll probably fail. I finished The Lean Startup book and wanted to use it.

Database job bores me. Important! I get drowsy working on it. Someone must do it. I remember this happening once. I needed examples at the time. Something similar to Recall (my other project) that I can copy — or at least use as a reference.

Frequently googled. Many tabs open. The results were useless. I raised my hand and agreed to construct the database myself.

It resurfaced. I decided to do something.

Due Diligence

Lean Startup emphasizes validated learning. Everything the startup does should result in learning. I may build something nobody wants otherwise. That's what happened to Recall.

So, I wrote a business plan document. This happens before I code. What am I solving? What is my proposed solution? What is the leap of faith between the problem and solution? Who would be my target audience?

My note:

In my previous project, I did the opposite!

I wrote my expectations after reading the book's advice.

“Failure is a prerequisite to learning. The problem with the notion of shipping a product and then seeing what happens is that you are guaranteed to succeed — at seeing what happens.” — The Lean Startup book

These are successful metrics. If I don't reach them, I'll drop the idea and try another. I didn't understand numbers then. Below are guesses. But it’s a start!

I then wrote the project's What and Why. I'll use this everywhere. Before, I wrote a different pitch each time. I thought certain words would be better. I felt the audience might want something unusual.

Occasionally, this works. I'm unsure if it's a good idea. No stats, just my writing-time opinion. Writing every time is time-consuming and sometimes hazardous. Having a copy saved me duplication.

I can measure and learn from performance.

Last, I identified communities that might demand the product. This became an exercise in creativity.

The MVP

So now it’s time to build.

A MVP can test my assumptions. Business may learn from it. Not low-quality. We should learn from the tiniest thing.

I like the example of how Dropbox did theirs. They assumed that if the product works, people will utilize it. How can this be tested without a quality product? They made a movie demonstrating the software's functionality. Who knows how much functionality existed?

So I tested my biggest assumption. Users want schema references. How can I test if users want to reference another schema? I'd love this. Recall taught me that wanting something doesn't mean others do.

I made an email-collection landing page. Describe it briefly. Reference library. Each email sender wants a reference. They're interested in the product. Few other reasons exist.

Header and footer were skipped. No name or logo. DbSchemaLibrary is a name I thought of after the fact. 5-minute logo. I expected a flop. Recall has no users after months of labor. What could happen to a 2-day project?

I didn't compromise learning validation. How many visitors sign up? To draw a conclusion, I must track these results.

Posting Time

Now that the job is done, gauge interest. The next morning, I posted on all my channels. I didn't want to be spammy, therefore it required more time.

I made sure each channel had at least one fan of this product. I also answer people's inquiries in the channel.

My list stinks. Several channels wouldn't work. The product's target market isn't there. Posting there would waste our time. This taught me to create marketing channels depending on my persona.

Statistics! What actually happened

My favorite part! 23 channels received the link.

I stopped posting to Discord despite its high conversion rate. I eliminated some channels because they didn't fit. According to the numbers, some users like it. Most users think it's spam.

I was skeptical. And 12 people viewed it.

I didn't expect much attention on a startup subreddit. I'll likely examine Reddit further in the future. As I have enough info, I didn't post much. Time for the next validated learning

No comment. The post had few views, therefore the numbers are low.

The targeted people come next.

I'm a Toptal freelancer. There's a member-only Slack channel. Most people can't use this marketing channel, but you should! It's not as spectacular as discord's 27% conversion rate. But I think the users here are better.

I don’t really have a following anywhere so this isn’t something I can leverage.

The best yet. 10% is converted. With more data, I expect to attain a 10% conversion rate from other channels. Stable number.

This number required some work. Did you know that people use many different clients to read HN?

Unknowns

Untrackable views and signups abound. 1136 views and 135 signups are untraceable. It's 11%. I bet much of that came from Hackernews.

Overall Statistics

The 7-day signup-to-visit ratio was 17%. (Hourly data points)

First-day percentages were lower, which is noteworthy. Initially, it was little above 10%. The HN post started getting views then.

When traffic drops, the number reaches just around 20%. More individuals are interested in the connection. hn.algolia.com sent 2 visitors. This means people are searching and finding my post.

Interesting discoveries

1. HN post struggled till the US woke up.

11am UTC. After an hour, it lost popularity. It seemed over. 7 signups converted 13%. Not amazing, but I would've thought ahead.

