About klingnathan

Auburn based father, singer (musician?), actor, hiker, over-educated (4.5 degrees), sci-fi dweeb, Berlin-loving renaissance man.

Five for Friday: AI sans Musk, Leadership + culture, and black swans


Well, after a few busy weeks that wreaked a little havoc in my writing time, this week we’ll start with the pending downfall of mankind due to the rise of the machines.  Wait, no, that’s not what this is about … that was a few weeks ago.  But look at this article about how a couple of robots came to be the newest hires at a Wisconsin factory in search of reliable workers. It’s just the title of the article this time.  Also, there was that story about how Facebook pulled the plug on some AI that developed its own language that humans couldn’t understand, and while nothing wrong with being excited about all the opportunities that AI will bring for the future, at the same time we need to look at its consequences from all perspectives while Salesforce set out to create “AI for everyone”—to make machine learning affordable for companies who’ve been priced out of the market for experts. They’ve promised to “democratize” AI.  While we’re at it, all that big data has to be stored somewhere.  Look at how it’s impacting one small Oregon town.  Then there’s the rise of AI forcing Microsoft and Google to become chip makers and the business of artificial intelligence.  Oh, and Microsoft replaced Mobile with AI as one of its top priorities in their most recent annual report.

Think that AI isn’t really touching your life?  Check this out from Venture Beat.

I was a little surprised when I read this article from Business Insider about what three of the potential black swans are that will likely trigger a global recession in 2018 as I didn’t see any of the others I’ve seen a lot of news about of late, but I think they do a great job of laying it out.  What with the current potential instability from governmental posturing, it’s likely that the global economy will be impacted sooner rather than later, but these are some more to keep an eye on.  That said, it’s logical that we should see a major market correction based on historical data and trends.  What’s odd is how changes in the trading systems have likely artificially inflated the markets as well.

You may have heard the news about how some hackers stole a whole bunch of money from the Ethereum platform, but did you hear the story about how a bunch of other hackers stole it back?

Both Gates and Zuckerberg are sounding alarms about jobs.  Should we listen?

Being a week full of cheer and merry-making (I mean, it wasn’t a week full of bad news, just odd news, which seems to be our current era), let’s move to some leadership-focused articles.  Strategy+business has had a few good ones of late, from one about how improving company culture is not about free snacks, to another about how leaders can improve their thinking agility, to this article about what the body tells us about leadership. Along with that is an article I think every leader or hiring manager should read about why emotional intelligence is so important to consider when hiring (this is a key area I focus on with every candidate I interview) and somewhat unrelated but still “fitting” in the category is this article from HBR about the personality traits of good negotiators.

Any time I feel like I’ve had a circuitous route to the world of tech, I come across another article about how those of us with humanities backgrounds are in high demand in the tech world.  Why?  It seems to boil down to ideas explored in this article: making stuff vs. making stuff people want.

Right, so I know I’m avoiding the elephant in the room with the Google memo that came out, but I’ll leave you with this orthogonal piece instead: not only has Kalanick been removed from his job as CEO at Uber, now Benchmark Capital (an early investor) is accusing him of fraud in an attempt to have him removed from the board of the company as well.

I realize I was just bragging a bit about the liberal arts cohort to which I belong, but we can’t deny the importance of science in the world around us.  This TED talk from Naomi Oreskes gives a historical view of why we should trust scientists.  Check it out.



Five for Friday: AI + Musk, Employee Productivity, Human Curiosity + More


Yea, well, it’s more than five for sure this week, and just like Blockchain, I’m auguring in on AI plus a few other topics, but here goes …

If you’ve never checked out Wait but Why, you should. Tim Urban does a great job of going deep on every topic he touches, and he does a good job at it.  This week I thought I’d share his dive into the AI Revolution as a primer for a few other articles that follow.  So, the first few in this list are Elon Musk related … apparently he thinks that one of the biggest threats to humanity is AI.  He recently told America’s governors that we need to regulate AI before it is too late.  Maybe he had read this piece about how AI is inventing languages humans can’t understand (obviously it’s not that linear or simple, but it’s somewhat funny, and the guy is wicked smaht), but to quote him “Until people see robots going down the street killing people, they don’t know how to react because it seems so ethereal,” he said. “AI is a rare case where I think we need to be proactive in regulation instead of reactive. Because I think by the time we are reactive in AI regulation, it’s too late.”  As Wired rightly points out, we need to worry about first things first with AI before we worry about the killer robots.  The flip side of this one is Parc CEO’s Tolga Kurtoglu’s belief that humans and AI will work together in almost every job.

Don’t take this as meaning it’s time to break out your Ouija boards and Tarot cards, but the U.S. Military believes that people have a sixth sense.  At least, that’s how they view the phenomena of premonition and intuition, which they spent four years and close to $4M researching.  While I’m not trying to comment on the cost of the research, it’s interesting to see the U.S. Military trying to understand these phenomena to the end of accelerate the spread of them throughout the military institution.  And incredulity aside, the article from Time is actually pretty interesting for the applications the military is looking at for this research.

Interested in how the next financial crisis may unfold?  Jim Mooney from Baustop Group says it is tied to two things: leverage and volatility.

So, there are two interesting articles this week that are related about how people do (or don’t do) the work they do: first, from Fast Company, is this piece about how our employee’s lack of productivity might be on us as managers/companies (hint: task switching), and then this piece from HuffPost about the potential big miss of coworking.

A quick side trip back to last week’s delve into digital: small nations and islands are winning the digital revolution race.

MIT is now offering a master’s program that doesn’t even require a high school degree.  How?  By letting students take rigorous courses online for credit and then, if they perform well on exams, place into master’s degree programs on campus.

The long read (from Longreads) this week is our last topic: human curiosity.  In an interview this week, astrophysicist Mario Livio is asked about what he discovered on his journey to understand what makes humans curious.  If you’ve got the time, it’s is in my opinion worth it, given the range of topics.

Two TED talks for you this week: first, from psychologist Adam Alter on why our screens are making us less happy and what to do about it, and second, this time from Tricia Wang, the human insights missing from big data, a talk in which she demystifies big data and identifies the pitfalls the lead big companies to make massive mistakes based on data.

Five for Friday: Blockchain, Digitization, and Disruption (of a sort)


So we had a pretty straightforward  (and sometimes grammatically incorrect) explanation of blockchain previously that outlined how the technology works and why it is important … well, sort of.  The importance of it doesn’t just lie in pulling the middleman out of the process, and it doesn’t just apply to financial services – a while back I threw out an article about how it was revolutionizing the shipping industry as well.  To follow on to last week’s share, this week I wanted to throw out a few other articles to keep moving us down the path: first is this piece from Infocast that outlines blockchain more technically (it’s from the Director of the IBM Blockchain Labs, so go figure –by the way, he has a series of posts about the topic through Infocast) and then this piece from HBR entitled “The Truth About Blockchain,” and finally, direct from IBM, guidelines for blockchain adoption in the enterprise … again, by Nitin Guar.

While I was perusing HBR this week, I also stumbled across an article from 2012 that I hadn’t seen before entitled “Data Scientist: The Sexiest Job of the 21st Century.”  Paired with that in the same issue was a piece about how Big Data was bringing about a management revolution.   I know we’re all already aware of those facts, but it’s still good to take a moment to go back five years later and revisit.  That somewhat sets up this piece from McKinsey out this week around competing in a world of sectors without borders.  IT’s not short, but it is interesting as it outlines how we might continue to see digitization impact and influence the world next.

