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.

(And you can have it for free)

Master email to sell anything.

Most novice creators don't know how to begin.

Many use online templates. These are usually fluff-filled and niche-specific.

They're robotic and "salesy."

I've attended 3 courses, read 10 books, and sent 600,000 emails in the past five years.

Outcome?

This *proven* email sequence assures me a month's salary every time I send it.

What you will discover in this article is that:

• A full 6-part email sales cycle

• The essential elements you must incorporate

• placeholders and text-filled images

• (Applies to any niche)

This can be a product introduction, holiday, or welcome sequence. This works for email-saleable products.

Let's start

Email 1: Describe your issue

This email is crucial.

How to? We introduce a subscriber or prospect's problem. Later, we'll frame our offer as the solution.

Label the:

• Problem

• Why it still hasn't been fixed

• Resulting implications for the customer

This puts our new subscriber in solve mode and queues our offer:

Email 2: Amplify the consequences

We're still causing problems.

We've created the problem, but now we must employ emotion and storytelling to make it real. We also want to forecast life if nothing changes.

Let's feel:

• What occurs if it is not resolved?

• Why is it crucial to fix it immediately?

• Tell a tale of a person who was in their position. To emphasize the effects, use a true account of another person (or of yourself):

Email 3: Share a transformation story

Selling stories.

Whether in an email, landing page, article, or video. Humanize stories. They give information meaning.

This is where "issue" becomes "solution."

Let's reveal:

• A tale of success

• A new existence and result

• tools and tactics employed

Start by transforming yourself.

Email 4: Prove with testimonials

No one buys what you say.

Emotionally stirred people buy and act. They believe in the product. They feel that if they buy, it will work.

Social proof shows prospects that your solution will help them.

Add:

• Earlier and Later

• Testimonials

• Reviews

Proof this deal works:

Email 5: Reveal your offer

It's showtime.

This is it. Until now, describing the offer and offering links to a landing page have been sparse in the email pictures.

We've been tense. Gaining steam. Building suspense. Email 5 reveals all.

In this email:

• a description of the deal

• A word about a promise

• recapitulation of the transformation

• and make a reference to the urgency Everything should be spelled out clearly:

Email no. 6: Instill urgency

When there are stakes, humans act.

Creating and marketing with haste raises the stakes. Urgency makes a prospect act because they'll miss out or gain immensely.

Urgency converts. Use:

• short time

• Screening

• Scarcity

Urgency and conversions. Limited-time offers are easy.

TL;DR

Use this proven 6-part email sequence (that turns subscribers into profit):

• Introduce a problem

• Amplify it with emotions

• Share transformation story

• Prove it works with testimonials

• Value-stack and present your offer

• Drive urgency and entice the purchase

Decentralizing Web3's soul

Our tech must reflect our non-transactional connections. Web3 arose from a lack of social links. It must strengthen these linkages to get widespread adoption. Soulbound NFTs help.

This NFT creates digital proofs of our social ties. It embodies G. Simmel's idea of identity, in which individuality emerges from social groups, just as social groups evolve from people.

It's multipurpose. First, gather online our distinctive social features. Second, highlight and categorize social relationships between entities and people to create a spiderweb of networks.

1. 🌐 Reducing online manipulation: Only socially rich or respectable crypto wallets can participate in projects, ensuring that no one can create several wallets to influence decentralized project governance.

2. 🤝 Improving social links: Some sectors of society lack social context. Racism, sexism, and homophobia do that. Public wallets can help identify and connect distinct social groupings.

3. 👩‍❤️‍💋‍👨 Increasing pluralism: Soulbound tokens can ensure that socially connected wallets have less voting power online to increase pluralism. We can also overweight a minority of numerous voices.

4. 💰Making more informed decisions: Taking out an insurance policy requires a life review. Why not loans? Character isn't limited by income, and many people need a chance.

5. 🎶 Finding a community: Soulbound tokens are accessible to everyone. This means we can find people who are like us but also different. This is probably rare among your friends and family.

NFTs are dangerous, and I don't like them. Social credit score, privacy, lost wallet. We must stay informed and keep talking to innovators.

E. Glen Weyl, Puja Ohlhaver and Vitalik Buterin get all the credit for these ideas, having written the very accessible white paper “Decentralized Society: Finding Web3’s Soul”.

You’ll be surprised; I sure was

Blogging and writing online as a side income has now been around for a significant amount of time. Nowadays, it is a continuously rising moneymaker for prospective writers, with several writing platforms existing online. At the top of the list are Medium, Vocal Media, Newsbreak, and the biggest one of them, Quora, with 300 million active users.