After 4pm UTC, traffic grew again. 4pm UTC is 9am PDT. US awakened. 10am PDT saw 512 views.

2. The product was highlighted in a newsletter.

I found Revue references when gathering data. Newsletter platform. Someone posted the newsletter link. 37 views and 3 registrations.

3. HN numbers are extremely reliable

I don't have a time-lapse graph (yet). The statistics were constant all day.

• 2717 views later 272 new users, or 10.1%

• With 293 signups at 2856 views, 10.25%

• At 306 signups at 2965 views, 10.32%

Learnings

1. My initial estimations were wildly inaccurate

I wrote 30% conversion. Reading some articles, looks like 10% is a good number to aim for.

2. Paying attention to what matters rather than vain metrics

The Lean Startup discourages vanity metrics. Feel-good metrics that don't measure growth or traction. Considering the proportion instead of the total visitors made me realize there was something here.

What’s next?

There are lots of work to do. Data aggregation, display, website development, marketing, legal issues. Fun! It's satisfying to solve an issue rather than investigate its cause.

In the meantime, I’ve already written the first project update in another post. Continue reading it if you’d like to know more about the project itself! Shifting from Quantity to Quality — DbSchemaLibrary

This could cost you.

Content creators may have heard about Google's "Helpful content upgrade."

This change is another Google effort to remove low-quality, repetitive, and AI-generated content.

Why should content creators care?

Because too much content manipulates search results.

My experience includes the following.

Website admins seek high-quality guest posts from me. They send me AI-generated text after I say "yes." My readers are irrelevant. Backlinks are needed.

Companies copy high-ranking content to boost their Google rankings. Unfortunately, it's common.

What does this content offer?

Nothing.

Despite Google's updates and efforts to clean search results, webmasters create manipulative content.

As a marketer, I knew about AI-powered content generation tools. However, I've never tried them.

I use old-fashioned content creation methods to grow my website from 0 to 3,000 monthly views in one year.

Last year, I launched a niche website.

I do keyword research, analyze search intent and competitors' content, write an article, proofread it, and then optimize it.

This strategy is time-consuming.

But it yields results!

Here's proof from Google Analytics:

Proven strategies yield promising results.

To validate my assumptions and find new strategies, I run many experiments.

I tested an AI-powered content generator.

I used a tool to write this Google-optimized article about SEO for startups.

I wanted to analyze AI-generated content's Google performance.

Here are the outcomes of my test.

First, quality.

I dislike "meh" content. I expect articles to answer my questions. If not, I've wasted my time.

My essays usually include research, personal anecdotes, and what I accomplished and achieved.

AI-generated articles aren't as good because they lack individuality.

Read my AI-generated article about startup SEO to see what I mean.

It's dry and shallow, IMO.

It seems robotic.

I'd use quotes and personal experience to show how SEO for startups is different.

My article paraphrases top-ranked articles on a certain topic.

It's readable but useless. Similar articles abound online. Why read it?

AI-generated content is low-quality.

Let me show you how this content ranks on Google.

The Google Search Console report shows impressions, clicks, and average position.

Low numbers.

No one opens the 5th Google search result page to read the article. Too far!

You may say the new article will improve.

Marketing-wise, I doubt it.

This article is shorter and less comprehensive than top-ranking pages. It's unlikely to win because of this.

AI-generated content's terrible reality.

I'll compare how this content I wrote for readers and SEO performs.

Both the AI and my article are fresh, but trends are emerging.

My article's CTR and average position are higher.

I spent a week researching and producing that piece, unlike AI-generated content. My expert perspective and unique consequences make it interesting to read.

Human-made.

In summary

No content generator can duplicate a human's tone, writing style, or creativity. Artificial content is always inferior.

Not "bad," but inferior.

Demand for content production tools will rise despite Google's efforts to eradicate thin content.

Most won't spend hours producing link-building articles. Costly.

As guest and sponsored posts, artificial content will thrive.

Before accepting a new arrangement, content creators and website owners should consider this.

This resulted to obimy's new audience.

Hi! obimy's official account. Here, we'll teach app developers and marketers. In 2022, our downloads increased dramatically, so we'll share what we learned.

obimy is what we call a ‘senseger’. It's a new method to communicate digitally. Instead of text, obimy users connect through senses and moods. Feeling playful? Flirt with your partner, pat a pal, or dump water on a classmate. Each feeling is an interactive animation with vibration. It's a wordless app. App Store and Google Play have obimy.