Also in the news this week were some interesting bits from the world of higher education: first, a piece about MIT and Stanford researchers demoing 3D computing chips capable of both storage and computing, which will dramatically impact the current limitations around compute created by having to move data between chips.  A while back I posted an article about the death of Moore’s Law.  Well, this is the kind of evolution in computing that challenges that notion.  Then there was this piece about how Texas A&M landed a $1.6M grant to study algorithmic decision making.  OK, so what?  Well, first the grant came from DARPA, the folks who brought a few other technology revolutions over the past few decades, and second, because the intent is to lead to machine-based decisions that can also outline the “why” the machine reached the decision.  It’s one more step along that path towards artificial general intelligence I’ve outlined before.

Here’s a great one from Inc. about the twenty things most valued employees do every day.  Key for us is setting forth a vision for our people and then giving them the path towards how we achieve it.

Yes, it’s a tech heavy week, but that’s just sort of where things are in the news coming through my feed, at least with what is catching my eye. More so, it’s a disruptor-heavy week, and  along those lines I’d be remiss if I didn’t include this article about DoNotPay, a bot platform that is helping people get out of paying parking tickets that just released 1000 new bots to help with legal troubles … and gives you a taste of how technology could upend the legal profession.

There’s that great ad by Apple “Here’s to the Crazy Ones” which outlined how people who used and embraced Macs were different.  Apple’s culture revolves around the why they do what they do, not the what.  Many of us are familiar with Simon Sinek’s  book Start with Why, which is based on his TED talk from 2010, “How great leaders inspire action.”  Take some time this morning and watch his follow on, “Why good leaders make you feel safe.”


Five For Friday: Blue Apron and the Amazon Effect, Code, Digital Transformations + more


Well, it’ll continue through a few more news cycles due to the magnitude of it, so best to get it out of the way first … Blue Apron had it’s IPO this week.  Wait, what?  Not what you expected?  Well, here’s the two of that punch: it IPO’ed at $10 a share, 70% off the original forecast high, which analysts think was driven by … you guessed it, Amazon’s acquisition of Whole Foods.

From the NY Times this week was a great piece on how Tech firms have pushed coding into American classrooms … and, frankly, in classrooms around the world (and elsewhere in the world first).  And while it just turned two year’s old, Paul Ford’s article from Bloomberg entitled What is Code? continues to be a great read and refresher.  While we’re speaking of the world of tech, The Guardian had a piece about Technology startups being hostile to working mothers.  Google has also been in the news this week, but perhaps most interesting to me was this news that the search giant is now the “world’s largest job board.”  Interesting times lie ahead for all those job sites … cause Google will just inherently do it better given the data backing it up.

This might be useful amongst the ranks this week: how to interview engineers from Triplebyte.

Blockchain does continue to make waves, this week with the CEO of Nasdaq driving to modernize Wall Street with it.  And what delivered the best returns for the first half of 2017 to date?  Bitcoin and etheruem.

Last this week is an article from strategy+business about an emerging class of digital leaders that are being embraced by companies across the spectrum.  Digital transformation is going to be the focus for many over the next few years as companies attempt to manage/manipulate/make use of/gain insight from all the data being collected every second of every day … as others figure out how to collect data that is meaningful.  We’re seeing disruption not just come from companies that turn industries on their head, but by companies that are able to glean critical knowledge from the data at their fingertips.  If you’ve got time, I recommend two reads: Digital to the Core (from Gartner), which is at more of a strategic/overview level and Leading Digital, which connects the dots through from strategic to … well, not tactical implementation, but far enough down the path that you should be able to see the light at the end of the tunnel.

This week from TED is Anab Jain, who brings the future to life, creating experiences where people can touch, see and feel the potential of the world to be created. Catch a glimpse of possible futures in this eye-opening talk.

Five for Friday: Machine Learning, Cybersecurity Skill Gap, Snap Maps + more


First this week is an excellent look “under the hood” of machine learning from OReilly.  While I’ve written on this topic before, OReilly does well exploring some options around reference architecture.  Oh, and there’s this article about AI taking over transcription.  I’m unusually excited about that future.

There’s a trend out there I’ve mentioned before around “the death of staffing agencies” that hasn’t seen the traction/evolution that was predicted in 2016, however there is another trend that might actually help speed that up – the gig economy, in that as entrepreneurs/digitally innovative firms latch on to that notion, we’ll see more companies like Konsus come about.  Buying by work product created has been around for a little bit, but how Konsus does it is what’s interesting.

Speaking of digital firms and a topic from last week, Venture Beat has a good read this week on how digital organizations are facing a severe cybersecurity skill gap.

I think it’s expected that there’d be a follow on article about Amazon this week after last week’s Whole Foods announcement.  This one is a little different though, looking at the monopoly that is Amazon.  Oh, and fun fact, the Whole Foods purchase in effect paid for itself with the surge in Amazon’s stock price after it was announced.  I wonder how many people on Wall Street are longing for the days when PE ratios made sense.

Just to comment in passing, Snap launched Snap Maps this week.  Not a lot of detail that I want to dig into, just to note it and the fact that yet another company that doesn’t do mapping is getting into mapping and all the metadata that comes with … geolocation/geofencing seems to still be a thing.  There’s also this mega road trip across Europe if you’re open to some travel this summer/fall.  Oh, and Kalanicky resigned, which shouldn’t shock anyone given Holder’s report.

For your longer read this week, check out this from McKinsey about how cities can benefit from the future of mobility.

Last this week, check out this TED talk from Stanley McChrystal about how to build sense of purpose across people of many ages and skill sets.

Five for Friday: Wannacry, Attrition, Amazon + more

A photo by Kristopher Allison. unsplash.com/photos/6x90rJDo-WA

It’s been a few weeks since the WannaCry incident, and while that attack was shut down in a very novel way, it brought to the forefront again how adept hackers are becoming at leveraging our human weaknesses to penetrate networks en masse.  Two articles for you to consider as you start your week in this area: one from Business Insider where a malware researcher talks about the latest evolution in ransomeware, and then this article from strategy+business on how to resist future attacks.

Why do people choose to quit their jobs?  Well, Inc. thinks there’s one sentence that sums up the entire reason.  Then there’s the recent outcome of the culture investigation at Uber, a culture for which Bloomberg posits we’re all to blame.

Good news coming down from the Supreme Court when it comes to Patent Trolls, and the direct results could be a big win for innovation.  And speaking of innovation, how did America become so against it?

Here’s a question to consider: are you even aware of how Amazon is eating the world?  Yes?  No?  Maybe?  Zack Kanter has his own views on it and it makes for an interesting read.  Hint: you know that looming apocalypse in the world of retail and commercial real estate?  Yup, you can than Bezos for that. Especially with this news … every commerical real estate entity just woke up to a very bad morning.

Last is a great piece from The New Yorker, titled “How to Call B.S. on Big Data: A Practical Guide.  It’s short and to the point, and more of a general-life approach to data than anything technical, but I like it because it reminds us that any data can be manipulated to tell a story.  And yes, even machines can be racist – remember Tay?

I’ve recommended Tim Ferriss’s podcasts before, and this week he has a new TED talk where he discusses why we should define our fears instead of goals – check it out.

Now for Something a Little Different …


I’m establishing a new rhythm for these collections and with that have a need to try a new format with them as well.  This month’s collection is a broad range, and while where I can I’ll bucket certain topics together as I have in the past, there are over 100+ articles that I’ve found interesting enough to read since my last missive and that gets a bit of a challenge from a curation standpoint.  I’m also looking at creating more evergreen content for you similar to what I’ve thrown together on machine learning or artificial intelligence, but more on that another time.

