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In the Wide Lens, Dartmouth Entrepreneurship professor Ron Adner explores the risks associated with innovation. Execution risk is the obvious one. Then there’s co-innovation risk, what might be called chained technology risk. For example, to build a new ML focused microchip, a startup relies on the chip fabrication plant to develop 7nm equipment. But the most interesting of the three is Adoption Chain Risk.

Adoption Chain Risk is “the extent to which partners will need to adopt your innovation before end consumers have a chance to assess the full value proposition.” Adoption Chain Risk applies the idea of a supply chain to innovation.

In software, we’ve moved from a world where a customer buys a piece of software to run on their own infrastructure, to a world where a customer pays a vendor to run software on the vendor’s infrastructure. With machine learning, we may see another evolution of this. Machine learning startups create models based on data provided by customers. Should customers be compensated for their contribution?

Unlike the first wave of SaaS software, machine learning startups benefit from the data their customers share with them. Many times, machine learning startups create one global machine learning model that is used across the customer base. Each marginal customer provides additional data that refines the model.

Amazon’s acquisition of Whole Foods is notable for many reasons. Of course, there’s the magnitude $13.7B. The second is the shockwaves reverberating through the grocery industry. Costco fell 10% and Kroger almost 25% on the news. Third, the acquisition underscores the importance of physical retail even to the largest American ecommerce giant. Those are all remarkable in their own right.

However, the most interesting part of this acquisition is that it marks the current apotheosis of technology’s impact in the broader economy. In the last 18 months, non-traditional tech acquirers changed the M&A landscape for startups. Walmart, Unilever, GM, Ford spent billions of dollars collectively to acquire Jet, Bonobos, Dollar Shave Club, Cruise, and Chariot. That’s all fine and expected.

I’ve been reading Fred Kofman’s book, Conscious Business. Written in 2006, the book summarizes Kofman’s experiences as a management consultant to some of the great leaders in technology and other industries. In the book, Kofman lists 12 questions Gallup used to identify great managers in one of the largest management surveys conducted.

As I read this list of 12 questions, I started answering them for each of the different roles I’ve had. When I worked for great managers and answered the questions, I found I answered yes to almost all of them. The converse is also true.

Earlier this week, I spoke at 2U’s annual employee conference. Redpoint partnered with 2U at the Series A, and they are now a $2B publicly traded education company that powers online degree programs for Georgetown, USC, Syracuse, Berkeley, and Yale, among others. It was an inspirational moment for me because I observed the intense power of developing strong company culture.

When I analyzed the SaaS fundraising market in 2016, three trends emerged. The number of SaaS companies raising rounds had stalled, while the total number of dollars plateaued. Meanwhile, round sizes swelled. In other words, there was a concentration of capital in an increasingly small number of names. A year later, those trends have continued to converge, and SaaS valuations have resurged, reaching their highs of the 2014-2015 boom.

I am most grateful for my work experiences that were apprenticeships. Whether it was Philip who taught me how to write a proper Java function (10 lines or less), or Kim and Scott who are great managers, or the partners at Redpoint who invested a huge amount of time to educate me, those collections of experiences have taught me far more than I could’ve expected.

Worklife is nuanced. Reading a blog article here or in HBR article there, a business book or an academic paper only goes so far because these summaries of knowledge lack one key ingredient. Judgment.

The New Zealand All Blacks are the most successful athletic team perhaps of all time. A rugby outfit whose name originates from the solid black uniforms, they have won 79% of their international matches spanning 68 years. James Kerr followed the All Blacks, interviewed them and distilled his learnings into a book, Legacy. Kerr organizes the book into 15 life lessons, three of which stood out to me.

Humility is the first. After each match, the All Blacks, among the greatest rugby players in the world, sweep up after themselves. They clean up their locker room after all the celebration of a victory or the disappointment of defeat.

What could be more natural than a marketer selling a product to other marketers? Or an engineer pushing a new devops tool to other developers? Or a customer success person pitching CS tools? After all, they both speak the same language, come from the same domain, will develop trust quickly. Consequently, they will sell faster and more efficiently. This might seem like a very logical argument for differentiating on sales processes, but it’s a fallacy.

We’ve entered an era when computers can understand speech, computers can synthesize speech, computers can develop music, author encryption algorithms, create novel art, respond to customer support questions, and even generate new summaries and reports from data. Increasingly, humans will struggle to distinguish between computer-generated and human generated. Consequently, here’s an opportunity for startups to lead, not just technologically, but more broadly.

DT is a creative digital agency business based in Australia that developed a prototype that distinguishes between human speech and synthetic speech. Shaped like a hearing aid, the Anti AI AI chills the listener behind the ear using a Peltier device when the onboard TensorFlow model detects computer-generated speech. It’s one of undoubtedly many technologies which will use one machine learning model to detect another machine learning model.