We had 20,000 users in 2022. Two to five thousand of them opened the app monthly. Our DAU metric was 500.

We have 6 million users after 6 months. 500,000 individuals use obimy daily. obimy was the top lifestyle app this week in the U.S.

And TikTok helped.

TikTok fuels obimys' growth. It's why our app exploded. How and what did we learn? Our Head of Marketing, Anastasia Avramenko, knows.

our actions prior to TikTok

We wanted to achieve product-market fit through organic expansion. Quora, Reddit, Facebook Groups, Facebook Ads, Google Ads, Apple Search Ads, and social media activity were tested. Nothing worked. Our CPI was sometimes $4, so unit economics didn't work.

We studied our markets and made audience hypotheses. We promoted our goods and studied our audience through social media quizzes. Our target demographic was Americans in long-distance relationships. I designed quizzes like Test the Strength of Your Relationship to better understand the user base. After each quiz, we encouraged users to download the app to enhance their connection and bridge the distance.

We got 1,000 responses for $50. This helped us comprehend the audience's grief and coping strategies (aka our rivals). I based action items on answers given. If you can't embrace a loved one, use obimy.

We also tried Facebook and Google ads. From the start, we knew it wouldn't work.

We were desperate to discover a free way to get more users.

Our journey to TikTok

TikTok is a great venue for emerging creators. It also helped reach people. Before obimy, my TikTok videos garnered 12 million views without sponsored promotion.

We had to act. TikTok was required.

I wasn't a TikTok user before obimy. Initially, I uploaded promotional content. Call-to-actions appear strange next to dancing challenges and my money don't jiggle jiggle. I learned TikTok. Watch TikTok for an hour was on my to-do list. What a dream job!

Our most popular movies presented the app alongside text outlining what it does. We started promoting them in Europe and the U.S. and got a 16% CTR and $1 CPI, an improvement over our previous efforts.

Somehow, we were expanding. So we came up with new hypotheses, calls to action, and content.

Four months passed, yet we saw no organic growth.

Russia attacked Ukraine.

Our app aimed to be helpful. For now, we're focusing on our Ukrainian audience. I posted sloppy TikToks illustrating how obimy can help during shelling or air raids.

In two hours, Kostia sent me our visitor count. Our servers crashed.

Initially, we had several thousand daily users. Over 200,000 users joined obimy in a week. They posted obimy videos on TikTok, drawing additional users. We've also resumed U.S. video promotion.

We gained 2,000,000 new members with less than $100 in ads, primarily in the U.S. and U.K.

TikTok helped.

The figures

We were confident we'd chosen the ideal tool for organic growth.

• Over 45 million people have viewed our own videos plus a ton of user-generated content with the hashtag #obimy.

• About 375 thousand people have liked all of our individual videos.

• The number of downloads and the virality of videos are directly correlated.

Where are we now?

TikTok fuels our organic growth. We post 56 videos every week and pay to promote viral content.

We use UGC and influencers. We worked with Universal Music Italy on Eurovision. They offered to promote us through their million-follower TikTok influencers. We thought their followers would improve our audience, but it didn't matter. Integration didn't help us. Users that share obimy videos with their followers can reach several million views, which affects our download rate.

After the dust settled, we determined our key audience was 13-18-year-olds. They want to express themselves, but it's sometimes difficult. We're searching for methods to better engage with our users. We opened a Discord server to discuss anime and video games and gather app and content feedback.

TikTok helps us test product updates and hypotheses. Example: I once thought we might raise MAU by prompting users to add strangers as friends. Instead of asking our team to construct it, I made a TikTok urging users to share invite URLs. Users share links under every video we upload, embracing people worldwide.

Key lessons

Don't direct-sell. TikTok isn't for Instagram, Facebook, or YouTube promo videos. Conventional advertisements don't fit. Most users will swipe up and watch humorous doggos.

More product videos are better. Finally. So what?

Encourage interaction. Tagging friends in comments or making videos with the app promotes it more than any marketing spend.

Be odd and risqué. A user mistakenly sent a French kiss to their mom in one of our most popular videos.