First this week, please pay attention: there is an incredibly effective Gmail scam out there right now.  Go read up about it, but essentially, a sender who looks like a trusted contact, sends what looks like a pdf but is actually an image that will take you to a fake google login page and from there, your identity is history. There’s a similar ruse going on with Apple IDs, but there’s less press about it.

If you’ve not heard and you have an iPhone, go update to 10.3 – it’ll save you on average 3gb of space on your phone, although it does convert to a new file format that is troublesome for a few.  Oh, and Apple also acquired Workflow, which is an amazing automation app for iOS devices and they made it free – go check it out.

Ever wonder which country in the world has the happiest people?  No?  Well, ever wondered how the data is analyzed to determine who are the happiest people in the world?  If you said yes, you’re in luck.

Middle management may seem like a thankless job, but according to HBR, it’s also an exhausting one.  It has to do with the constant need to code switch as you deal with different levels of the hierarchy of an organization, and they’ve got some suggestions on how to lessen the toll from it.

Robots, robots everywhere!  Not, necessarily, to the extreme that we see in Asimov’s I, Robot ( the first person to recite the three laws to me gets a prize), but they are becoming more present in our day to day lives.  Heck, I even heard a story of how robots are being tested in D.C. to help “augment” the current food delivery process.  I say augment in quotes because that’s what the company rep in the article said when asked if the robots would replace people.  Well, one person who doesn’t seem to get how robots are going to impact our society in the near term is Steve Mnuchin.  In his mind, robots won’t be displacing U.S. jobs for at least 50 to 100 years.  Problem is, he’s wrong – robots have been replacing humans for a while now.

strategy+business had a good piece recently on the Ten Principles for Leading the Next Industrial Revolution.  I don’t think there’s anything that would surprise you in these principles, but it does put them together in a logical order and is worth exploring.  Ah, and they’ve also replaced “fail fast and often” with “innovate rapidly and openly.”

Deloitte also has an interesting exploration of how the auto industry is going to change in the near and mid-term, and how massive that transformation is going to be.  With more millennials opting out of car ownership and into a sharing economy, the automation of delivery vehicles that we’ll see culminate in the next five to ten years, and the looming death of a generation who have driven car sales most of their lifetime, the auto industry is on a precipice and they’ve done a good job of analyzing and detailing all the possible outcomes.  While many of us aren’t tied directly to the automotive industry, this in addition to the previous piece on the next industrial revolution should get you thinking differently about your own challenges.  By the way, one trend that you’ll find between them has to do with data.  Oh, and stratechery wrote on the same topic with greater brevity and a very different approach, but the same outcome – car ownership is going to change.  It already is.

Speaking of data, here’s something about how big data is helping find the Achilles heel of each individual cancer.

Well, there’s been a little bit of news this last week about how individual privacy on the internet is being betrayed by “235 stooges in Washington” to quote one news source, and while I think we’ve been giving up more and more of our privacy for a long time, if you are concerned about yours, check out this article from Kevin Mitnick on how to go invisible online.  By the way, at a minimum, you should be using a virtual private network (VPN) on your personal devices to keep your personal data from being stolen as you enjoy a coffee at your local Starbucks.  And yes, I know I’ve said this before.  Also, there’s this article from the Pew Research Center on what the public know about cybersecurity.  They even have an interactive quiz.  Also, take a look at what the future of passwords may hold.

Speaking of stolen identity, check out this story on a $30k sting operation one person pulled when hackers stole her website.

Time for a quick video break: this week Fast Company has an interesting (and short) one on how circular runways could lead to more efficient airports.

We’re in the midst of Spring Break season, and with that, Legoland Florida has launched an educational, road-trip friendly app for kids.  It seems pretty cool and is a good indicator of where how we’ll continue to see content and experiences evolve.

Tech will lead to new sub-prime crunch.  That’s a bold headline, even without the missing preposition, and TechCrunch makes the case that while in the past P2P lending rates in the subprime arena have been indicators of coming economic contraction (note:  the overheated economy and tightening labor pools is a more classic indicator), the gist is that more people are going to be pushed into a lower wage earner bucket with a continuing stagnation of salaries (which have been stagnant since the 80s compared to economic growth and corporate profits – just ask a real economist), and that will push the sub-prime market to continue to grow and with that growth, eventually blow up.

OK, so, I’ve written about AI before on many occasions, and with good reason as it is a topic that is getting a lot of press these days.  I took the time to try and explain the differences between narrow and general AI, and as well to keep us all up with how it is intersecting with machine learning.  What this article points out, however, is that that interest in AI and machine learning has created so much different data sets itself that it has started to skew the data and what is “real” about … data, much less the preponderance of actual fake news that is out there.  To quote ”this pairing of interest with ignorance has created a perfect storm for a misinformation epidemic. The outsize demand for stories about AI has created a tremendous opportunity for impostors to capture some piece of this market.”  Oh, and then there’s the latest about how AI will change everything … again.

Also speaking of data, here’s an interesting article about how Charity: Water is using it to connect donors to the people they are helping.

A fun article (maybe) that relates to the world of AI and algorithms: When Machines Go Rogue.  To wit, complex systems have lots of parts, and that means there are lots of ways they can fail.  Also note, however, that there are lots of redundancies for that reason.

Google has been in the news little of late, from a big headline standpoint, but one interesting read is their approach to creating the next Silicon Valley.  Oh, and then there was the demise of Google Fiber.

In case you’re interested, here’s a look at Goldman Sachs’ Annual Report.  It’s a treasure trove, as most annual reports are, at the direction the company is going … especially when you read between the lines.  While on the topic of Goldman, take a read as to why the firm is going on a buying binge for delinquent mortgages … again (2008 sound familiar?).

I’ve written a lot about how we need to change how we are recruiting and hiring women.  Here’s an article about the need to change our strategy around this, and for obvious reasons – we’ve seen a desire by millennials to change how we work, and how we work is, in fact, changing.  Let’s not hold women to a standard and antiquated version of the workplace when we’re willing to accommodate others.

Take a moment, if you will, and go look at Business Insider’s list of the most powerful female engineers in 2017 and what they are working on.

Thank the Boston Globe for this one: the biggest threat facing middle-aged men isn’t smoking or obesity … its loneliness.  I know it’s a little off-topic, but given the audience of these missives, I thought it relevant.

There’s been a lot of news lately about the new Amazon play into the grocery business, and how they plan to make it so that we not only ever have to wait in lines, but we never even have to talk to another person!  (Now do you see my reference to loneliness above?).  Well, the ‘zon is trying to break into that $800 billion market with a splash, what with their foray into physical stores after online has failed in that domain for them.  Read about the genesis of that journey here.  And speaking of Amazon, check out why ad agencies are so afraid of what Bezos might be planning next given the 60% jump in revenue from advertising last quarter.

So why are employees at Apple and Google more productive?  Is it the swanky digs?  The free lunches?  The compensation packages?  Nope, it’s what they do with their star performers and their internal development programs.  Note: companies that lack development programs will always play in the minor leagues.  Along with that is this article about why the best employees quit, even when they love their job.

I just like the title of this article (because it’s true): iteration is not design. The point, really, is that while iteration is a great design tool, it doesn’t create great design because it can’t innovate, solve usability problems, or create delight.

SXSW has come and gone for another year, and as always there was a flurry of “new” and “hot” technology this year mixed in with weird films and lots of bands.  According to Forbes, AI dominated the SXSW conversation this year; while CNET has a good round-up of everything that happened and WIRED claims that tech is finally trying to clean up the mess it made.

