wrong tool

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

Dealing with the FUD machine

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One of the most exasperating professional situations is dealing with the FUD machine.

The FUD machine is the following:

Team A has a proposal.

Team B disagrees and has an agenda to propose an alternative and the alternative is not ready yet.

Team B instead of arguing that the proposal of Team A is problematic focuses on narrow limitations.

Team A responds to narrow limitations.

Team B comes back with more limitations.

Time for Team A is lost and forward progress is lost.

Team B is then able to use the failure of Team A to move forward on their proposal, and argues that their new proposal is better and is able to take over the project.

Team B then basically does what team Team A was going to do.

The core of the FUD machine is that Team B wants to win the project and is trying to buy time and destroy Team A’s credibility to take over the project.

The good news is that I have rarely experienced this within a company. I have experienced this in competitive situations between vendors as a customer in the role of Team A.

So what do you do?

If you’re team Team A member, the first step is to realize the FUD machine is attacking.

The strategy to win is not to fight the FUD machine.

Focus your response on the business problem. Explain why your solution is better for the problem. And then – and this is important – explain why your solution does solve the problem and point out that their solution – as is – won’t meet the business requirements.

 

 

P-zero or Die

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VMware has – possibly – the coolest skunkworks system at any company. Skunkworks projects are shared at a three-day internal conference known as RADIO.

During the year, employees across the company work on projects and produce papers based on those projects whose only purpose is to share them at RADIO and possibly get funded later on.

Andrew Lambeth is a Fellow and all-around amazing person who gave an excellent talk titled P0 or die. The point of the talk was how to take any big idea that you had and get it funded.

The fundamental principle of the talk is that if you don’t make your big idea a p0, it will get deprioritized for other stuff. And the reason it got deprioritized is that it’s big, and its value proposition was unclear, and people didn’t understand what you were trying to accomplish.

And so here’s the checklist of things you need to do to make your big idea someone else’s p0.

  1. Describe it effectively in 5 minutes
  2. Make sure that success is easy to measure
  3. Your listeners must understand, not agree.
  4. No slides.
  5. Describe it on a whiteboard
  6. Pitch at every opportunity, relentlessly

And the most crucial thing is step 7:

If you get no traction, then move on to the next big idea.

Sometimes a big idea’s time has not come, and you just need to let it go.

I liked the talk so much that I decided to make a t-shirt.

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.

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.