TikTok helps test hypotheses and build your user base. It also helps develop apps. In our upcoming blog, we'll guide you through obimy's design revisions based on TikTok. Follow us on Twitter, Instagram, and TikTok.

I was entering tens of millions of records per hour when I first published Slonik PostgreSQL client for Node.js. The data being entered was usually flat, making it straightforward to use INSERT INTO ... SELECT * FROM unnset() pattern. I advocated the unnest approach for inserting rows in groups (that was part I).

However, today I’ve found a better way: jsonb_to_recordset.

jsonb_to_recordset expands the top-level JSON array of objects to a set of rows having the composite type defined by an AS clause.

jsonb_to_recordset allows us to query and insert records from arbitrary JSON, like unnest. Since we're giving JSON to PostgreSQL instead of unnest, the final format is more expressive and powerful.

SELECT *
FROM json_to_recordset('[{"name":"John","tags":["foo","bar"]},{"name":"Jane","tags":["baz"]}]')
AS t1(name text, tags text[]);
 name |   tags
------+-----------
 John | {foo,bar}
 Jane | {baz}
(2 rows)

Let’s demonstrate how you would use it to insert data.

Inserting data using json_to_recordset

Say you need to insert a list of people with attributes into the database.

const persons = [
  {
    name: 'John',
    tags: ['foo', 'bar']
  },
  {
    name: 'Jane',
    tags: ['baz']
  }
];

You may be tempted to traverse through the array and insert each record separately, e.g.

for (const person of persons) {
  await pool.query(sql`
    INSERT INTO person (name, tags)
    VALUES (
      ${person.name},
      ${sql.array(person.tags, 'text[]')}
    )
  `);
}

It's easier to read and grasp when working with a few records. If you're like me and troubleshoot a 2M+ insert query per day, batching inserts may be beneficial.

What prompted the search for better alternatives.

Inserting using unnest pattern might look like this:

await pool.query(sql`
  INSERT INTO public.person (name, tags)
  SELECT t1.name, t1.tags::text[]
  FROM unnest(
    ${sql.array(['John', 'Jane'], 'text')},
    ${sql.array(['{foo,bar}', '{baz}'], 'text')}
  ) AS t1.(name, tags);
`);

You must convert arrays into PostgreSQL array strings and provide them as text arguments, which is unsightly. Iterating the array to create slices for each column is likewise unattractive.

However, with jsonb_to_recordset, we can:

await pool.query(sql`
  INSERT INTO person (name, tags)
  SELECT *
  FROM jsonb_to_recordset(${sql.jsonb(persons)}) AS t(name text, tags text[])
`);

In contrast to the unnest approach, using jsonb_to_recordset we can easily insert complex nested data structures, and we can pass the original JSON document to the query without needing to manipulate it.

In terms of performance they are also exactly the same. As such, my current recommendation is to prefer jsonb_to_recordset whenever inserting lots of rows or nested data structures.

The Industrial Revolution's Curse: Why One Age's Power Prevents the Next Ones

A surprising fact. Recently, Big Oil's 1970s climate change projections were disturbingly accurate. Of course, we now know that it worked tirelessly to deny climate change, polluting our societies to this day. That's a small example of the Industrial Revolution's curse.

Let me rephrase this nuanced and possibly weird thought. The chart above? Disruptive science is declining. The kind that produces major discoveries, new paradigms, and shattering prejudices.

Not alone. Our civilisation reached a turning point suddenly. Progress stopped and reversed for the first time in centuries.

The Industrial Revolution's Big Bang started it all. At least some humans had riches for the first time, if not all, and with that wealth came many things. Longer, healthier lives since now health may be publicly and privately invested in. For the first time in history, wealthy civilizations could invest their gains in pure research, a good that would have sounded frivolous to cultures struggling to squeeze out the next crop, which required every shoulder to the till.

So. Don't confuse me with the Industrial Revolution's curse. Industry progressed. Contrary. I'm claiming that the Big Bang of Progress is slowing, plateauing, and ultimately reversing. All social indicators show that. From progress itself to disruptive, breakthrough research, everything is slowing down.