Uber has been in the news of late, and while I’m sure a lot of you have already seen many articles about all that has passed, there are a few I think are important because of what they mean for the future of the company: did Uber steal their driverless car tech from Google? (If they did, there’s a company big enough to take them down). Doubtful?  Check out this timeline.  Then there’s this piece from Pando about the economic evidence that shows that Uber isn’t as innovative as we all claim it is.  And Newsweek had something to say on the matter as well and The Verge asks if Uber can be saved from itself.  And while The Guardian states that every time we take an Uber we’re spreading its social poison, one of the most interesting tidbits to come out in my mind is that Uber has been using a fake app to get around legal blocks in certain markets.  You know what really annoys me, though?  The lack of the umlaut.  Seriously, is it that hard to have a stinking umlaut in your brand?

Seriously, not to make light of all the news that has come out, it’s clear that Silicon Valley has a “bro” problem, and Über epitomizes it, even with their recently released diversity report and bringing Arianna Huffington in to clean up their image.  That’s going to be tough given all that has passed.  This goes back to the fundamental question, mind you, of why is Silicon Valley is so awful to women.

This article does a good job of exploring why sexism/harassment/discrimination is such a rampant issue in Silicon Valley.

There’s a new project at Google where they are using facial recognition software to measure gender equality in films.

Would you spend $25bn to acquire 100m new customers?  Well, Mukesh Ambani did, with the goal of transforming the telecom market in India.  And transform it he did.

So, Warren Buffet sold basically his entire stake in Wal-Mart recently, and we’re seeing a continued downward spiral with brick and mortar retail stores as more and more shopping is done online.  So what’s next for the American Mall? Oh, and if that trend continues, there will be a collapse of commercial real estate in the next few years.

Viacom may be onto something in the VR space with its new VR experience, The Melody of Dust.

There’s a really interesting conversation with Chris Anderson on how and why we should close the loop on all the new and old systems that are out there and how ongoing innovation is making that possible.  It’s worth a read.

Last time around I spent some time talking about Moore’s Law and Quantum Computing (note to self: that may be a good future deep dive), but this time around there’s an article from Quartz about how two small changes may make your phone battery last forever, even if Moore’s Law won’t.  Or, better put, how the desire to have longer lasting devices that don’t catch on fire may finally force manufacturers to think differently about design.

Another interesting piece of telecom news came out recently: NYC is suing Verizon for failing to provide fiber broadband to all its households.

We all spend so much time in meetings, and while we can’t control how others run their meetings, we can certainly control how we run ours.  HBR has a great article on that point, and, in fact, it was refreshing to see their take as it reinforces how annoyed I get when I go to a meeting without a set agenda and clear purpose.

Let me follow that up with two articles from HBR on something completely different: blockchains, how safe they are, and how they’ll move beyond finance.  Along with that is this piece from the NY Times about how blockchain is a better way to track everything from pork chops to peanut butter.

Seeking Alpha did some analysis lately on Seagate and why the hardware provider will continue to face end market challenges.  I read it as “without innovation, Seagate will decline.”

It looks like the worlds of voice recognition, AI, and Alexa is being taken on by a small outfit in Japan, whose IM Line is working on a virtual assistant to topple Amazon and its market dominance.

Oh, and remember that little glitch a few weeks back with the internet because of an errant keystroke in an AWS data center?

Elon Musk has started investing (as have others) in how we turn people into cyborgs.  No, really.  The flip side of that is using humans to teach AI to “perform” smarter.

Something else AI might lead to?  The useless class.

I’ll tell you, there’s a lot of doom and gloom out there, like this old topic made new again: medical devices are the next security nightmare.  I say old because I’m pretty sure I talked about this around this time last year.

Snap recently had their IPO, and was it a day.  This piece from the New Yorker highlights the trouble with all the SV IPO optimism.

McKinsey has a really great study that’s just come out that is all about connecting talent with opportunity in the digital age.  It’s a good read, especially with some people predicting that Big Data will make human recruiters obsolescent as early as next year … which I think is a stretch.

Almost last this week is this article on LinkedIn that captures twelve lessons on leadership from the Navy SEALs.  Most salient: there is no such thing as a bad team, just bad leaders.

So last I’ll leave you with two TED talks: the first, Dan Bell taking us through the inside of America’s dead shopping malls and the second with Joy Buolamwini on how she is fighting bias in algorithms.

Artificial Intelligence, A Bank Will Fail in 2017, the End of Moore’s Law + more


It’s been a while since I’ve last thrown a collection of what’s piqued my curiosity together and shared it with the world, and boy has a lot happened since then.  I’d like to say that I’ve been keeping tabs and can pull from the best over the past few weeks, but, honestly, it’d be too much content to share.

First off, I have to start with a topic that has gotten quite a bit of news the past week or so: Uber.  Now, I know, the story of sexism and, frankly, border line assault has gotten a lot of press, but it’s indicative of a broader problem in and out of Silicon Valley in tech firms.  For those of you who’ve been under a rock, check out this article from recode or this one from Vanity Fair.  And while we’ve known for a while that Travis Kalanick is, for lack of a better term, an asshole, that too was captured recently, his stating that he needs to “grow up” as though that’s excuse enough for that behavior and the culture he has created at Uber is ludicrous.  If you’re thinking it’s not that big a deal, recode has another piece that might change your mind.  All that said, HBR has a great article this week on what Silicon VAlley firms could do to stop driving away female engineers.   Core to that is to stop making female engineers prove themselves over and over again while we excuse unacceptable behavior from men while expecting women to fit into a tightly defined box of what’s appropriate behavior.

Not shockingly, AI and Machine Learning are driving quite a bit of M&A activity, along with IoT technology.  This article from Forbes notes that AI will be at the center of most corporate deals in 2017.  While we’re on the topic, there’s also this piece from Techcrunch about how machine learning is going to accelerate even further with greater open source adoption and how AI research reached a tipping point precipitated by a combination of low-cost ultra-powerful computing, progress in algorithm design and access to large sources of data.  While AI has been the topic of many articles these past months and few years, MIT Technology Review notes that it is “the new black” with a seismic shift in both how businesses use artificial intelligence and how that impacts all of us.  The long and the short is we’re getting even closer to cognitive computing, which puts us further down the path towards artificial general intelligence I spoke about last spring.  Scientific American goes so far to question whether democracy can survive AI and big data.  As they state, artificial intelligence is no longer programmed line by line, but is now capable of learning, thereby continuously developing itself.  That begs the question, however, of if technology leaders are scared of artificial intelligence, shouldn’t we be?

Then there’s the flip side of Big Data and how it kills businesses because it’s, well, so big.

Heard of Cloudbleed?  It’s likely you have, but if not, here’s the scoop, oh, and before you read that, you may want to change your passwords … again.  Then there’s this article about how a chip flaw has exposed hundreds of thousands of devices.

From McKinsey somewhat recently is a dive into what Telcos need to do to grow in an increasingly digital world.  Facebook’s suggestion?  Just share the infrastructure.

Then there’s this interesting article about Kernel, the company trying to hack the human brain.  While neuroscience may be far from ready, it’s amazing how much more attainable those science fiction concepts of even a decade ago are today.

OK, so a quick break from technology for a second – there’s been a few things written of late on strategy and organizations.  First, there’s this one about how leaders don’t fear risk but instead turn it into a money-making strategy.  The next is how communities go about solving problems that matter.  Another is how strategy talk creates value, while this article captures how a technology company (Microsoft) turned itself around by not accepting the status quo.

