Apple, Innovation & Changing A Company’s DNA

Following its announcement of Apple as the most innovative company in the world, Fast Company has a great interview with Apple CEO Tim Cook.

For 30 years I have seen people struggle to understand Apple and their approach to everything from products to revenue. It has always been right here out in the open, its just very different from how most large tech companies approach customers and product.

As Cook himself says:

It’s always products and people. The question at the end of every year, or every month or every week or every day, is, Did we make progress on that front?

Granted this can sound simplistic or like marketing fluff or both, but the proof is in both the consistency of the message by all Apple exes over the years as well as the product line itself.

The reality is that companies have a DNA that is typically set early on. It can be hard to maintain as you grow and is frequently lost, but it is even harder to change the DNA of a company into something like Apple’s if it has never had it in the first place. Few companies have ever managed it. One notable exception in the last few years has been Microsoft under Satya Nadella, which has undergone a tectonic shift in its approach to customers, developers and its products. It can be done. Its just really hard.

Working With Co-Founders

Fred Wilson has a post up on the investor perspective of working with co-founders and the risks to the people and business when things take a turn for the worse in the relationship:

The co-founder dysfunction impacts everyone in and around the company, but mostly the team underneath the founders. It is like being in a family where mom and dad aren’t getting along. There is stress and strain, messed up decision making, and everyone is walking on eggshells.

There is tremendous opportunity for things to go badly wrong, especially for first-time founders. A common area of disagreement can be where the original idea was born from the group but as it becomes a start-up not everyone is willing or able to move forward with it.

While that is totally fair (everyone’s personal and professional circumstances will be different), inexperienced founders can end up with unrealistic expectations of how equity should be divided and vested.

Ultimately that can lead to both an unfair distribution of equity (which should be based on ongoing participation, responsibility and investment, not share of original idea) and resentment. Its very easy to end up in a situation where everyone is unhappy and the whole concern collapses or is at least thrown into chaos (which Fred alludes to.)

I have been very fortunate to work with 3 co-founders across 2 companies where we each understood and respected the area of strength of each other. In these situations you can build very powerful complimentary teams of co-founders. And all 3 are still very close friends to this day.

Steven Sinofsky’s Observations From CES

Steven Sinofsky has been writing some fantastic articles since he left Microsoft and I am an avid reader. You should follow him on Twitter. Go do that now.

He put together a very detailed and enjoyable piece on CES (saving me and countless others from having to endure it ourselves).

He has five key takeaways, which I am not going to spoil for you other than to give you the headlines, because you need to read the whole article:

  • Voice.
  • Electronics for the home.
  • Cars.
  • No wires. 
  • But still too much technology at the endpoint…

Seriously, go read the article. There are lots of great photos too.

The Next Decade: The S Curve of Machine Learning

A little over 20 years ago, I read something from a technology analyst that completely changed the way my early twenties self thought about the world, that has stayed with me ever since.

I had already been using the Internet for about 8 years at that point, initially as an academic network and now in the early days of its commercial use.

I was one of the founders of a technology company and had just moved to San Francisco to set up the U.S. operation for. At the time I thought I was pretty much on the cutting edge because I had an ISDN line in my apartment, which was essentially like a much faster version of dial-up. (It could even bond two channels together for a 128kbps experience.)

The analyst was talking about Yahoo! and in particular its 12 month target stock price. The details of that are lost to time, just like the company itself essentially. (Hello Verizon.)

The comment that he made was that you need to think not of the world as it was then (small numbers of millions of people on the Internet, almost all using 28.8kbps dial-up) but rather the world as it will be. The comment that stuck with me was something to the effect of “Imagine everyone has an always-on high speed Internet connection and you can take that as a given. Now what kind of applications can you build on it?”

Timing is always the hard part but that concept has influenced not only my thinking but the three companies I have started.

I was reminded of it again when listening to Ben Evans talk about S curves and what the future might look like in another 10 years. He touches on mixed reality and crypto-currencies but he spends a good deal of time providing one of the clearest business explanations for machine learning that I have seen. Well worth a watch.

BTW, my favorite line is “every person in this image is a cell in a spreadsheet and the entire building is an Excel file” when talking about automation and referencing this scene from the 1960 Billy Wilder movie The Apartment:

Why Does Apple Have so Much Cash?

The insightful Horace Dediu writes some of the easiest to understand pieces on Apple and its economic model. Many people know that Apple is sitting on a large cash pile, over $270 billion (with a “B”) but people ask me sometimes why they don’t spend it (by doing large acquisitions) or give it back to shareholders (dividends or share buybacks.) The reason they don’t do the former is cultural and they do actually do the latter. Horace has put together a great FAQ on all of this.

Going Global With Your Startup

The Y Combinator folks are on a roll with another great post, this time on Going Global With Your Startup.

