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.

Adopt Elon Musk's learning strategy to succeed.

Medium writers rank first and second when you Google “Elon Musk's learning approach”.

My article idea seems unoriginal. Lol

Musk is brilliant.

No doubt here.

His name connotes success and intelligence.

He knows rocket science, engineering, AI, and solar power.

Musk is a Unicorn, but his skills aren't special.

How does he manage it?

Elon Musk has two learning rules that anyone may use.

You can apply these rules and become anyone you want.

You can become a rocket scientist or a surgeon. If you want, of course.

The learning process is key.

Make sure you are creating a Tree of Knowledge according to Rule #1.

Musk told Reddit how he learns:

“It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang onto.”

Musk understands the essential ideas and mental models of each of his business sectors.

He starts with the tree's trunk, making sure he learns the basics before going on to branches and leaves.

We often act otherwise. We memorize small details without understanding how they relate to the whole. Our minds are stuffed with useless data.

Cramming isn't learning.

Start with the basics to learn faster. Before diving into minutiae, grasp the big picture.

Rule #2: You can't connect what you can't remember.

Elon Musk transformed industries this way. As his expertise grew, he connected branches and leaves from different trees.

Musk read two books a day as a child. He didn't specialize like most people. He gained from his multidisciplinary education. It helped him stand out and develop billion-dollar firms.

He gained skills in several domains and began connecting them. World-class performances resulted.

Most of us never learn the basics and only collect knowledge. We never really comprehend information, thus it's hard to apply it.

Learn the basics initially to maximize your chances of success. Then start learning.

Learn across fields and connect them.

This method enabled Elon Musk to enter and revolutionize a century-old industry.

While our genes can't be changed easily, you can avoid some dementia risk factors. Today we discuss dementia and five drugs that may increase risk.

Memory loss appears to come with age, but we're not talking about forgetfulness. Sometimes losing your car keys isn't an indication of dementia. Dementia impairs the capacity to think, remember, or make judgments. Dementia hinders daily tasks.

Alzheimers is the most common dementia. Dementia is not normal aging, unlike forgetfulness. Aging increases the risk of Alzheimer's and other dementias. A family history of the illness increases your risk, according to the Mayo Clinic (USA).

Given that our genes are difficult to change (I won't get into epigenetics), what are some avoidable dementia risk factors? Certain drugs may cause cognitive deterioration.

Today we look at four drugs that may cause cognitive decline.

Dementia and benzodiazepines

Benzodiazepine sedatives increase brain GABA levels. Example benzodiazepines:

• Diazepam (Valium) (Valium)

• Alprazolam (Xanax) (Xanax)

• Clonazepam (Klonopin) (Klonopin)

Addiction and overdose are benzodiazepine risks. Yes! These medications don't raise dementia risk.

USC study: Benzodiazepines don't increase dementia risk in older adults.

Benzodiazepines can produce short- and long-term amnesia. This memory loss hinders memory formation. Extreme cases can permanently impair learning and memory. Anterograde amnesia is uncommon.

2. Statins and dementia

Statins reduce cholesterol. They prevent a cholesterol-making chemical. Examples:

• Atorvastatin (Lipitor) (Lipitor)

• Fluvastatin (Lescol XL) (Lescol XL)

• Lovastatin (Altoprev) (Altoprev)

• Pitavastatin (Livalo, Zypitamag) (Livalo, Zypitamag)

• Pravastatin (Pravachol) (Pravachol)

• Rosuvastatin (Crestor, Ezallor) (Crestor, Ezallor)

• Simvastatin (Zocor) (Zocor)

This finding is contentious. Harvard's Brigham and Womens Hospital's Dr. Joann Manson says:

“I think that the relationship between statins and cognitive function remains controversial. There’s still not a clear conclusion whether they help to prevent dementia or Alzheimer’s disease, have neutral effects, or increase risk.”

This one's off the dementia list.

3. Dementia and anticholinergic drugs

Anticholinergic drugs treat many conditions, including urine incontinence. Drugs inhibit acetylcholine (a brain chemical that helps send messages between cells). Acetylcholine blockers cause drowsiness, disorientation, and memory loss.

First-generation antihistamines, tricyclic antidepressants, and overactive bladder antimuscarinics are common anticholinergics among the elderly.

Anticholinergic drugs may cause dementia. One study found that taking anticholinergics for three years or more increased the risk of dementia by 1.54 times compared to three months or less. After stopping the medicine, the danger may continue.

4. Drugs for Parkinson's disease and dementia

Cleveland Clinic (USA) on Parkinson's:

Parkinson's disease causes age-related brain degeneration. It causes delayed movements, tremors, and balance issues. Some are inherited, but most are unknown. There are various treatment options, but no cure.

Parkinson's medications can cause memory loss, confusion, delusions, and obsessive behaviors. The drug's effects on dopamine cause these issues.

A 2019 JAMA Internal Medicine study found powerful anticholinergic medications enhance dementia risk.

Those who took anticholinergics had a 1.5 times higher chance of dementia. Individuals taking antidepressants, antipsychotic drugs, anti-Parkinson’s drugs, overactive bladder drugs, and anti-epileptic drugs had the greatest risk of dementia.

Anticholinergic medicines can lessen Parkinson's-related tremors, but they slow cognitive ability. Anticholinergics can cause disorientation and hallucinations in those over 70.

5. Antiepileptic drugs and dementia

The risk of dementia from anti-seizure drugs varies with drugs. Levetiracetam (Keppra) improves Alzheimer's cognition.

One study linked different anti-seizure medications to dementia. Anti-epileptic medicines increased the risk of Alzheimer's disease by 1.15 times in the Finnish sample and 1.3 times in the German population. Depakote, Topamax are drugs.