What’s that about a bank failing in 2017?  Well, that’s exactly one of the predictions from BBC News in this article.  The expectation is that a successful cyber security attack on a bank in 2017 will erode the confidence in that (and possibly other) institution and lead to a run on it.  Want to know about one of the tools hackers use?  This article has a good look at how botnets are created and used – ignore the title of the article if you will, the overview is pretty concise.  Then there’s this one that asks whether today’s cryptography can survive quantum computing.

Interested in the state of the internet in 2017?   Well, we’ve got all your statistics for you here.

So there’s been a lot of news of late about the future of work (or not working, for that matter).  Bill Gates recently said that if a robot takes your job, that robot should be expected to pay taxes.    Gates also had a number of other things to say, but that one seems to have stuck out.  Then there’s JPMorgan’s software that now does in seconds what used to take lawyers 360,000 hours (yes, you read that right).  Then there’s this piece that explores how tech leaders think that universal income is going to be driven by the “automation” or skill replacement of so much of what we do.   While it’s not the 24th century yet, it’s interesting to consider.

So, a brief side-step to look at this piece that does an excellent job of demonstrating (visually, among other ways) the fundamental differences between Apple and Google and how they have fundamentally different innovation signatures.

Elon Musk, by the by, also thinks that we will all have to merge with machines to survive.  Yup. He sure does.

There’s another good read recently from McKinsey about how CIOs need to adopt an ecosystem view of business technology.  Speaking of, here’s an interesting bit of news: over half of the CIOs out there today don’t have a digital transformation strategy.

Pew Research Center has a dive into the pros and cons of the algorithm age, and then there’s this piece about how serial killers should fear a particular algorithm.

 While Netflix may be dominating over Amazon in the states for streaming content (due, in my opinion, to a really crappy interface), the tables are turned in India – read more to find out why, but fundamentally it comes down to understanding your market and how to price your product.

Would you believe the next big blue-collar job is going to be coding?  Let Wired explain why.

Last this week is a post by Rodney Brooks about the end of Moore’s Law, the law that has driven so many of our beliefs and understanding for fifty plus years.  As Brooks puts it, we’re to a point where we’ve gotten down to a single grain of sand, which we can’t then divide into more grains of sand.  Integrated circuitry has been reduced to such a small size that to reduce it further would, in essence, make it break down due to quantum effects.  That end, however, is going to finally force a major change in computer architecture because we won’t be constrained by a law that, while insightful, in the end has constrained us as well.  This will lead, as noted elsewhere, to technology like quantum computers, and better yet, technology we can’t even fathom because we’ve haven’t been forced to yet.

So, for a TED talk this week, I thought taking a back from the edge might be a bit appropriate.  Wanis Kabbaj is a self-proclaimed transportation geek who believes that traffic can flow through our cities in an effortless flow.  In his forward-thinking talk, preview exciting concepts like modular, detachable buses, flying taxis and networks of suspended magnetic pods that could help make the dream of a dynamic, driverless world into a reality.

Machine Learning


I have been absent in sending these the past few weeks and am changing topics, but bear with me, as I feel I’ve been remiss of late to not tackle the slew of machine learning articles that have been published.  As well, given that AI and machine learning are one of Gartner’s Top Ten for 2017, it seems time to revisit.  First, though, what is machine learning?

If you remember back, I did a dive into artificial intelligence last spring, mostly because I got overly curious myself.  Machine learning and artificial intelligence go hand in hand, and in fact machine learning is a subset of AI.  It provides computers with the ability to learn without being explicitly programmed and focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

The process of machine learning is similar to that of data mining; both systems search through data to look for patterns. However, instead of extracting data for human comprehension — as is the case in data mining applications — machine learning uses that data to detect patterns in data and adjust program actions accordingly.  That said, there are some who argue that machine learning is not a subset of AI, but the only kind of AI there is.  Perhaps we’ll evolve to that point, but there are still many examples of artificial general intelligence out there that don’t leverage machine learning that it’s still meaningful to distinguish between the two in my mind.

So why has machine learning gained so much momentum in the past few years?  Two factors stand out: data availability and computational power.

Today, the amount of digital data being generated is huge thanks to smart devices and Internet of Things (see previous posts). This data can be analyzed to make intelligent decisions based on patterns,  and Machine Learning helps to do exactly that.  As well, Moore’s law has ensured that the current hardware has the capability to reliably store and analyze the massive data and perform massive amount of computations in a reasonable amount of time. This allows us to build complex Machine Learning models with billions of parameters which was not possible a decade ago.

Machine learning evolved from the study of pattern recognition and explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions.  It is a method of data analysis that automates analytical model building, allowing computers to find hidden insights without being explicitly programmed where to look.  And it is everywhere.  For example:

Financial services:  Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud.

Government: Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.

Health care: Machine learning is a fast-growing trend in the health care industry, thanks to wearable devices and sensors that can use data to assess a patient’s health in real time.

Marketing and sales: Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history – and promote other items you’d be interested in. As well, it’s the place you most often experience machine learning in an obvious way and Amazon’s predictive engine is one of the best examples out there of how machine learning can enhance a consumer’s experience and has decades of data from millions of users to pull from.

Oil and gas: Finding new energy sources, analyzing minerals in the ground, predicting refinery sensor failure, or streamlining oil distribution to make it more efficient and cost-effective; the number of machine learning use cases for this industry is vast – and still expanding.

Transportation: Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations. As well, with autonomous vehicles coming more into the mainstream (you can get an autonomous Uber in San Francisco now), machine learning will have an even greater impact.  Let’s just hope we don’t end up with Johnny Cabs.

So, those are the obvious areas, and perhaps many of you are rolling your eyes at this point wondering when I’ll get to the good stuff.  If you can bear with me a little longer, we’ll get to the articles I mentioned … or you can always scroll to the end.

Some of the base level requirements for creating a good machine learning system include data preparation capabilities, the quality of algorithms – basic and advanced (duh), automation and iterative processes, scalability, and ensemble modeling.  Wait, what’s that last one?  It’s the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.  OK, so let’s clarify a few other terms that’ll help later on: in machine learning, a target is called a label, however in statistics, a target is called a dependent variable.  Right.  Great, not too confusing.  But wait, a variable in statistics is called a feature in machine learning and a transformation in statistics is called feature creation in machine learning.  Oh, and for those of you who’ve not had statistics in a while or never had it (or when you took it, it was a bit of a joke in your grad program), the Khan Academy is a great place to go get an overview, or you can try Stanford.  That doesn’t touch on fit, overfit, and underfit as it relates to machine learning.  There’s also a great visualization of machine learning from R2D3.

There are a variety of machine learning methods out there that are employed today: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

Unsupervised learning is used against data that has no historical labels. The system is not told the “right answer.” The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.

Semi-supervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person’s face on a web cam.

Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.

But that brings us to the topic of deep learning.  Wait, are we talking about a subset of a subset now?  Why, yes.  Yes we are.

Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.  Algorithmia has a great blog about why Deep Learning matters for a more detailed look, but the long and the short is that deep learning trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

So what are some of the emerging trends in machine learning?  Three came out of the  Machine Learning / Artificial Intelligence Summit last summer in Seattle: data flywheels, the algorithm economy, and cloud-hosted intelligence.

Digital data and cloud storage follow Moore’s law: the world’s data doubles every two years, while the cost of storing that data declines at roughly the same rate. This abundance of data enables more features, and better machine learning models to be created.  In the world of intelligent applications, data will be king, and the services that can generate the highest-quality data will have an unfair advantage from their data flywheel — more data leading to better models, leading to a better user experience, leading to more users, leading to more data.  Feel free to review the flywheel concept, but it’s an apt analogy here.