Kwindla Hultman Kramer from highlights 5 key issues he has experienced and heard from other founders when it comes to selling your product or service outside the United States:

  1. Fulfilling international orders is still surprisingly complicated and expensive
  2. If you’re opening an office in a new country, put someone who already knows your company well in charge of that process
  3. Anywhere you have employees, you need an accountant and a lawyer
  4. Work visas are complicated, expensive, and stressful
  5. You’re going to need to get on an airplane sometimes

There is lots more detail and good advice in the post so you should read the whole thing. (I’ll wait…)

I have taken a software company global in both directions – first a company started in Europe (Ireland) that expanded into the United States, and then my current company Qstream, which started here in Boston and then expanded into Europe (Ireland and the UK.)

I have no experience with #1 because my companies have never made physical products. (Technically I guess that is not quite true because my first company was long enough ago that we originally shipped the product on 3.5″ floppy disks and later on CDs. Good times. Good times.)

However, I have dealt with #2 through #5 extensively and strongly agree with the recommendations. I have spoken to groups of entrepreneurs and individuals in the past about going global and you can definitely short-circuit learning a lot of lessons the hard way by talking to someone who has done it.

It is still remarkably complicated to set-up and run a business in multiple locations, particularly for a start-up that is resource constrained. You also need people who know the local culture, customs and laws. If you can get the balance right (don’t rush to do it, take it slow, one country at a time) then it can be a big competitive advantage.

Microsoft Wrote a Book on The Ethics of AI

The good folks at Microsoft have published a book [PDF] (“The Future Computed: Artificial Intelligence and its role in society”) and associated web site on the ethics of AI.

The Future Computed

The executive summary cites the following proposed principles:

  • Fairness: When AI systems make decisions about medical treatment or employment, for example, they should make the same recommendations for everyone with similar symptoms or qualifications. To ensure fairness, we must understand how bias can affect AI systems.
  • Reliability: AI systems must be designed to operate within clear parameters and undergo rigorous testing to ensure that they respond safely to unanticipated situations and do not evolve in ways that are inconsistent with original expectations. People should play a critical role in making decisions about how and when AI systems are deployed.
  • Privacy and security: Like other cloud technologies, AI systems must comply with privacy laws that regulate data collection, use and storage, and ensure that personal information is used in accordance with privacy standards and protected from theft.
  • Inclusiveness: AI solutions must address a broad range of human needs and experiences through inclusive design practices that anticipate potential barriers in products or environments that can unintentionally exclude people.
  • Transparency: As AI increasingly impacts people’s lives, we must provide contextual information about how AI systems operate so that people understand how decisions are made and can more easily identify potential bias, errors and unintended outcomes.
  • Accountability: People who design and deploy AI systems must be accountable for how their systems operate. Accountability norms for AI should draw on the experience and practices of other areas, such as healthcare and privacy, and be observed both during system design and in an ongoing manner as systems operate in the world.

Technology itself of course is just a tool and tends to be inherently amoral (in the sense of lacking the concept of morals.)

Those of us who create it and use it are a different story.

We can choose whether to use a technology and how to use a technology and there are very real implications for society, short and long term, for all these choices. What we can’t do is un-invent it. (Much as occasionally that seems like a good idea.)

What we can, and must, do as a society is decide on a set of ethical principles of how we will use any such technology.

This is also complicated when something is brand new, or, as in the case of AI, technology reaches a new level of critical mass it had not previously achieved.

Due to its newness, as a society we usually find that we haven’t developed these principles yet. Also in our world of software the rate of change tends to be very high. It can feel like we have gone from something not existing to being pervasive in a heart beat.

Many people in the AI community have been debating ethics around AI for decades, so like AI itself as a discipline, this is not a new topic and in many ways the Microsoft book does not break new ground. With the soon-to-be pervasiveness of AI though, Microsoft has a global platform to bring a much greater awareness to the issues and that is to be applauded.

Some of our brightest minds have very publicly raised their concerns of the potential problems with AI, notably Elon Musk and Stephen Hawking. We should use their opinions and books like this to have informed debates as we continue to push AI forward.

Advice For First Time Founders

Y Combinator has a great post that starts with 3 questions:

1. What are some things that you should’ve known as a first-time founder but did not?
2. How did you learn them?
3. How did they help?


They collected responses from a bunch of founders and there is some great stuff in here.

Part of me wonders though how much it will resonate with someone who hasn’t yet dealt with the issues raised.

I see so much good advice in here, based on lessons learned the hard way across the 3 companies I have started, but part of doing a tech startup is having a certain amount of healthy delusion. Delusion about how good your idea is and your likelihood of succeeding.

This is necessary because startups are one of those endeavors where if you knew how hard it was going to be and how long it was going to take, you would never do it. People will tell you you are crazy. You will hit roadblock after roadblock. You need this delusion to persevere.

But it is also your Achilles Heel. How do you recognize good advice (on hiring for example) but ignore bad advice (on your go-to-market model say)? Here your healthy delusion can become just plain old delusion and you end up having to learn the lessons the hard way.

I’ll let you know if I figure out the answer to that one. In the meantime, the responses are full of pearls of wisdom. Also, as is often the case on Hacker News, the comments are great too.