The company's epidemic gains are gone.

Mark Zuckerberg was in line behind Jeff Bezos and Bill Gates less than two years ago. His wealth soared to $142 billion. Facebook's shares reached $382 in September 2021.

What comes next is either the start of something truly innovative or the beginning of an epic rise and fall story.

In order to start over (and avoid Facebook's PR issues), he renamed the firm Meta. Along with the new logo, he announced a turn into unexplored territory, the Metaverse, as the next chapter for the internet after mobile. Or, Zuckerberg believed Facebook's death was near, so he decided to build a bigger, better, cooler ship. Then we saw his vision (read: dystopian nightmare) in a polished demo that showed Zuckerberg in a luxury home and on a spaceship with aliens. Initially, it looked entertaining. A problem was obvious, though. He might claim this was the future and show us using the Metaverse for business, play, and more, but when I took off my headset, I'd realize none of it was genuine.

The stock price is almost as low as January 2019, when Facebook was dealing with the aftermath of the Cambridge Analytica crisis.

Irony surrounded the technology's aim. Zuckerberg says the Metaverse connects people. Despite some potential uses, this is another step away from physical touch with people. Metaverse worlds can cause melancholy, addiction, and mental illness. But forget all the cool stuff you can't afford. (It may be too expensive online, too.)

Metaverse activity slowed for a while. In early February 2022, we got an earnings call update. Not good. Reality Labs lost $10 billion on Oculus and Zuckerberg's Metaverse. Zuckerberg expects losses to rise. Meta's value dropped 20% in 11 minutes after markets closed.

It was a sign of things to come.

The corporation has failed to create interest in Metaverse, and there is evidence the public has lost interest. Meta still relies on Facebook's ad revenue machine, which is also struggling. In July, the company announced a decrease in revenue and missed practically all its forecasts, ending a decade of exceptional growth and relentless revenue. They blamed a dismal advertising demand climate, and Apple's monitoring changes smashed Meta's ad model. Throw in whistleblowers, leaked data revealing the firm knows Instagram negatively affects teens' mental health, the current Capital Hill probe, and the fact TikTok is eating its breakfast, lunch, and dinner, and 2022 might be the corporation's worst year ever.

After a rocky start, tech saw unprecedented growth during the pandemic. It was a tech bubble and then some.

The gains reversed after the dust settled and stock markets adjusted. Meta's year-to-date decline is 60%. Apple Inc is down 14%, Amazon is down 26%, and Alphabet Inc is down 29%. At the time of writing, Facebook's stock price is almost as low as January 2019, when the Cambridge Analytica scandal broke. Zuckerberg owns 350 million Meta shares. This drop costs him $71 billion.

The company's problems are growing, and solutions won't be easy.

• Facebook's period of unabated expansion and exorbitant ad revenue is ended, and the company's impact is dwindling as it continues to be the program that only your parents use. Because of the decreased ad spending and stagnant user growth, Zuckerberg will have less time to create his vision for the Metaverse because of the declining stock value and decreasing ad spending.

• Instagram is progressively dying in its attempt to resemble TikTok, alienating its user base and further driving users away from Meta-products.

• And now that the corporation has shifted its focus to the Metaverse, it is clear that, in its eagerness to improve its image, it fired the launch gun too early. You're fighting a lost battle when you announce an idea and then claim it won't happen for 10-15 years. When the idea is still years away from becoming a reality, the public is already starting to lose interest.

So, as I questioned earlier, is it the beginning of a technological revolution that will take this firm to stratospheric growth and success, or are we witnessing the end of Meta and Zuckerberg himself?

Generative AI hype: some thoughts

The sudden surge in "generative AI" startups and projects feels like the inverse of the recent "web3" boom. Both came from hyped-up pots. But while web3 hyped idealistic tech and an easy way to make money, generative AI hypes unsettling tech and questions whether it can be used to make money.

Web3 is technology looking for problems to solve, while generative AI is technology creating almost too many solutions. Web3 has been evangelists trying to solve old problems with new technology. As Generative AI evolves, users are resolving old problems in stunning new ways.

It's a jab at web3, but it's true. Web3's hype, including crypto, was unhealthy. Always expected a tech crash and shakeout. Tech that won't look like "web3" but will enhance "web2"

But that doesn't mean AI hype is healthy. There'll be plenty of bullshit here, too. As moths to a flame, hype attracts charlatans. Again, the difference is the different starting point. People want to use it. Try it.

With the beta launch of Dall-E 2 earlier this year, a new class of consumer product took off. Midjourney followed suit (despite having to jump through the Discord server hoops). Twelve more generative art projects. Lensa, Prisma Labs' generative AI self-portrait project, may have topped the hype (a startup which has actually been going after this general space for quite a while). This week, ChatGPT went off-topic.

This has a "fake-it-till-you-make-it" vibe. We give these projects too much credit because they create easy illusions. This also unlocks new forms of creativity. And faith in new possibilities.

As a user, it's thrilling. We're just getting started. These projects are not only fun to play with, but each week brings a new breakthrough. As an investor, it's all happening so fast, with so much hype (and ethical and societal questions), that no one knows how it will turn out. Web3's demand won't be the issue. Too much demand may cause servers to melt down, sending costs soaring. Companies will try to mix rapidly evolving tech to meet user demand and create businesses. Frustratingly difficult.

Anyway, I wanted an excuse to post some Lensa selfies.

These are really weird. I recognize them as me or a version of me, but I have no memory of them being taken. It's surreal, out-of-body. Uncanny Valley.