Next, all the data in the world isn’t very useful if you can’t leverage it. Algorithms are how you efficiently scale the manual management of business processes.  This creates an algorithm economy, where algorithm marketplaces function as the global meeting place for researchers, engineers, and organizations to create, share, and remix algorithmic intelligence at scale. As composable building blocks, algorithms can be stacked together to manipulate data, and extract key insights.  In the algorithm economy, state-of-the-art research is turned into functional, running code, and made available for others to use. The intelligent app stack illustrates the abstraction layers, which form the building blocks needed to create intelligent apps.

Last is cloud-hosted intelligence.  For a company to discover insights about their business, using algorithmic machine intelligence to iteratively learn from their data is the only scalable way. It’s historically been an expensive upfront investment with no guarantee of a significant return.  However, with more data becoming available, and the cost to store it dropping, machine learning is starting to move to the cloud, where a scalable web service is an API call away. Data scientists will no longer need to manage infrastructure or implement custom code. The systems will scale for them, generating new models on the fly, and delivering faster, more accurate results.

OK, I think that’s enough of a dive for our purposes today.  Now to the news:  first is a set of articles from Harvard Business Review that explore current trends.  These include the simple economics of machine intelligence, where HBR explores how machines learning will impact how prediction with regards to the production of goods and services and how it will impact the value of other inputs, then there’s what every manager should know about machine learning, and, last, how to make your company machine learning ready.

Alex Hern, from The Guardian, went so far as to give machine learning a go himself last summer.  You can read about his experience here.  Before we move on to other, still tech related news, I thought a few of you might be interested in how machine learning is impacting healthcare.  Forbes, HuffPost, VentureRadar, and MedCity News have their own takes on that topic.  Oh, and The Atlantic has a great article on searching for lost knowledge in the age of machines while Medium explores the public policy implications of AI and the New York Times Magazine has a long piece on “The Great AI awakening.”

I included Magic Leap in the round up a few weeks back, and already the luster is beginning to dull as The Verge notes that they are “way behind” in their VR device development.   The concept is stunning, the execution may be years away and is far behind Microsoft’s HoloLens.  Speaking of Microsoft, my former employer has rounded up 17 predictions from 17 researchers for 2017 and 2027.  Fast Company also recently published a brief history of the most important economic theory in tech, based on an HBR article by W. Brian Arthur back in 1996.  One of the best quotes form the interview is when Arthur was asked what kind of CEO can best take advantage of increasing returns that might exist: “You need an awareness of the ecology you are in. If you think of different firms and products as being different species, then you have to be very aware of how that entire network of different companies operates, even if they are quite peripheral to you.”

It seems fitting this week to revisit a TED talk by a buddy of mine from a few months ago: how computers are learning to be creative.  Blaise Aguera y Arcas is one of the more brilliant guys I’ve ever worked with, and to boot he is a renaissance musician and an avid evangelist for advancing technology.  In this talk, he discusses his work with deep neural networks for machine perception and distributed learning and shows how neural nets trained to recognize images can be run in reverse, to generate them.

Good to Great + more


This week, we continue our journey on building high performance teams, but first, some (mostly tech) news:

I was sent an article about Magic Leap by my brother a few weeks ago and the article’s title claims that the company is changing computing forever.  I first talked about Magic Leap a few months ago when I was covering the various virtual/augmented reality companies out there, but this article by Forbes goes quite a ways further in exploring this company than anything that was available at that time.  Magic Leap is getting a glut of venture capital money, and with good reason it seems, as their product, a next-generation interface for working with computers, could be what we use for decades to come.  Their new interface, in essence glasses, completely changes how we interface with our world by mixing virtual and augmented reality in a way that creates a mixed reality experience that overlays your real world experience by projecting imagery directly on your retina through an optics system built into semitransparent glass.   If you skip everything else this week, don’t skip this – this technology, if it comes to market, will change everything about how we interact with our world.

Speaking of change, Fortune this week had a great one on Satya Nadella over at Microsoft.  I know, I often write about my former employer, but I got to work with Satya while I was there, and while at first I had my own doubts about an insider being chosen to chart the new course for the company, he continues to impact the company, it’s culture, and it’s people in ways that are transforming them into the multi-national change agent they have the potential to be.

There are a few articles from Harvard Business Review worth checking out around Artificial Intelligence and teaching algorithms right from wrong.  Additionally, they discuss the competitive landscape of machine intelligence and how to make your company ready for machine learning.  Along with that is this video with Hilary Mason on the impact of AI technologies from O’Reilly media.  Last, this article from TechCrunch digs into how we define our relationship with early AI.

Strategy+business rightly digs into the shift in industry from products to programming, tracking how research and development dollars and the impact that early investment in software has on revenue growth.  It’s going to make for an interesting future state when artificial general intelligence creates greater access for all companies (and individuals) to create software in novel ways.

Another article from s+b this week I think is worth sharing: we’re told that smartphones are “bad for our health” and that of our employees, but the reality is our bad management is the root cause.  Just because we have the ability to be connected (to our people) 24/7, doesn’t mean we should be.  Here’s an interesting idea: that constant access to our people has an adverse impact on their productivity and engagement.  But  don’t take my word for it.  “Smartphones are not the problem. But the endless connectivity they provide means that work time is no longer finite. In many organizations, the result is that managerial incentives to use employees’ time effectively are at a new low.”

Now that next step on the high-performance team journey, in the form of a book report of sorts:

Good is the Enemy of Great

In his book Good to Great, Jim Collins hits it on the head by starting with the premise that by settling for good, many companies never position themselves for greatness and even if they have “great” leaders, when that leadership goes away since they were only good companies to begin with, the ability to sustain “good-ness” is difficult and many companies slide into mediocrity.  The intent of the book, however, is to identify what took certain businesses from good to great.

He places an emphasis on how a good company can be transformed to a great company through research of certain companies he considers great and comparison companied from similar sectors.  The performance of the companies was based on the performance of their stock versus both the stock market as a whole and the specific comparison companies.  Through their research, Collins’ team discovered the following insights:

  • Larger than life: Companies that were run by larger-than-life CEOs were much more likely to fail either while that CEO was in power or immediately after. Most of the celebrity-led companies show the positive correlation with taking good to good or mediocre.  Good to great takes a leader who is humble.
  • There is no linking between executive compensation and the process of going good to great
  • Both good to great and comparison companies  had strategic planning
  • Mergers and acquisition has no impact on the movement of a good to great company
  • The good to great company didn’t necessarily have people trained in managing change, motivation or creating alignment
  • There was also a lack of awareness of any official “launch event”  for the transformation – it was organic
  • It is not necessary for good to great companies to be at large or in an industry that is at an advantage due to innovation/newness – in fact, all of the Good to Great companies were established in the 1960s or earlier (mid-1800s for one)
  • Most of the good to great companies focus on what they should stop instead of what they should do
  • Technology was not cause for transformation in the good to great companies – many times, technological advances would happen after or along the way but were never the impetus

So what was the impetus for change?

Level 5 Leadership

Collins identifies that what he calls “Level 5 Leadership” was the driver for the transformation from good to great.  SO what is Level 5 Leadership?

Level 5 leaders do what they do for the success of the company, not individual recognition.  These leaders demonstrate humility and a professional will that reveals a fierce resolve to do what was best for the company, no matter how radical it might be – as Collins puts it, an unwavering resolve to do what must be done.

