Here’s my account of the seven remarkably well-curated sessions at Day 1 of Internet culture-maker festival XOXO in Southeast Portland, Oregon. 

This is my first year at the event, for which I had to apply to be granted a submission to a drawing to get a 24 hour window to pay $500 for a ticket, or some such process several months ago. It’s been here in my town for several years and this year my darling wife said “put it on the credit card and go if you can!” (Free video of all the following will be on YouTube soon though! Plus, see sketch notes.)

I’ve been working full time on the Internet 10 years next month and I saw a lot of faces I recognized – as well as many I did not. It’s a little overwhelming so I’m hiding in a dark corner of a feminist-alligned bar down the street, writing this on my phone plugged into the wall. (I’ll add more links later.)

XOXO is about people struggling to make careers for themselves making the Internet, and thus the world, better for themselves and humanity. It’s about art, science and commerce, as far as I can tell. I’ll go watch some short cartoons and films, maybe play some video and tabletop games, after I finish this.  It’s kinda Portlandia, but not screwing around. 

Founding era blogmother Dooce kicked it all off, with the words “hairy vaginas” in 400 point font and the statement “I am the motherfucking queen of the mommybloggers.”  She talked about the price of multimillion dollar success (exploitation of her crying children who didn’t want to be photographed in yet another faux-happy staged situation), her decision to walk away from The current state of sponsored content and back to authentic gritty life-affirming blog posts and photography. 
Then the NPR Planet Money guy talked about how his fame as a media creator opened doors for him but did not lead to fast VC funding for his startup. (Yup!) He had a great quote that summed up the XOXO ethos well: “You’ll never be able to explain the thing as well as you can show the thing.”  -Alex Blumberg https://gimletmedia.com/show/startup/
Then comic trailblazer and successful publishing company founder Spike Trotman  http://spikedrewthis.tumblr.com took the stage. She’s a young, Black, smart, high energy, an early adopter of Kickstarter who said: “I’d say I’m grateful, but I worked hard for this! It’s a combination of ‘how on earth did I get here?’ And ‘yeah, I worked hard for this!'”  
I’d like to read multiple blog posts just about Trotman’s presentation. The things I heard people say about it over lunch were remarkable.
Then, a non-panel-panel of the founding team of early smart-gossip blog Suck.com on one stage for the first time ever talked about how the sausage was made. I was proud to hear about a young Owen Thomas’s role there; Owen wasn’t here today because he’s rebuilding the blog my team and I built, ReadWrite. (Made me proud.)
Then Anil Dash pretended to be an egomaniac while actually shining light like an Olympian on women and people of color who have made the culture we live in.  “We’ve always been here,” I think he said, though Spike may have said that first. Dash showed off Makerba.se.

Zoey Quinn spoke about her lessons learned going from teenage depressed malleable jerk-face to the 2nd or 3rd worst-attacked target of the assholes of #GamerGate. She spoke about it from a game developer’s perspective, said anonymity is really important to protect and urged everyone to just be nicer! She was great.
Finally, CSS granddaddy Eric Meyer closed out the first day’s sessions with a simple nerd-for-good message:
The systems we design impact the things we do.  What we do online will appear normal to our children. System design is always social design. What kind of society do we want to design for?
That pretty much sums it up. I look forward to the evening’s events and tomorrow. 

I can’t imagine what could go further than today. Literally. 

Tonight I’m working on some thinking about future scenarios for my business, regarding a particular question (pricing).  I found myself thinking that there must be some good structured thinking tools for exploring questions like this in the foresight/futurist community.  Last week we made a big report on 125 top women futurists on the social web over at Little Bird.

I exported that report, grabbed all the home page URLs associated with these top futurist women, used them to populate a Google Custom Search engine and sure enough, found some awesome assets I could use. Quickly and easily.

Here’s that Search Engine: Women Futurists.  I thought I’d share it here, in the event that others are equally excited about leveraging the treasure trove of resources across all these wonderful websites.  I found this great page of resources for foresight exercises, for example.  But you could search for any topic and see what these people have written about it.  I love Custom Search Engines.  Bookmark and enjoy!

I’m watching futurist Gerd Leonhard‘s June 23rd 2015 talk in London titled Future of technology and-with-versus humanity: Future of Business (terrible audio quality, sorry) and something jumped out and caught my eye.  (Leonhard is one of the world’s most influential futurists, among top futurist peers like Andy Hines, Paul Higgins and John Smart.)

