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Is the great cloud shell game over?

March 3, 2019 by kostadis roussos 3 Comments

With the success of VMC on AWS, it’s time for us to admit that the Cloud Native programming model as the dominant and only programming model for the cloud is dead.

The Cloud Native programming model was a godsend for IT and software engineers. The move from CAPEX to OPEX forced all of the pre-existing software that ran on premises to be completely re-written for the cloud.

Jobs, careers, and consulting firms exploded as everybody tried to go on this cloud journey.

It was like the great y2k rewrite, which was followed by the C++ rewrite, which was in turn followed by the great Java rewrite …

This rewrite was forced because On-Prem software assumed infrastructure that did not exist in the cloud and could not work.

On-premises, you have very reliable and robust networks, storage that offers 5 9’s reliability, and a virtualization infrastructure that provides automatic restart-ability of virtual machines.

Furthermore, on-prem you had the opportunity to right-size your VM to your workload instead of playing whack-a-mole with the bin-packing strategy known as “which cloud instance do I buy today?”

The problem with on-prem was that you had to buy the hardware, and worse you had to learn how to operate the hardware.

The operating environment for the cloud where networks are unreliable, storage is unreliable, and applications must be HA aware and integrate with HA aware systems required a full-rewrite of perfectly fine working software.

What motivated this mass migration and mass rewrite?

The motivation was that new software could legitimately be written faster on the EC2 PaaS. Furthermore, companies didn’t want to own the hardware and wanted to rent their infrastructure.

The two factors pushed companies to look at Cloud Native not as a way to augment their existing software assets, but a once-in-the-lifetime opportunity to rewrite their software assets.

But it turns out that is hard. And it also turns that the pre-existing operating model on premises is kind-of-valuable. Instead of every application having to figure out how to deal with infrastructure that’s flaky, it’s just simpler to have a more robust infrastructure.

And now that the cost and agility advantage of the cloud has been in-part neutralized, what I hope we might see is a collective pause in the software industry as we ask ourselves – not whether it’s on-prem or cloud-native, but what is the appropriate programming model for the task at hand.

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Filed Under: Hardware, innovation, Software

The archer and the gun and misunderstood impact of automation

December 29, 2016 by kostadis roussos 2 Comments

Last night I went to a great burger place in Arnold, called The Giant Burger. And I sat there waiting for my burger to arrive, I had a chance to reflect on the impact of automation.

The Giant Burger is not a fast place. It’s a place for great food. Not a place for getting great food fast. The reason is one of the employees will carefully assemble each burger to order. And because orders are big, and because she is one person, orders come out at about the rate of six per hour.

And as I was staring at her and thinking about machines, I was wondering what do machines do?

What machines do isn’t replace human beings. What they do is make less skilled workers more skilled.

Consider in the middle ages the archer. Being an archer requires a lot of skill and practice. You had to train from a young age and continuously hone your craft. In some sense, you could argue that archers were the artisans of war.

And then the gun showed up. And it wasn’t more reliable and more efficient than the original long-bow, but you could find 50 people hand them 50 fifty muskets and do almost as much damage as the archer.

In short, the gun made large armies of archers possible by reducing the skill requirement.

And that happens over and over and over again.

Look at the modern military drone. I can’t fly an F16 because I am too old, too tall and too fat. I could fly a drone. And there are more middle-aged fat guys than there are highly trained fighter pilots.

And so what happened?

We have drones all over the world killing random terrorists because we can have armies of fat people sitting in rooms flying a robot.

We have more people killed from the air than at any time since the Gulf War, and not a single pilot has done the kill.

Or look at the DaVinci system for surgery. To date, surgery was about skill. Surgeons were more athletes than scientists. With DaVinci, the skill necessary to do surgery will decline over time.

What automation does, what machines do, is they reduce the value of specialized skills and democratize those skills. And in the process make the value of the human labor declines because the number of people who can do the task increases, thereby reducing salaries.

And now software is making it worse. In the past, upgrades required new physical systems, now with software we can upgrade existing systems in place. And because of how electronics work, we can improve the intelligence of systems at the rate of 2x every 18 months.

And where it gets interesting is that in the past before software, mechanical systems had to be carefully engineered. For example, a mechanical lever has less tolerance than a computerized control system that can make micro-adjustments very quickly.

In short, we can innovate faster and cheaper than ever before in creating machines that make anybody be able to do anything.

Automation isn’t about replacing people; it’s about eliminating the need for skill and with that we remove the value of training and with that, we replace the highly trained archer with conscripts.

Which begets the obvious question:

So what?