Quora, unlike Medium, is a question-and-answer format platform. On Medium you are permitted to write what you want, while on Quora, you answer questions on topics that you have expertise about. Quora, like Medium, now compensates its authors for the answers they provide in comparison to the previous, in which you had to be admitted to the partner program and were paid to ask questions.

Quora just recently went live with this new partner program, Quora Plus, and the way it works is that it is a subscription for $5 a month which provides you access to metered/monetized stories, in turn compensating the writers for part of that subscription for their answers.

I too on Quora have found a lot of success on the platform, gaining 23 Million Content Views, and 300,000 followers for my space, which is kind of the Quora equivalent of a Medium article. The way in which I was able to do this was entirely thanks to a hack that I uncovered to the Quora algorithm.

In this article, I plan on discussing how much money I received from 23 million content views on Quora, and I bet you’ll be shocked; I know I was.

A Brief Explanation of How I Got 23 Million Views and How You Can Do It Too

On Quora, everything in terms of obtaining views is about finding the proper question, which I only understood quite late into the game. I published my first response in 2019 but never actually wrote on Quora until the summer of 2020, and about a month into posting consistently I found out how to find the perfect question. Here’s how:

The Process

Go to your Home Page and start scrolling… While browsing, check for the following things…

1. Answers from people you follow or your followers.

2. Advertisements

These two things are the two things you want to ignore, you don’t want to answer those questions or look at the ads. You should now be left with a couple of recommended answers. To discover which recommended answer is the best to answer as well, look at these three important aspects.

1. Date of the answer: Was it in the past few days, preferably 2–3 days, even better, past 24 hours?

2. Views: Are they in the ten thousands or hundred thousands?

3. Upvotes: Are they in the hundreds or thousands?

Now, choose an answer to a question which you think you could answer as well that satisfies the requirements above. Once you click on it, as all answers on Quora works, it will redirect you to the page for that question, in which you will have to select once again if you should answer the question.

1. Amount of answers: How many responses are there to the given question? This tells you how much competition you have. My rule is beyond 25 answers, you shouldn’t answer, but you can change it anyway you’d like.

2. Answerers: Who did the answering for the question? If the question includes a bunch of renowned, extremely well-known people on Quora, there’s a good possibility your essay is going to get drowned out.

3. Views: Check for a constant quantity of high views on each answer for the question; this is what will guarantee that your answer gets a lot of views!

The Income Reveal! How Much I Made From 23 Million Content Views

DRUM ROLL, PLEASE!

8.97 USD. Yes, not even ten dollars, not even nine. Just eight dollars and ninety-seven cents.

Possible Reasons for My Low Earnings

• Quora Plus and the answering partner program are newer than my Quora views.

• Few people use Quora+, therefore revenues are low.

• I haven't been writing much on Quora, so I'm only making money from old answers and a handful since Quora Plus launched.

• Quora + pays poorly...

Should You Try Quora and Quora For Money?

My answer depends on your needs. I never got invited to Quora's question partner program due to my late start, but other writers have made hundreds. Due to Quora's new and competitive answering partner program, you may not make much money.

If you want a fun writing community, try Quora. Quora was fun when I only made money from my space. Quora +'s paywalls and new contributors eager to make money have made the platform less fun for me.

This article is a summary to save you time. You can read my full, more detailed article, here.

For some people, the glass is half empty. For others, it’s half full. And for some, the question is, How do I get this glass totally full again?

Problem-solvers are the last group. They're neutral. Pragmatists.

Problems surround them. They fix things instead of judging them. Problem-solvers improve the world wherever they go.

Some fail. Sometimes their good intentions have terrible results. Like when they try to help a grandma cross the road because she can't do it alone but discover she never wanted to.

Most programmers, software engineers, and data scientists solve problems. They use computer code to fix problems they see.

Coding is best done by understanding and solving the problem.

Despite your best intentions, building the wrong solution may have negative consequences. Helping an unwilling grandma cross the road.

How can you improve problem-solving?

1. Examine your presumptions.

Don’t think There’s a grandma, and she’s unable to cross the road. Therefore I must help her over the road. Instead think This grandma looks unable to cross the road. Let’s ask her whether she needs my help to cross it.

Maybe the grandma can’t cross the road alone, but maybe she can. You can’t tell for sure just by looking at her. It’s better to ask.

Maybe the grandma wants to cross the road. But maybe she doesn’t. It’s better to ask!

Building software is similar. Do only I find this website ugly? Who can I consult?

We all have biases, mental shortcuts, and worldviews. They simplify life.