  • Level 5 leaders set their people up for success by putting a culture in place that supports succession
  • Level 5 leaders are quiet and dogged by nature, relentless in their pursuit of the core purpose they’ve identified for the company
  • Most companies have (and are successful with) Level 4 leaders, but they won’t realize the market outperformance of their competitors who have a Level 5 leader
  • They were more than just “clock builders”, they had unique characteristics such as humility and professional will towards excellence. This type of a leader is known for taking credit for bad performance while giving credit to others when things go well
  • Conceptually it’s a shift to think about the simple desire to produce sustainable results through a dedication to doing whatever it takes, big or small, to achieve greatness versus the quick fix mentality prevalent today


Professional Will Personal Humility
Creates superb results, a clear catalyst in the transition from good to great. Demonstrates a compelling modesty, shunning public adulation; never boastful.
Demonstrates an unwavering resolve to do whatever must be done to produce the best long-term results, no matter how difficult. Acts with quiet, calm determination; relies principally on inspired standards, not inspiring charisma, to motivate.
Sets the standard of building an enduring great company; will settle for nothing less. Channels ambition into the company, not the self; sets up successors for even greater success in the next generation.
Looks into the mirror, not out the window, to apportion responsibility for poor results, never blaming other people, external factors, or bad luck. Looks out the window, not in the mirror, to apportion credit for the success of the company – to other people, external factors, and good luck.


First Who, Then What

Collins talks about that, while people are important, it’s making sure you have the right people in the right roles that creates value.  In essence, people are not our most important asset – the right people in the right roles are.  As he says, you have to make sure not only that you have the right people on the bus, but that they are in the right seats.  Foundational to this is making sure we hire people whose personal values match our business values.  Whether someone is the right person has more to do with their character and abilities than what they know or have done.

  • Hire people with characteristics you cannot easily instill and focus on who you are paying
  • Analyze a potential employee’s character, work ethic, intelligence, and dedication to their values before deeply analyzing credentials and practical skills
  • A core difference between Level 5 and Level 4 leaders is that a 4 will look first at what they want to accomplish and then determine who they want to hire.  A 5 leader focuses on the who, including building a superior executive team, before considering what the path to greatness is

Great companies are a rigorous culture and if you don’t have what it takes to succeed, you won’t last long, but not in a ruthless manner.  This, to me, it a key differentiation that people should consider when they are looking for companies to work for. Most people simply look for a job, not something about which they can be passionate – this is a key difference about success.

  • When in doubt, don’t hire – keep looking
  • When you know you need to make a people change, act immediately
  • Don’t churn for the sake of churn, churn because you need to and churn better
    • At the same time, you may have someone who is a great fit to the culture but not their particular job – the responsibility of a leader is to get them in the right job/seat
    • Put your best people on your biggest opportunities, not your biggest problems

As Collins says,  “good-to-great management teams consist of people who debate vigorously in search of the best answers, yet who unify behind decisions, regardless of parochial interests.”  It needs to be a team that can confront brutal facts that face a company, but will never lose faith in the company.  Many time, we expect a quick fix to solve a problem, but breakthroughs occur because of a series of good decisions that are diligently executed on and accumulate one on top of the other.  As a leader, charisma can be a disadvantage because your subordinates will hide facts from you because they want you to like them or don’t want to disappoint you with bad news.  This isn’t the first time we’ve heard this, as Lencioni makes this point as well.

The key isn’t to motivate people, but to not de-motivate them – when you have the right people on the bus, they will be self-motivated.  To do so, we need to create a climate where the truth is spoken AND heard and brutal facts are confronted.  To that end, we lead with questions, not answers and have the humility to grasp that you don’t know everything nor do you understand enough to have all the answers so you have to ask questions that gets you to the right insights.

  • Engage in dialogue and debate, not coercion
    • You have to have a desire to have intense dialogue and rigorous debate – engage in a search to find the best answers by questioning a premise, not the value of people
    • Always keep it about the situation at hand, never about individuals
  • Conduct autopsies, without blame
    • As the leader, you are always in the end to blame for failures and you have to take responsibility for it but you also have to extract the maximum learning you can from the situation
    • With the right people on the bus, you’ll never need to assign blame but only search for true understanding and learning
  • Build red flag mechanisms that turn information into information that cannot be ignored
    • Good to great companies have no better access to information than any other company. They simply give their people and customers’ ample opportunities to provide unfiltered information and insight that can act as early warning for potentially deeper problems

Collins wraps all this into what he calls the Stockdale Paradox:

Retain faith that you will prevail in the end, regardless of the difficulties. AND at the same time Confront the most brutal facts of your current reality, whatever they might be.


The Stockdale paradox is named after Admiral James Stockdale who was held as a prisoner of war for eight years during the Vietnam War.  Stockdale was tortured and beaten during this ordeal and never had any reason to believe he would ever be able to leave, let alone see his wife again.  But through it all, he never lost faith.  “I never doubted not only that I would get out, but also I would prevail in the end and turn the experience into the defining event of my life, which, in retrospect, I would not trade.”

Here is the paradox:  While Stockdale had unbelievable faith that things would work out, he said that it was always the most optimistic of his fellow POW’s who actually were the ones who failed to make it out alive.  “They were the ones who said, ‘We’re going to be out by Christmas.’ And Christmas would come, and Christmas would go.  Then they’d say, ‘We’re going to be out by Easter.’ And Easter would come, and Easter would go. And then Thanksgiving, and then it would be Christmas again. And they died of a broken heart.”  By setting unrealistic, fantasy-based expectations, it was easier for them to give up, and in the end, many of them did.

The Hedgehog Concept (Simplicity within the Three Circles)

Next we explore the Hedgehog Concept, or, the “one big thing” for our organizations to understand and stick to. The question we need it as is what does or can your organization do, understand, or use as your core solution to competitive threats and changes in the industry?

  • The concept itself is similar to your core ideology (which never changes), differing only in the sense that it can be slightly less permanent.
  • Crucial distinction is that this isn’t a goal, strategy or intention – it is an understanding of what you can be the best at which is a crucial distinction
  • Your hedgehog concept must be something you are deeply passionate about, best at in the world, and are able to make a profit by doing.
  • Figure out what falls into all three of these categories, and obtain an understanding and strategy based on it.

Passion:  Good to great companies did not pick a course of action and then encourage their people to become passionate about their direction. Rather, those companies decide to do only those things that they could get passionate about. They recognized that passion cannot be manufactured nor can it be the end result of a motivation effort.

What you are best at:

  • Goes far beyond core competence.
  • Just because you possess a core competence doesn’t necessarily mean you are the best in the world at that competence.
  • Conversely, what you can be best in the world at might not even be something in which you are currently engaged.
  • The Hedgehog concept is not a goal or strategy to be the best at something, it is an understanding of what you can be the best at and almost equally important on what you cannot be the best at

What makes a profit:

  • Search for the one dominator that has the single greatest impact
  • Sometimes quite subtle, even unobvious but the key is to use the question of the denominator to gain understanding and insight into your economic model
  • Even if you can’t find a single denominator, the challenge of the question will drive you to much deeper insight which is the purpose of having the denominator in the first place – that it ultimately leads to more robust and sustainable economics

You have to be willing to “transcend the curse of competence” and avoid doing many things simply because you can but instead focus on that one piece that you can do best.  It’s an iterative process that is not the result of an individual but a council:

  • Exists to gain understanding about important issues facing the organization
  • Assembled and used by leading executive and consists of 5 – 7 people
  • Each member has ability to argue and debate in search of understanding but not from ego or to protect an interest
  • Respect is maintained without exception
  • Come from a range of perspectives but each member has deep knowledge about some aspect of organization
  • Includes key management team members but is not limited to that group nor is everyone from management team a part
  • Standing body, not an ad hoc committee assembled for a specific project – it has longevity
  • Rhythm of meeting determined by situation at hand
  • Does not seek consensus, recognizing that consensus decisions are typically at odds with intelligent decisions – in the end, the final decision rests with the leader
  • Informal body, not listed on any formal organizational chart or document
  • Usually have innocuous or benign names

The hedgehog concept is not a goal, strategy or intention; it is an understanding that good to great companies use to find their “one big thing” and stick to it.