Given that the value of skill is declining faster and faster, then that implies that the value of most human labor is decreasing, and therefore the per-unit cost of paying someone to do the job is below what people would accept.

And so when we say: Automation is killing jobs, what we are saying is that automation is causing the price we are willing to pay for humans to do jobs is decreasing.

And then we get to the policy prescriptions.

1. Some kind of universal income

One approach is to realize that there is a net surplus labor force at the current labor price, a price artificially kept high because of the minimum wage, medicare, food stamps, etc. And recognize that that group of people is going to have to die off, or leave the country for the surplus to get eliminated and in the meantime continue to extend those benefits including something like a universal income.

The problem is that that group of unemployable people is going to expand over time.

And the other problem is that there will be an increasingly shrinking set of people who will subsidize the lives of those whose skills have no value at the current price.

2. Make human labor competitive by retraining

This approach recognizes that it takes some time to build computer systems that can replace all skills and that the computer systems themselves need human operators. And so we continuously retrain people.

The challenge is that during retraining people are not employed and post-retraining the value of the labor is low. And so humans continue to experience points in time where they make less money and don’t have access to a stable income.

This also has the problem that the cost of the training has to be covered. And the folks who are making money will resent that their money is helping other people.

3. Make human labor competitive by lowering price and over time increase the price by reducing the number of people in the labor supply.

Another policy prescription is to cut those benefits such that the surplus labor becomes competitive with machines at a much lower price point, and then rely on other policies to cause the labor pool to shrink over time.

For example, a starving man will work for less than $7.25.

Cut his medical coverage, and a sick person will die off quickly.

Cut off his social security, and when he is too old to work, he will die of hunger and illness.

Restrict immigration and the number of people who enter the country will decrease over time.

The net effect will be that surplus labor will decline over time. In the short term there will be some pain, but in the long run, this will work out.

In the press, there is some discussion of the heartlessness of the tech industry because we create the machines that displace skill.

Tech is amoral. Our policy prescriptions are moral. If you are outraged with the outcomes of an amoral device, go ask yourself what policy prescriptions do you favor?

 

 

 

 

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Filed Under: innovation, Jobs, Software

AWS and the automation of retail

December 29, 2016 by kostadis roussos Leave a Comment

I as noodling on how automation was affecting industries. And I was also noodling about cloud in my role at VMware.

And that got me thinking about what is going on with retail because it is the Christmas season.

Amazon is forcibly re-engineering the entire retail supply chain to be digital.

You use a mobile device to find and then buy stuff. If your business doesn’t have a mobile presence, your business is not reaching a staggering number of customers.

The change from brick-and-mortar to digital interaction is so huge that it’s got its own name: Digital Transformation.

Then this got me thinking about, how does this affect society?

The computers sitting in the cloud are doing the job of the retail employee who would help you find stuff, and then ring you up at the register.

 

This retail season, I spent a lot of time thinking about the macro of the cloud. And I realized that the macro of the cloud is that anyone in the retail industry is moving to a cloud service model because they need a peek burst capacity. During the gift-giving season, retail makes more money and employs more people than at any point in time. And the total number of people they require during the low retail season is significantly less.

And the computing capacity required during the low retail season is significantly lower. And since the fixed cost of peek burst capacity is very high, it makes a lot of sense to spin up capacity on demand in the cloud.

And that got me thinking – what happened before?

And the answer is what we used to call seasonal hiring.

And if I was right then the impact of automation on seasonal hiring should already be visible in hiring patterns.

And lo and behold:

http://www.retaildive.com/news/bucking-trend-jc-penney-hiring-many-more-seasonal-workers/426625/

Last year’s job gains were 1.4 percent lower than 2014 figures, according to employment data from the Bureau of Labor Statistics cited by Challenger, Gray & Christmas. “We continue to move from brick-and-mortar toward click-and-order,” Challenger, Gray & Christmas CEO John A. Challenger said in a statement. “But even in the internet era of holiday shopping that means that brick-and-mortar fulfillment facilities need seasonal workers.”

 

 

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Filed Under: innovation, Jobs, Uncategorized

God is alive, and scientists resurrected him – Nietzsche.

September 15, 2016 by kostadis roussos 1 Comment

My dad’s a scientist. Apparently a well known one. To me, he is just my dad.

And I suppose, because of him, I acquired a healthy respect for reason. And science.

And then this bullshit happens:

In the 1960s, the sugar industry funded research that downplayed the risks of sugar and highlighted the hazards of fat, according to a newly published article in JAMA Internal Medicine.