It was a statistic about the Automation of Knowledge Work, and it was on a slide referencing the May 2013 McKinsey Global Institute report titled Disruptive technologies: Advances that will transform life, business, and the global economy. There are 12 different types of technology discussed but automation of knowledge work is defined as “Intelligent software systems that can perform knowledge work tasks involving unstructured commands and subtle judgments.”

McKinsey said in 2013 that automation of knowledge work is going to have the one of the largest economic impacts around the world of any of the most disruptive technologies over the next 10 years, impacting the $9 trillion dollars that makes up 27% of global employment costs that go to knowledge workers.

Interestingly, at least as of 2 years ago, the hype-to-potential discrepancy that McKinsey saw was intense. Look at this chart:

That’s right, automation of knowledge work is expected to have one of the very highest economic impacts of all these disruptive technologies – but is the very-least discussed among general interest and business publications. What does that mean? I think it means “get in now,” for one thing.  High potential, low hype sounds like an opportunity for arbitrage against the future.

I bet if you redrew this chart two years later, for example, Internet of Things would have shot way up the Y-axis, as suddenly media and big companies have gotten very excited about it.   (For what it’s worth, I was writing extensively about IoT more than 5 years ago in 2010, this being my favorite post: an interview with Chetan Sharma about How 50 Billion Connected Devices Could Transform Brand Marketing & Everyday Life.)

As someone who’s at-root interested in building technology that automates knowledge work, I’m pretty interested to read McKinsey’s findings on its disruptive potential. (“This is not a market size,” the report emphasizes.)

That size of potential market to impact just below mobile internet and above (though they insist these aren’t rankings) cloud computing, robotics, and more. Higher total numbers are cited for the Internet of Things, which could impact $36 trillion in operating costs of healthcare, manufacturing and mining, and the $11 trillion in global manufacturing GDP that could be impacted by 3D printing.

McKinsey: “Advances in artificial intelligence, machine learning, and natural user interfaces (e.g., voice recognition) are making it possible to automate many knowledge worker tasks that have long been regarded as impossible or impractical for machines to perform. For instance, some computers can answer ‘unstructured’ questions (i.e., those posed in ordinary language, rather than precisely written as software queries), so employees or customers without specialized training can get information on their own. [Me: I’m especially interested in things like subjective judgement that used to be exclusively the domain of humans.] This opens up possibilities for sweeping change in how knowledge work is organized and performed. Sophisticated analytics tools can be used to augment the talents of highly skilled employees [Me: Now we’re talking! Let’s talk about things that weren’t even possible before!], and as more knowledge worker tasks can be done by machine, it is also possible that some types of jobs could become fully automated.”

Later, the report says: “Automated knowledge work tools will almost certainly extend the powers of many types of workers and help drive top-line improvements with innovations and better decision making.”

Again, as someone whose company is building technology used by enterprises for expert discovery, detection of emerging knowledge and related marketing opportunities, I’m excited about that.

But as a human being who is excited about building, hopefully sharing widely and personally experiencing augmented cognition, I’m even more excited.

PS. This reminds me of one of my favorite graphs around skills, from the OECD several years ago, demonstrating that the only skills that have grown instead of declined over the past 50 years are non-routine analytic and non-routine interactive. I don’t know how I feel about this politically, I certainly think that non-ambitious people most interested in doing routing work deserve to be able to support a family with dignity and freedom, not in poverty, but this is a pretty darned interesting graph.

“I want to meet that person face to face, in order to get an idea what their bandwidth is.” I’ll never forget the first time I heard someone say that about someone else in business.

As a first-time startup leader, I have a lot to learn. In order to do my ever-changing job as well as possible and learn at the same time, one of the (many) things I’ve been thinking about is expanding my personal bandwidth. Why? So I can avoid being overwhelmed, make fewer mistakes, catch problems earlier and be more effective. Among other benefits.

I’m a fan of James Altutcher’s How to Become an Idea Machine, where he challenges readers to come up with 10 ideas every day. “People say ideas are a dime a dozen and that execution is everything. Is this true?” he asks. “No. Ideas are a dime for 3. A dozen ideas are hard. Try it.”

So the other day, I made as my daily list of 10 ideas, “10 ways I could increase my personal bandwidth.” Specifically, I made that list in June and this weekend I’m going over my notes from June (I give each month its own Evernote file), and doing the Discern and Assimilate parts of how Chuck Frey says we learn: Gather info, Discern which of it is worth keeping, Assimilate it into our worldview and Use it.