It's troubling. Because progress slows and plateaus, pre-modern social problems like fascism, extremism, and fundamentalism return. People crave nostalgic utopias when they lose faith in modernity. That strongman may shield me from this hazardous life. If I accept my place in a blood-and-soil hierarchy, I have a stable, secure position and someone to punch and detest. It's no coincidence that as our civilization hits a plateau of progress, there is a tsunami pulling the world backwards, with people viscerally, openly longing for everything from theocracy to fascism to fundamentalism, an authoritarian strongman to soothe their fears and tell them what to do, whether in Britain, heartland America, India, China, and beyond.

However, one aspect remains unknown. Technology. Let me clarify.

How do most people picture tech? Say that without thinking. Most people think of social media or AI. Well, small correlation engines called artificial neurons are a far cry from biological intelligence, which functions in far more obscure and intricate ways, down to the subatomic level. But let's try it.

Today, tech means AI. But. Do you foresee it?

Consider why civilisation is plateauing and regressing. Because we can no longer provide the most basic necessities at the same rate. On our track, clean air, water, food, energy, medicine, and healthcare will become inaccessible to huge numbers within a decade or three. Not enough. There isn't, therefore prices for food, medicine, and energy keep rising, with occasional relief.

Why our civilizations are encountering what economists like me term a budget constraint—a hard wall of what we can supply—should be evident. Global warming and extinction. Megafires, megadroughts, megafloods, and failed crops. On a civilizational scale, good luck supplying the fundamentals that way. Industrial food production cannot feed a planet warming past two degrees. Crop failures, droughts, floods. Another example: glaciers melt, rivers dry up, and the planet's fresh water supply contracts like a heart attack.

Now. Let's talk tech again. Mostly AI, maybe phone apps. The unsettling reality is that current technology cannot save humanity. Not much.

AI can do things that have become cliches to titillate the masses. It may talk to you and act like a person. It can generate art, which means reproduce it, but nonetheless, AI art! Despite doubts, it promises to self-drive cars. Unimportant.

We need different technology now. AI won't grow crops in ash-covered fields, cleanse water, halt glaciers from melting, or stop the clear-cutting of the planet's few remaining forests. It's not useless, but on a civilizational scale, it's much less beneficial than its proponents claim. By the time it matures, AI can help deliver therapy, keep old people company, and even drive cars more efficiently. None of it can save our culture.

Expand that scenario. AI's most likely use? Replacing call-center workers. Support. It may help doctors diagnose, surgeons orient, or engineers create more fuel-efficient motors. This is civilizationally marginal.

Non-disruptive. Do you see the connection with the paper that indicated disruptive science is declining? AI exemplifies that. It's called disruptive, yet it's a textbook incremental technology. Oh, cool, I can communicate with a bot instead of a poor human in an underdeveloped country and have the same or more trouble being understood. This bot is making more people unemployed. I can now view a million AI artworks.

AI illustrates our civilization's trap. Its innovative technologies will change our lives. But as you can see, its incremental, delivering small benefits at most, and certainly not enough to balance, let alone solve, the broader problem of steadily dropping living standards as our society meets a wall of being able to feed itself with fundamentals.

Contrast AI with disruptive innovations we need. What do we need to avoid a post-Roman Dark Age and preserve our civilization in the coming decades? We must be able to post-industrially produce all our basic needs. We need post-industrial solutions for clean water, electricity, cement, glass, steel, manufacture for garments and shoes, starting with the fossil fuel-intensive plastic, cotton, and nylon they're made of, and even food.

Consider. We have no post-industrial food system. What happens when crop failures—already dangerously accelerating—reach a critical point? Our civilization is vulnerable. Think of ancient civilizations that couldn't survive the drying up of their water sources, the failure of their primary fields, which they assumed the gods would preserve forever, or an earthquake or sickness that killed most of their animals. Bang. Lost. They failed. They splintered, fragmented, and abandoned vast capitols and cities, and suddenly, in history's sight, poof, they were gone.

We're getting close. Decline equals civilizational peril.

We believe dumb notions about AI becoming disruptive when it's incremental. Most of us don't realize our civilization's risk because we believe these falsehoods. Everyone should know that we cannot create any thing at civilizational scale without fossil fuels. Most of us don't know it, thus we don't realize that the breakthrough technologies and systems we need don't manipulate information anymore. Instead, biotechnologies, largely but not genes, generate food without fossil fuels.