A Culture of Discipline

Collins then explores  how we create a culture of discipline.  He makes the point that we need to hire people who are disciplined in their own right. The second you need to manage someone, you have made a hiring mistake and we should focus on managing systems, not people. Collins believes this is superior to managing people because:

  • When you have disciplined people, you do not need hierarchy.
  • When you have disciplined thought, you do not need bureaucracy.
  • When you have disciplined action, you do not need excessive controls.

To that end, we need to build a culture full of self-disciplined people who take disciplined action, fanatically consistent with the three circles and the hedgehog concept.  We need to give our people freedom and responsibility within a framework — build a consistent system with clear constraints, but give people freedom and responsibility within the framework of that system. When bureaucratic culture arises, it is to compensate for incompetence and lack of discipline, which arise from having the wrong people on the bus in the first place.  Therefore, we build a culture around the idea of freedom and responsibility, within a framework and then fill that culture with self-disciplined people who are willing to go to extreme lengths to fulfill their responsibilities. They will “rinse their cottage cheese.” That said, we must be careful not to confuse a culture of discipline with a leader who is a tyrannical disciplinarian.

All in all, G2G companies have the courage to say no to big opportunities that stray outside their three circles, even if it is a once-in-a-lifetime opportunity – in fact, the more G2G companies stayed within their three circles, the more they had opportunities for exponential growth

Technology Accelerators

So what about technology?  Collins notes that with Good to Great companies, technology is an accelerator of momentum, not a creator of it.  Great companies use it to execute better, but it won’t save a mediocre company.  They also avoid technology fads and bandwagons but they become pioneers in the application of carefully selected technology.  None of the G2G companies began their transformations with pioneering technology, however they became pioneers in the application of technology once they grasped how it fit with their three circles and after they hit breakthrough.

Great companies respond with thoughtfulness and creativity, driven by a compulsion to turn unrealized potential into results, mediocre companies react and lurch about motivated by fear of being left behind

Technology is never the source of either greatness or decline for a company, it all comes down to culture.

The Flywheel and the Doom Loop

This refers to the idea of momentum – keep pushing in one direction and you’ll build up the momentum needed to help you to overcome obstacles.  By using the flywheel as a metaphor, Collins stresses how momentum is built a little bit at a time – it’s not a dramatic, revolutionary change, but constant, diligent work over years.  While good to great transformation often looks like dramatic, revolutionary from the outside, internally they feel like organic, cumulative processes. The confusion of end outcomes (dramatic result) with

While good to great transformation often looks like dramatic, revolutionary from the outside, internally they feel like organic, cumulative processes. The confusion of end outcomes (dramatic result) with process (organic and cumulative) skews our perception of what really works over the long haul.  No matter how dramatic the end result, the good-to-great transformation never happened in one fell swoop. There was no single defining action, no grand program, no one killer innovation, no solitary lucky break, no miracle moment.  Sustainable transformations follow a predictable pattern of buildup and breakthrough. Like pushing on a giant, heavy flywheel, it takes a lot of effort to get the thing moving at all, but with persistent pushing in a consistent direction over a long period of time, the flywheel builds momentum, eventually hitting a point of breakthrough. Unfortunately, we’ve allowed the way transition looks from the outside to drive how we perceive

Like pushing on a giant, heavy flywheel, it takes a lot of effort to get the thing moving at all, but with persistent pushing in a consistent direction over a long period of time, the flywheel builds momentum, eventually hitting a point of breakthrough. Unfortunately, we’ve allowed the way transition looks from the outside to drive how we perceive change in a company when in reality the process is much slower and, in a sense, the result of a million turns of the flywheel as it builds up momentum.




When Collins looked at comparison companies, he noted they followed a different pattern, the doom loop.


Rather than accumulating momentum-turn by turn of the flywheel-they tried to skip buildup and jump immediately to breakthrough. Then, with disappointing results, they’d lurch back and forth, failing to maintain a consistent direction.  Comparison companies frequently tried to create a breakthrough with large, misguided acquisitions whereas G2G companies, in contrast, principally used large acquisitions after breakthrough, to accelerate momentum in an already fast-spinning flywheel.

  • In “doom loop” company, every time a new executive comes in, a new fad for management/vision/goals grinds the flywheel to a halt and results in doom loop. In contrast, with each new leader the flywheel keeps turning in G2G companies
  • G2G companies had same short-term pressures for returns on them as their competitors, however they had the patience an discipline to follow the buildup-breakthrough flywheel model despite this external pressure – similar to how Wooden took years to build a program that would eventually create a dynasty of winning for more than a decade at UCLA
  • G2G companies did have incredible commitment and alignment, but they didn’t think about it – they focused on the fundamentals and let the momentum over time do the rest – it was transparent to them and in that, came the commitment, alignment, motivation and change that just made problems melt away – they first learned how to put on their socks and built it from there


How to tell if you’re on the Flywheel or in the Doom Loop

Signs that you’re on the Flywheel Signs that you’re in the Doom Loop
Follow a pattern of buildup leading to breakthrough Skip buildup and jump right to breakthrough
Reach breakthrough by an accumulation of steps, one after another, turn by turn of the flywheel; feels like an organic evolutionary process Implement big programs, radical change efforts, dramatic revolutions; chronic restructuring – always looking for a miracle moment or new savior
Confront the brutal facts to see clearly what steps must be taken to build momentum Embrace fads and engage in management hoopla, rather than confront the brutal facts
Attain consistency with a clear Hedgehog Concept, resolutely staying within the three circles Demonstrate chronic inconsistency – lurching back and forth and straying far outside the three circles
Follow the pattern of disciplined people (“first who”), disciplined thought, disciplined action Jump right to action, without disciplined thought and without first getting the right people on the bus
Harness appropriate technologies to your Hedgehog Concept to accelerate momentum Run about like Chicken Little in reaction to technology change, fearful of being left behind
Make major acquisitions AFTER breakthrough (if at all) to accelerate momentum Make major acquisitions before breakthrough, in a doomed attempt to create momentum
Spend little energy trying to motivate or align people; the momentum of the flywheel is infectious Spend a lot of energy trying to align and motivate people, rallying them around new visions
Let the results do most of the talking Sell the future to compensate for a lack of results
Maintain consistency over time, each generation builds on the work of previous generations; the flywheel continues to build momentum Demonstrate inconsistency over time; each new leader brings a radical new path; the flywheel grinds to a halt, and the doom loop begins anew

In all of this, there is only one area I disagree with: Collins’s belief that the flywheel doing all the communicating you need (I exaggerate) – I think a key component of any leader’s role is to communicate even the simplest of goals again and again and showing the employees where along the track they are.   Why?  Well, because no matter how simple an idea, when you start to work on anything you can get so augured in you can only see the weeds, not even the trees or the forest.  It is critical that the leader bring everyone back up for air with regularity and also be a source of inspiration (not in an ego-based way) to the entire workforce.

Well, if you’ve made it this far, you’re likely expecting a TED video about some topic.  We’ll get to that in a moment, but first, contrary to everything else I’ve been writing, there’s a funny piece from a while back by Geekwire about the top ten reasons why Darth Vader was an amazing project manager.

Right, so in parting this week, I leave you with this TED talk by  Dan Ariely where he discusses what it is, in his mind, that motivates us to work.