The other thing I like is Scott Young’s Feynman Technique for learning. (If you visit that page, please forgive Young’s persistent hucksterism for his e-learning course. It’s really obnoxious but his free resources are really valuable if you can tolerate that.) The Feynman Technique is this: take something hard that you’re aiming to learn and present it out loud, like you’re trying to teach it. Pay particular attention to the relationship between parts. Do this without looking at your notes, until you need to. Note the places where your understanding fails you – because that’s what you’ve got still to learn. As an exercise, it’s effective. A good example of Active Recall, another part of what Young recommends.

It’s been 3 weeks now since I made my list of 10 Ways I Can Increase My Personal Bandwidth, and in reviewing my notes I’ve now talked through them out loud a couple of times. I thought I’d post them here too.

Tell me what you think. What preamble!

1. Read more, to reduce Unknown Unknowns. I’m reading The Essential Peter Drucker right now and learning about a bunch of questions in business that I had no knowledge of before. Drucker’s recommended answers to those questions are good – but being familiar with the questions alone feels like a big boost to bandwidth. One of the ways I’m making my online reading more efficient is to bookmark things in a to-read app, then send each link to a virtual assistant with a request to email me back the first paragraph and 3 bullet points with key facts or arguments from the article. I can scan 5 or 10 summaries pretty quickly each morning.
2. Learn from experience, including by taking notes. The definition of Learning, in one dictionary at least, is “the acquisition of knowledge or skills, through experience, study or being taught.” I love that. So much of it flows together in practice, too. So I try to take, and revisit, a lot of notes. One thing I’ve been trying to do more and more of is this: when I come up with a new or tricky question about how to do my job, I open up Evernote, type out the question and then just write out my gut-level working answer. Based on previous thinking, just spit it out. Then, I set a reminder on the doc (only available on mobile app? I can’t find it on desktop) to revisit the question in 1 week. A lot happens in a week, so I come back with fresh eyes and see if I still like the answer I put down. I very often do, which is encouraging in the “trust your gut” department. If appropriate, I edit the doc with more perspective or info. Then, I change the alert to come back in 30 days and then in 1 year. One year might be too long, but that’s what I’ve been doing so far.
3. Leverage mentors. I’ve got a number of friends or advisors who have been startup CEOs or business leaders otherwise and whose worldviews I really like. Talking with them is like an IV infusion of new thinking, experience and perspective. Except I look at it a little more critically than I might an IV. 🙂
4. Delegate.
5. Plan ahead. For myself and for those I would delegate to, the sooner you decide to turn the car, the less abrupt and disruptive the turn ends up being, right?
6. Business rules. Make decisions ahead of time, in the abstract, like if/then statements. Don’t re-invent the wheel or make every decision anew every time. I haven’t really done this one much.
7. Make fast decisions.
8. Make a list and pick 6. Ram Charan said in an HBR interview in 2013 that the best CEOs “take in a lot of information from many sources and then crystalize a point of view. They sort and sift the information and select the handful of factors that matter most – usually no more than six – from the myriad possibilities. That’s what they’ll base their decisions on. They cut through the complexity to get to the heart of the matter, without getting superficial. And they do it without losing sight of the customer.” I bought that issue of HBR at an airport, for $20, and read that on the plane. It’s been on my bookshelf for the past 2 years and I’m glad I was able to grab it to type it in here tonight. I’ve been using that method ever since, often to make really fast decisions when there is a lot of complexity at issue.
9. Pick your battles.
10. Prioritize.

Done! That’s 10.

Readers, what have you done to expand your personal bandwidth?

Here’s a great suggestion over on Twitter:

.@marshallk i would add, and @jaltucher would probably agree, make sure meditation is on the list. it's all about awareness

— gregorylent (@gregorylent) July 12, 2015

“If self-driving cars cut the roughly 40,000 annual US traffic fatalities in half, the car makers might get not 20,000 thank-you notes, but 20,000 lawsuits.” –A survey of research questions for robust and beneficial AI, Future of Life Institute (But at what point is the algorithm subject to double jeopardy and no longer subject to new lawsuits?)

After the equivalent of 75 human years of practice, in which it no doubt paid better attention to learning than a 16 year old human would, Google’s self-driving cars will now officially and openly hit public roads in California, the company announced today.

The key words in the announcement: “We’re looking forward to learning how the community perceives and interacts with the vehicles…” That reads to me like “we really hope people don’t react to these the way they did to Google Glass.”