We need another Industrial Revolution. AI, apps, bots, and whatnot won't matter unless you think you can eat and drink them while the world dies and fascists, lunatics, and zealots take democracy's strongholds. That's dramatic, but only because it's already happening. Maybe AI can entertain you in that bunker while society collapses with smart jokes or a million Mondrian-like artworks. If civilization is to survive, it cannot create the new Industrial Revolution.

The revolution has begun, but only in small ways. Post-industrial fundamental systems leaders are developing worldwide. The Netherlands is leading post-industrial agriculture. That's amazing because it's a tiny country performing well. Correct? Discover how large-scale agriculture can function, not just you and me, aged hippies, cultivating lettuce in our backyards.

Iceland is leading bioplastics, which, if done well, will be a major advance. Of sure, microplastics are drowning the oceans. What should we do since we can't live without it? We need algae-based bioplastics for green plastic.

That's still young. Any of the above may not function on a civilizational scale. Bioplastics use algae, which can cause problems if overused. None of the aforementioned indicate the next Industrial Revolution is here. Contrary. Slowly.

We have three decades until everything fails. Before life ends. Curtain down. No more fields, rivers, or weather. Freshwater and life stocks have plummeted. Again, we've peaked and declined in our ability to live at today's relatively rich standards. Game over—no more. On a dying planet, producing the fundamentals for a civilisation that left it too late to construct post-industrial systems becomes next to impossible, with output dropping faster and quicker each year, quarter, and day.

Too slow. That's because it's not really happening. Most people think AI when I say tech. I get a politicized response if I say Green New Deal or Clean Industrial Revolution. Half the individuals I talk to have been politicized into believing that climate change isn't real and that any breakthrough technical progress isn't required, desirable, possible, or genuine. They'll suffer.

The Industrial Revolution curse. Every revolution creates new authorities, which ossify and refuse to relinquish their privileges. For fifty years, Big Oil has denied climate change, even though their scientists predicted it. We also have a software industry and its venture capital power centers that are happy for the average person to think tech means chatbots, not being able to produce basics for a civilization without destroying the planet, and billionaires who buy comms platforms for the same eye-watering amount of money it would take to save life on Earth.

The entire world's vested interests are against the next industrial revolution, which is understandable since they were established from fossil money. From finance to energy to corporate profits to entertainment, power in our world is the result of the last industrial revolution, which means it has no motivation or purpose to give up fossil money, as we are witnessing more brutally out in the open.

Thus, the Industrial Revolution's curse—fossil power—rules our globe. Big Agriculture, Big Pharma, Wall St., Silicon Valley, and many others—including politics, which they buy and sell—are basically fossil power, and they have no interest in generating or letting the next industrial revolution happen. That's why tiny enterprises like those creating bioplastics in Iceland or nations savvy enough to shun fossil power, like the Netherlands, which has a precarious relationship with nature, do it. However, fossil power dominates politics, economics, food, clothes, energy, and medicine, and it has no motivation to change.

Allow disruptive innovations again. As they occur, its position becomes increasingly vulnerable. If you were fossil power, would you allow another industrial revolution to destroy its privilege and wealth?

You might, since power and money haven't corrupted you. However, fossil power prevents us from building, creating, and growing what we need to survive as a society. I mean the entire economic, financial, and political power structure from the last industrial revolution, not simply Big Oil. My friends, fossil power's chokehold over our society is likely to continue suffocating the advances that could have spared our civilization from a decline that's now here and spiraling closer to oblivion.

Sundar Pichai unveiled Simplicity Sprint at Google's latest all-hands conference.

To boost employee efficiency.

Not surprising. Few envisioned Google declaring a productivity drive.

Sunder Pichai's speech:

“There are real concerns that our productivity as a whole is not where it needs to be for the head count we have. Help me create a culture that is more mission-focused, more focused on our products, more customer focused. We should think about how we can minimize distractions and really raise the bar on both product excellence and productivity.”

The primary driver driving Google's efficiency push is:

Google's efficiency push follows 13% quarterly revenue increase. Last year in the same quarter, it was 62%.

Market newcomers may argue that the previous year's figure was fuelled by post-Covid reopening and growing consumer spending. Investors aren't convinced. A promising company like Google can't afford to drop so quickly.

Google’s quarterly revenue growth stood at 13%, against 62% in last year same quarter.

Google isn't alone. In my recent essay regarding 2025 programmers, I warned about the economic downturn's effects on FAAMG's workforce. Facebook had suspended hiring, and Microsoft had promised hefty bonuses for loyal staff.