Problem-solving requires questioning all assumptions. They might be wrong!

Think less. Ask more.

Secondly, fully comprehend the issue.

Grandma wants to cross the road? Does she want flowers from the shop across the street?

Understanding the problem advances us two steps. Instead of just watching people and their challenges, try to read their intentions.

Don't ask, How can I help grandma cross the road? Why would this grandma cross the road? What's her goal?

Understand what people want before proposing solutions.

3. Request more information. This is not a scam!

People think great problem solvers solve problems immediately. False!

Problem-solvers study problems. Understanding the problem makes solving it easy.

When you see a grandma struggling to cross the road, you want to grab her elbow and pull her over. However, a good problem solver would ask grandma what she wants. So:

Problem solver: Excuse me, ma’am? Do you wish to get over the road? Grandma: Yes indeed, young man! Thanks for asking. Problem solver: What do you want to do on the other side? Grandma: I want to buy a bouquet of flowers for my dear husband. He loves flowers! I wish the shop wasn’t across this busy road… Problem solver: Which flowers does your husband like best? Grandma: He loves red dahlia. I usually buy about 20 of them. They look so pretty in his vase at the window! Problem solver: I can get those dahlia for you quickly. Go sit on the bench over here while you’re waiting; I’ll be back in five minutes. Grandma: You would do that for me? What a generous young man you are!

A mediocre problem solver would have helped the grandma cross the road, but he might have forgotten that she needs to cross again. She must watch out for cars and protect her flowers on the way back.

A good problem solver realizes that grandma's husband wants 20 red dahlias and completes the task.

4- Rapid and intense brainstorming

Understanding a problem makes solutions easy. However, you may not have all the information needed to solve the problem.

Additionally, retrieving crucial information can be difficult.

You could start a blog. You don't know your readers' interests. You can't ask readers because you don't know who they are.

Brainstorming works here. Set a stopwatch (most smartphones have one) to ring after five minutes. In the remaining time, write down as many topics as possible.

No answer is wrong. Note everything.

Sort these topics later. Programming or data science? What might readers scroll past—are these your socks this morning?

Rank your ideas intuitively and logically. Write Medium stories using the top 35 ideas.

5 - Google it.

Doctor Google may answer this seemingly insignificant question. If you understand your problem, try googling or binging.

Someone has probably had your problem before. The problem-solver may have posted their solution online.

Use others' experiences. If you're social, ask a friend or coworker for help.

6 - Consider it later

Rest your brain.

Reread. Your brain needs rest to function.

Hustle culture encourages working 24/7. It doesn't take a neuroscientist to see that this is mental torture.

Leave an unsolvable problem. Visit friends, take a hot shower, or do whatever you enjoy outside of problem-solving.

Nap.

I get my best ideas in the morning after working on a problem. I couldn't have had these ideas last night.

Sleeping subconsciously. Leave it alone and you may be surprised by the genius it produces.

7 - Learn to live with frustration

There are problems that you’ll never solve.

Mathematicians are world-class problem-solvers. The brightest minds in history have failed to solve many mathematical problems.

A Gordian knot problem can frustrate you. You're smart!

Frustration-haters don't solve problems well. They choose simple problems to avoid frustration.

No. Great problem solvers want to solve a problem but know when to give up.

Frustration initially hurts. You adapt.

Famous last words

If you read this article, you probably solve problems. We've covered many ways to improve, so here's a summary:

1. Test your presumptions. Is the issue the same for everyone else when you see one? Or are your prejudices and self-judgments misguiding you?

2. Recognize the issue completely. On the surface, a problem may seem straightforward, but what's really going on? Try to see what the current situation might be building up to by thinking two steps ahead of the current situation.

3. Request more information. You are no longer a high school student. A two-sentence problem statement is not sufficient to provide a solution. Ask away if you need more details!

4. Think quickly and thoroughly. In a constrained amount of time, try to write down all your thoughts. All concepts are worthwhile! Later, you can order them.

5. Google it. There is a purpose for the internet. Use it.

6. Consider it later at night. A rested mind is more creative. It might seem counterintuitive to leave a problem unresolved. But while you're sleeping, your subconscious will handle the laborious tasks.

7. Accept annoyance as a normal part of life. Don't give up if you're feeling frustrated. It's a step in the procedure. It's also perfectly acceptable to give up on a problem because there are other, more pressing issues that need to be addressed.

You might feel stupid sometimes, but that just shows that you’re human. You care about the world and you want to make it better.

At the end of the day, that’s all there is to problem solving — making the world a little bit better.