I hope that the backlash against robots isn’t too severe. I don’t want to treat them like a technological inevitability which humans have no ability to resist, but…

The public’s reaction to the Google Car will likely act as a general referendum on the future of artificial intelligence and robotics. So far, looking around at Twitter replies posted to major media outlets covering today’s news, sentiment seems very mixed.

In a big-picture sense, I think of my company Little Bird as an autonomous learning machine. Today it’s for enterprise marketers doing research about market influencers, trends and intent. Long term I hope to work on self-improving systems for augmenting human learning in general, through discovery and filtering.

Thus I have an interest in how the public reacts to self-driving cars as a human myself, and as a person who wants to continue to build good machines. Maybe I shouldn’t associate what we’re doing with all of that, though.

Here’s an interesting one. “Machine ethics: How should an autonomous vehicle trade off, say, a small probability of injury to a human against the near-certainty of a large material cost? How should lawyers, ethicists, and policymakers engage the public on these issues? Should such trade-offs be the subject of national standards?”

It was convenient when we couldn’t blame an overwhelmed human for which choice they made under duress, but it’s all going to be rationalized by machines that aren’t overwhelmed.

That’s from the Future of Life organization linked-to above, which is full of technologists building artificial intelligence but also working to make sure it doesn’t result in substantially adverse consequences for humanity. It’s a pretty awesome organization; it’s the one Elon Musk made a big donation to earlier this year.

I don’t know how to wrap this all up thematically, these are just some thoughts thrown together. I think we need to think about autonomous vehicles from an ethical perspective, from a human evolution perspective, regarding city planning and ecology, regarding class, race and privilege. (Has anyone written about that yet? In science fiction, at length I’m sure.) This is an entire field of study that’s coming on really fast. I just thought I’d wade into the conversation.

I just got asked to contribute a story about an “aha moment” I’ve had in tech and this is what I’m submitting. I thought I’d share it here too, as I’m sure there won’t be much overlap. I’ve got a bunch of stories like this and they inform the creation of our startup, Little Bird.

When you’re raking leaves, the autumn can feel like a great time for introspection. Once, while working in my yard years ago, I found myself thinking about the internet. Specifically, I was thinking “what is it that I do on the internet that helps me learn about things before other people do – and are there other examples of the same kind of approach that I could be taking but am not yet?”

I was a tech blogger, at ReadWriteWeb, and I specialized in using tools and data to break news stories. That was my job, to find out things as early as possible.

That day is when I realized: I like to think about fields of data that are available online and treat them like hammers. They say when all you’ve got is a hammer, then everything looks like a nail. I like to frame that in the positive and say that when you get a new kind of hammer, there are sometimes a whole new set of nails you can discover.

I used RSS feeds, Tweets, every field available in Delicious, but was there more? That’s when I realized that I wasn’t doing anything with blog comments! Blog comments were structured, publicly accessible, tied to people and timely. So I thought of a way I could leverage blog comments to learn things early.

Here’s what I did: I took Robert Scoble’s Most Influential in Tech list on Twitter and I scraped all the home page URLs off of the bios there. Scoble knew a bunch of Silicon Valley people I didn’t know. I grabbed those URLs and I took them to a service called BackType (since acquired by Twitter and shut down). BackType would take any URL and scour all the comments fields in blogs around the web, and return any new comments where that URL appeared in the URL field of the comment, delivered to you by RSS. So I created a whole OPML file of RSS feeds of comments posted anywhere by the 500 most influential people in technology, according to Robert Scoble. Then I took all those RSS feeds and I plugged them into an RSS to IM real-time notification system. And I was able to break several news stories that way: an important engineer would post a comment on some obscure blog asking about help for a secret forthcoming project and I would get a real-time notification of the comment. So then I’d go report on the otherwise secret forthcoming project. It was pretty awesome and I never told any of the people I found info from how I found out about their news, except for once at 4am in a pizza line at SXSW.

That was the day I realized that the social web is full of various fields of structured data that can be mined and monitored to learn important things, intentionally and strategically.

Now I’m the CEO of a company that does similar but gentler things: it uses data to point marketers to people they should listen to and engage with!

Marshall Kirkpatrick is a former blogger, the first hired writer at TechCrunch and long-time co-editor of ReadWriteWeb, and is now CEO of Little Bird, a company that turns social data into competitive advantage for enterprise marketers.