The article draws on internal documents to show that an industry group called the Sugar Research Foundation wanted to “refute” concerns about sugar’s possible role in heart disease. The SRF then sponsored research by Harvard scientists that did just that. The result was published in the New England Journal of Medicine in 1967, with no disclosure of the sugar industry funding.

Sugar Shocked? The Rest Of Food Industry Pays For Lots Of Research, Too

The sugar-funded project in question was a literature review, examining a variety of studies and experiments. It suggested there were major problems with all the studies that implicated sugar, and concluded that cutting fat out of American diets was the best way to address coronary heart disease.

The article got me curious. And so I asked a friend of mine who studies the scientific process what he thought:

Diet effects, especially for single nutrients, such as sugar or types of fat are so difficult to establish with observational studies that basically what you see published in the literature and then further cherry picked by media is the algebraic sum of all the ridiculous opinions of opinionated professors plus all the bribery inputs of the industry.

My friends happen to be the set of people who recognize that global warming is a fact. My cousin happens to study some of the phenomena for NASA.

And my friends and family wonder why so many people choose not to believe or listen.

And it’s because of this bullshit. When scientists decide to sell out to advance their agendas or buy a new car, science is the victim. And science and reason are the only things that can save humanity. And we’ve managed to kill science.

Scientists took advantage of the age of reason, of our need to believe in rational gods, to lie to us for their petty interests. And now we are reaping the whirlwind.

Why should we trust anything they say?

If Nietzsche were around his new tag line would be “Science is Dead.” God, on the other hand representing the age of faith and lack of reason, he would note is very much alive.

 

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Filed Under: innovation, Science

Beyond whether Theranos Works

June 4, 2016 by kostadis roussos 1 Comment

Recently watched this interview with John Ioannidis on http://www.bloomberg.com/news/videos/2016-05-27/is-it-science-or-hype-behind-theranos-claims.

John, who happens to be a friend, was one of the first people to ask the obvious question: where’s the peer reviewed research that proves Theranos’ claims?

And yet, John makes a more important point.

Even if Theranos’ technology worked, is it a good thing?

Taking continuous blood tests will result in more procedures and more diagnostics and more medical procedures than are necessary increasing misery and putting patient health at risk without substantially improving the health of the patients.

We view authority these days with suspicion. There is a strong temptation to get rid of the middle man gatekeeper of medical health known as the doctor. And yet specialized knowledge adds value and it’s unclear whether complete disintermediation is a good thing.

In other words, it’s unclear that being able to take continuous blood tests is a good thing, period.

Before some other startup tries and resolves the technical limitations of Theranos’ technology, perhaps we should ask as a matter of public policy if such a technology is useful?

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Filed Under: innovation

Engagement and Retention Do Not Move and the limits of Big Data

February 18, 2016 by kostadis roussos 3 Comments

When I was at Zynga, we shipped a game that had marginal success. And in the deep dive the product management lunch lead said:

Engagement is really tough to move

At Zynga, Mark would demand game-changing features, demanding that we change the product in place and if we could pull that off the theory was we could change the retention curve.

And being an engineer, and surrounded by entrepreneurs, my assumption was that through the application big data and science we could change this number.

Then I had the opportunity to try and drive machine learning models into games to improve core metrics of the game.

The theory was that auto-tuning the game would improve engagement and retention.

And it worked, to a point.

And what I realized was that the hard problem is building a fabulous product. And a great product has high engagement. Everything else we do is about tuning or improving the excellent product at the fringes. And that changing engagement is equivalent to creating a new product.

And that got me thinking as to why that is impossible. And what I realized is that big data collects information about the product that is. And can only answer questions about what your product is doing.

To change engagement, you have to build a new product with new features and net new capabilities. And the data for that product doesn’t exist in any of your big data systems.

The short version of this story is the following, engagement is what it is, and if it isn’t what it needs to be, you need to scrap the damn thing and start all over.

 

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Filed Under: innovation, Zynga

How to build a product

February 14, 2016 by kostadis roussos Leave a Comment

There are two fundamental approaches to building products, a technology first and a customer first approach.

The technology first approach examines what is possible and based on what is possible builds something.

The customer first figures out what customer need is required and then builds something to satisfy that need.

I have had the opportunity to pursue both approaches in my career. And what I have observed is that they can both result in poor results.

The technology first approach can produce something that no one wants.

The customer first can produce a few deals that never grow past a certain point.

At one of my jobs, the GM had the head of product management and myself at each other’s throats. The head of product was very customer centric. I was very technology focused. And the GM would only approve a new project if we both agreed. Sometimes, the head of product would wear me down, and I would grumpily agree with his ideas. Sometimes, I would wear the head of product down and he would grumpily go along with mine.