In the same article, I predicted Google's troubles. Online advertising, especially the way Google and Facebook sell it using user data, is over.

FAAMG and 2nd rung IT companies could be the first to fall without Post-COVID revival and uncertain global geopolitics.

Google has hardly ever discussed effectiveness:

Apparently openly.

Amazon treats its employees like robots, even in software positions. It has significant turnover and a terrible reputation as a result. Because of this, it rarely loses money due to staff productivity.

Amazon trumps Google. In reality, it treats its employees poorly.

Google was the founding father of the modern-day open culture.

Larry and Sergey Google founded the IT industry's Open Culture. Silicon Valley called Google's internal democracy and transparency near anarchy. Management rarely slammed decisions on employees. Surveys and internal polls ensured everyone knew the company's direction and had a vote.

20% project allotment (weekly free time to build own project) was Google's open-secret innovation component.

After Larry and Sergey's exit in 2019, this is Google's first profitability hurdle. Only Google insiders can answer these questions.

• Would Google's investors compel the company's management to adopt an Amazon-style culture where the developers are treated like circus performers?

• If so, would Google follow suit?

• If so, how does Google go about doing it?

Before discussing Google's likely plan, let's examine programming productivity.

What determines a programmer's productivity is simple:

How would we answer Google's questions?

As a programmer, I'm more concerned about Simplicity Sprint's aftermath than its economic catalysts.

Large organizations don't care much about quarterly and annual productivity metrics. They have 10-year product-launch plans. If something seems horrible today, it's likely due to someone's lousy judgment 5 years ago who is no longer in the blame game.

Deconstruct our main question.

• How exactly do you change the culture of the firm so that productivity increases?

• How can you accomplish that without affecting your capacity to profit? There are countless ways to increase output without decreasing profit.

• How can you accomplish this with little to no effect on employee motivation? (While not all employers care about it, in this case we are discussing the father of the open company culture.)

• How do you do it for a 10-developer IT firm that is losing money versus a 1,70,000-developer organization with a trillion-dollar valuation?

When implementing a large-scale organizational change, success must be carefully measured.

The fastest way to do something is to do it right, no matter how long it takes.

You require clearly-defined group/team/role segregation and solid pass/fail matrices to:

• You can give performers rewards.

• Ones that are average can be inspired to improve

• Underachievers may receive assistance or, in the worst-case scenario, rehabilitation

As a 20-year programmer, I associate productivity with greatness.

Doing something well, no matter how long it takes, is the fastest way to do it.

Let's discuss a programmer's productivity.

Why productivity is a strange term in programming:

Productivity is work per unit of time.

Money=time This is an economic proverb. More hours worked, more pay. Longer projects cost more.

As a buyer, you desire a quick supply. As a business owner, you want employees who perform at full capacity, creating more products to transport and boosting your profits.

All economic matrices encourage production because of our obsession with it. Productivity is the only organic way a nation may increase its GDP.

Time is money — is not just a proverb, but an economical fact.

Applying the same productivity theory to programming gets problematic. An automating computer. Its capacity depends on the software its master writes.

Today, a sophisticated program can process a billion records in a few hours. Creating one takes a competent coder and the necessary infrastructure. Learning, designing, coding, testing, and iterations take time.

Programming productivity isn't linear, unlike manufacturing and maintenance.

Average programmers produce code every day yet miss deadlines. Expert programmers go days without coding. End of sprint, they often surprise themselves by delivering fully working solutions.

Reversing the programming duties has no effect. Experts aren't needed for productivity.

These patterns remind me of an XKCD comic.

Programming productivity depends on two factors:

• The capacity of the programmer and his or her command of the principles of computer science

• His or her productive bursts, how often they occur, and how long they last as they engineer the answer

At some point, productivity measurement becomes Schrödinger’s cat.

Product companies measure productivity using use cases, classes, functions, or LOCs (lines of code). In days of data-rich source control systems, programmers' merge requests and/or commits are the most preferred yardstick. Companies assess productivity by tickets closed.

Every organization eventually has trouble measuring productivity. Finer measurements create more chaos. Every measure compares apples to oranges (or worse, apples with aircraft.) On top of the measuring overhead, the endeavor causes tremendous and unnecessary stress on teams, lowering their productivity and defeating its purpose.

