wrong tool

You are finite. Zathras is finite. This is wrong tool.

Let’s start using Technical Leverage instead of Technical Debt

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Over the last year, I’ve been struggling with the term technical debt.

The theory behind technical debt is that there are choices we make that cost money later. And that’s motherhood and apple pie.

The problem with that phrasing is that there is an implicit assumption that technical debt is a bad thing because all debt is bad.

And that is just profoundly a wrong conclusion.

Debt is how you get leverage in the business, and it’s how you get leverage in time in engineering. And engineering is a tradeoff between time and resources.

More generally, because of the negative connotation of debt, the theory of technical debt says that:

Engineering tradeoffs aligned with business priorities are bad if hurt they architecture

And that is the wrong answer. Because if the business priorities result in growth and success, then this was the right tradeoff between time and technical correctness.

Engineers can use leverage to go faster, and like businessmen we can overdo it. And when we do — well there are consequences.

Beyond whether Theranos Works

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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?

A Case Study in Hiring in the Unicorn Era.

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There was a recent article on business insider that described how an engineer managed to get an annual salary of 250k.

What is interesting is not the negotiations, what is interesting is how fake valuations for an illiquid stock are being used to raise wages.

The lead of the article is that the engineer got 250k. The interesting  bit is at the bottom:

On all but two of his offers, he negotiated. The base salary was mostly the same at around $130,000 a year. He negotiated more aggressively on RSUs and signing bonuses.

Fascinating.

Non-Unicorns have a hard time competing for talent. 

No company was offering more in cash. Every company was offering more in equity. And here’s where Unicorn valuations help. A Unicorn can make a salary offer that is worth a lot through their RSU’s, an offer that can compete favorably with Google even though – in practice – the liquidity of Google and the values of the stocks is an apple to oranges comparison.

And so if you are not a Unicorn, you can’t compete. You simply don’t have the valuation necessary to get people to join. And this forces more and more companies to become unicorns so they can compete for talent.

 

Unicorns have to compete on dollars with Google

AirBnb could not offer less equity than Google and convince the employee to join. Theoretically, AirBnb should be able to offer less equity and win because of the growth potential. And yet AirBnb was unable to close this deal.

The net is that Unicorns are treating their private valuations as public valuations and using the face value of the valuations to attract and hire talent. Because the RSU’s are being treated as equivalent to Google stock even though they are much riskier, a down-round can be terrifying to a Unicorn. If employees are looking for a lottery ticket instead of a mission, a down round can result in a horrific wave of attrition. Stock going down in the public markets is annoying, stock going down in the private markets may convince employees that the stock may never be liquid. In effect, a down round may convince employees that the stock is now worthless.

The future 

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I read about the first vr system in the foundation in my teens. I saw the first at brown at 20. My son saw his first at a museum at 5. His child will have occular implants. 

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

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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.

 

How demand creates valuations

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Great post here by Mark Suster at Bothsid.es

Lots of good stuff here.

My key takeaway is here:

The vast majority of this recent boom in prices is not being driven by VCs but rather by hedge funds, mutual funds, corporate investors and other sources of non-traditional venture funding. In the chart below you can see that a decade ago for every dollar a VC raised from LPs a dollar went into a startup. Now for every dollar a VC raises $2.50 goes into a startup.

Many moons ago, I wondered where the hell the money was originating. Mostly non-VC money chasing yield has descended on the valley. And like the sub-prime mortgage crisis, the non-VC money figured out that using preferential terms allowed them to invest in riskier assets with less risk.

Shifting risk, and increasing the value of assets without increasing their value never ends well.

 

 

 

 

 

Manufactured Unicorns

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A friend of mine and I have been debating unicorns for months. And what we concluded is that there are two stories.

The first story is that there are a bunch of excellent businesses that are worth billions. These are the true unicorns. I won’t hazard a guess of how many or which companies fit that bill.

The second story is that there are a bunch of donkeys with horns that are using creative deal structures to acquire valuations that are questionable. And that the story of the true unicorns is hiding the story of the donkeys.

The recent article on TechCrunch tells us that the number of potential donkeys is on the rise.

And so my buddy and I debated the impact of these potentially faux-Unicorns.

The first narrative, dominating the press, is the effect of faux-Unicorns on investors. After a lot of discussions, we concluded that the recent faux-Unicorn phenomenon of artificially constructed valuations is benign to positive. Positive because it mitigates the downside risk, and because it captures more of the upside if an acquisition occurs at the cost of investing more capital in a business that has achieved a certain amount of success.

 

 

 

The second narrative that is emerging is the impact of faux-Unicorns on employees. There we agreed that the story is downright appalling. The shift of risk from investors to employees who find themselves locked in, or worse, have their interests misaligned with the core investors, or even worse is not positive.

The derisking for investors and founders is increasing the risk of employees.

And so what?

The danger is that employees eventually figure this out. And they start demanding higher salaries, longer periods between when they quit and when they have to sell their shares or just plain refuse to work for any private company.

Furthermore, as more employees figure out that a Unicorn or a startup is not a path to riches, and that the investment strategies are being used that minimize their already minimal chances of wealth, people will over the long-term lose interest in working at startups.

And worse, because employees are not investors they have a hard time disambiguating faux-Unicorns from real Unicorns.

If a startup is a job where you work long hours, at low pay, to change the world, there are a lot of options that are not working in tech.

We can talk about culture, and opportunity and learning and if there is no money, then people will go elsewhere.

The people who work in the tech sector have the ability to do anything they want. And eventually, they figure that out.

 

 

 

How to build a product

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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.

 

The Three Release Rule

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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.

 

 

Why join a large company?

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In my career, I’ve chosen to work at larger companies instead of smaller ones. Or maybe, a better way to describe it, is that I have joined small sized businesses that became enormous.

And I was reflecting on the differences.

My favorite game of all time is Civilization II. Some players, like the beginning game:

civ3ptw_screen006

The point is that you have the fewest pre-existing constraints. You can only make a small number of decisions. And each decision is a matter of life or death. You can very easily get destroyed if you make the wrong choices.

That part of the game is the most intense because every decision is, of course, critical, every moment obviously a life or death moment. You choose to explore a village, and if it turns out to be barbarians and you don’t have an army you lose the game.

In the later phases of the game, you have many cities and an accumulation of decisions. You can quickly destroy other civilizations, build wonders, and typically there is no existential threat. No decision is obviously existential. No crisis is obviously critical.

There are existential threats; they are just not obvious.

The challenge in this part of the game is how to marshall resources and make decisions to win the end-game while ignoring things that are not important.

When you join a large company, you are essentially being given someone else’s Civ game. Your job is to figure out what decisions your predecessors made that are prescient and build on those while figuring out what new decisions need to be made to win.

The funny thing about joining a Civ game in the middle phase is that you may have already lost. Some decisions made early on may have put your civilization in a terrible spot that are only obvious now.

And for many folks, this makes starting a game more attractive than taking on an existing game.

The funny thing is that when you launch a new game, the computer makes decisions for you, and those decisions, when combined with your preferred strategy, will doom you just as much.

The thing about the early part of the game is that when you lose you’re just a historical footnote, a leader of a small tribe. When you lose later on in the game, you may have a large thriving civilization that just didn’t make it to the finish line. You fail as a CEO of 10 person startup, and only your web page records your failure. You fail as the CEO of Yahoo, and it’s front page news on the WSJ.

The later part of the game in Civilization II can be more thrilling. You get to do more stuff, try more things, you are on a bigger stage and you are playing for all of the marbles instead of a chance at all of the marbles.