And those ideas had marginal success.

The best products, the ones that were huge successes were the ones where we both could see how this would satisfy the customer need and that there was real technology innovation.

 

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Filed Under: innovation, Selling, Software

The Three Release Rule

February 13, 2016 by kostadis roussos 2 Comments

When releasing a major new piece of functionality, I have this theory that it takes three major releases actually to deliver what the customer wants.

The first version is what you could build within the time and resource constraints available to you.

The second release is a set of bug fixes and desperate hacks to make the product more attractive.

The third release is the product the market wants.

Because it takes three releases to build a product the market wants, getting that first version out as fast as possible is so important. And not to get too anxious about whether the product is actually what the market wants.

Once you ship, then you get customer feedback and fix the thing you shipped. And while fixing issues and selling the product you learn the thing the customer wanted.

And then you build that.

Most products fail because we spend too much time and resources trying to make the perfect product or fail because we never get to version 3.

 

 

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Filed Under: innovation

Going to work for SpaceX … No, not really.

November 15, 2015 by kostadis roussos Leave a Comment

Four years ago, Andy Van Dam let me give a small talk to his CS 15 class. I stood in the auditorium, in front of 200+ students, and had a momentary epiphany.

I told the class:

You are the luckiest people on the face of the earth. When I started my career, working on computers meant building systems for banks, or weapons to kill. Software has now become embedded in every aspect of our lives. Everything you want to do has a software angle. You can save the world, or build a weapon, or save a child and still work on software. You can follow your passion and dream and work on software. And I am envious of you.

I think I said it better than Andreesen. But I am not a venture capitalist and the inventor of the web browser.

The point is that if you want to write software for a living you can do anything you want.

My son keeps asking, what do you do for a living daddy? And I keep trying to explain software. Try getting a child to understand what writing software is.

Every night, I read a book to my son. And today we read about the Curiosity rover. And I remembered that Jim Kurien, an old Brown University friend, had written software for that program.

And my wife said:

See, Nicholas if you write software you can work on robots that go to Mars.

And my son full of awe and admiration and eyes bigger than saucers asked:

Daddy, Daddy, do you work on robots?

For a moment, I was his hero and cool. My work wasn’t something that took me away from him, my work was something special. Software was special. The lightbulb of why I did what I did went on.

And I said:

No.

And my son looked as if I was the dumbest man on earth. Because if I could work on robots, why would I not be …

And then I turned to my wife and said:

I hope you like LA. You know, the headquarters of SpaceX.

All humor aside, I am happy with my job, and I love the problem I am working on, creating a unified virtualized hybrid infrastructure. Not as sexy as the Curiosity Rover, and probably not as transformative and just as exciting to me.

And hopefully, my son will learn that lesson in life. That what is interesting to you is all that matters.

 

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Filed Under: innovation, Jobs

How Stanford Screws the Middle Class

October 4, 2015 by kostadis roussos 3 Comments

One of my personal enduring mysteries was why do the top 100 private colleges charge about the same amount of money for tuition.

Given their wide variance in size, location, and endowments, you would expect to see a wide variance in price.

Except you don’t. The list price for a college education is about the same.

And then I spoke to someone who is deep in the bowels of Stanford’s budget and figured out how exactly Stanford is screwing the middle class.

Let’s begin with the following startling observation. Stanford has two sources of revenue. The first is a draw on their endowment. The second is their ability to issue bonds to borrow to build (check out http://bondholder-information.stanford.edu/home.html)  Tuition, is a drop in the proverbial budget, a rounding error.

Just to make it real, the draw on endowment is about 5% a year so

21.4 billion * 5% = 1.07 billion

Student tuition = 14k * 3 quarters * 7k = 294 million

Ah you say, look! it’s 30% of the budget… except. about 4679 get some kind of tuition reduction, so let’s cut that number in half so it’s about 150 million dollars or about 15% … A drop in the proverbial bucket in a billion dollar budget.

Let me think about this for a moment. Stanford benefits from tax exemptions from gifts and simultaneously benefits from tax benefits while borrowing money while demanding money it doesn’t need from parents after tax income.

Hmm…

Let me repeat, the tuition that basically destroys a college graduate’s ability to buy a house or devastates a parents retirement is a rounding error in Stanford’s budget and comes from your taxable income.

Is this about Stanford? Certainly not, it’s about Harvard and Yale and Brown and by implication every institution of higher learning that is charging more money because they can.

Why does your college education destroy your life and your parents retirement? Because we’re stupid enough to pay for it.

 

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Filed Under: innovation, Jobs

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