Macro productivity measurements make sense. Amazon's factory-era management has done it, but at great cost.

Google can pull it off if it wants to.

What Google meant in reality when it said that employee productivity has decreased:

When Google considers its employees unproductive, it doesn't mean they don't complete enough work in the allotted period.

They can't multiply their work's influence over time.

• Programmers who produce excellent modules or products are unsure on how to use them.

• The best data scientists are unable to add the proper parameters in their models.

• Despite having a great product backlog, managers struggle to recruit resources with the necessary skills.

• Product designers who frequently develop and A/B test newer designs are unaware of why measures are inaccurate or whether they have already reached the saturation point.

• Most ignorant: All of the aforementioned positions are aware of what to do with their deliverables, but neither their supervisors nor Google itself have given them sufficient authority.

So, Google employees aren't productive.

How to fix it?

• Business analysis: White suits introducing novel items can interact with customers from all regions. Track analytics events proactively, especially the infrequent ones.

• SOLID, DRY, TEST, and AUTOMATION: Do less + reuse. Use boilerplate code creation. If something already exists, don't implement it yourself.

• Build features-building capabilities: N features are created by average programmers in N hours. An endless number of features can be built by average programmers thanks to the fact that expert programmers can produce 1 capability in N hours.

• Work on projects that will have a positive impact: Use the same algorithm to search for images on YouTube rather than the Mars surface.

• Avoid tasks that can only be measured in terms of time linearity at all costs (if a task can be completed in N minutes, then M copies of the same task would cost M*N minutes).

In conclusion:

Software development isn't linear. Why should the makers be measured?

Notation for The Big O

I'm discussing a new way to quantify programmer productivity. (It applies to other professions, but that's another subject)

The Big O notation expresses the paradigm (the algorithmic performance concept programmers rot to ace their Google interview)

Google (or any large corporation) can do this.

1. Sort organizational roles into categories and specify their impact vs. time objectives. A CXO role's time vs. effect function, for instance, has a complexity of O(log N), meaning that if a CEO raises his or her work time by 8x, the result only increases by 3x.

2. Plot the influence of each employee over time using the X and Y axes, respectively.

3. Add a multiplier for Y-axis values to the productivity equation to make business objectives matter. (Example values: Support = 5, Utility = 7, and Innovation = 10).

4. Compare employee scores in comparable categories (developers vs. devs, CXOs vs. CXOs, etc.) and reward or help employees based on whether they are ahead of or behind the pack.

After measuring every employee's inventiveness, it's straightforward to help underachievers and praise achievers.

Example of a Big(O) Category:

If I ran Google (God forbid, its worst days are far off), here's how I'd classify it. You can categorize Google employees whichever you choose.

The Google interview truth:

O(1) < O(log n) < O(n) < O(n log n) < O(n^x) where all logarithmic bases are < n.

O(1): Customer service workers' hours have no impact on firm profitability or customer pleasure.

CXOs Most of their time is spent on travel, strategic meetings, parties, and/or meetings with minimal floor-level influence. They're good at launching new products but bad at pivoting without disaster. Their directions are being followed.

Devops, UX designers, testers Agile projects revolve around deployment. DevOps controls the levers. Their automation secures results in subsequent cycles.

UX/UI Designers must still prototype UI elements despite improved design tools.

All test cases are proportional to use cases/functional units, hence testers' work is O(N).

Architects Their effort improves code quality. Their right/wrong interference affects product quality and rollout decisions even after the design is set.

Core Developers Only core developers can write code and own requirements. When people understand and own their labor, the output improves dramatically. A single character error can spread undetected throughout the SDLC and cost millions.

Core devs introduce/eliminate 1000x bugs, refactoring attempts, and regression. Following our earlier hypothesis.

The fastest way to do something is to do it right, no matter how long it takes.

Conclusion:

Google is at the liberal extreme of the employee-handling spectrum

Microsoft faced an existential crisis after 2000. It didn't choose Amazon's data-driven people management to revitalize itself.

Instead, it entrusted developers. It welcomed emerging technologies and opened up to open source, something it previously opposed.

Google is too lax in its employee-handling practices. With that foundation, it can only follow Amazon, no matter how carefully.

Any attempt to redefine people's measurements will affect the organization emotionally.

The more Google compares apples to apples, the higher its chances for future rebirth.