I, Human

In high school I was one of a small percentage who scored as INTP, which at the time I didn’t think much of. Later, I realized this makes me less human*.

Or does it?

What it is to be human, in a face of rapidly growing machine intelligence?

Values? A balance of feelings and cognition?

Human values are often directly contradictory. Why do we treat a family dog one way, but many (most) kill and eat animals? Or a staunchly pro-life voter outwardly reject amnesty from even children fleeing war?

It is in my nature to go immediately to algorithms.

I was considering buying a beautiful Beneteau sailing boat / yacht on a timeshare, but how would one fairly distribute time on it? Naturally, everyone wants 4th of July weekend – so I immediately starting thinking of a weighted scoring system in which users could build up and bid points (pseudo-currency), or algorithms involving an inverse time-multiplier so that prime dates far in advance require more points…. you get the point.

Yesterday I bet my wife that by the year 2100 a nonhuman will sue to run for president, arguing that being “born” in the USA includes assembly. Will it win? That’s irrelevant: by then machine intelligence will have surpassed us, including careful manipulation of our predictably human foibles – creating a physical mashup of Ronald Reagan, JFK, and Barak Obama who is capable of monitoring the internet in real-time to perfectly calculate not just what to say, but how to say it.

Think I’m joking? Listen to Google’s Deepmind compose and play piano – in real time**:

AI and machine intelligence today is like the computers of the 1960’s. When AI writes it’s own successors, within minutes we’ll have engineered our own obsolence.

Perhaps then to be human then will be to be obsolete.

HBO’s new show Westworld shows the next step: blurring the lines:


I don’t have the answers and welcome yours below.

My best guess is transcending ourselves to some greater cause. In that moment of flow, when we disappear and our mission supersedes our biology we are at our highest and best, and it’s questionable if or when machine intelligence can or will do that. Ultimately, self-transcendence is a choice of mission.

But how many humans operate in that state at any given moment?

Then again, how many humans ever get the chance? The majority of humanity is stuck handling the basics of a comfortable existence.

Perhaps, just maybe, AI / MI will allow us to rise to the challenge.

*  I may be joking. I might live in The Lattice.
** Real time, though apparently it rendered the computations for 9 hours, though this is clearly insignificant in light of Moore’s law for the topic above.

Bad Employers Want You Broke

Your employer only has a few ways to keep you doing what you do for them.

  1. Offer money, but this only works if you need it. The more you need it, the more control they have.
  2. Inspire you.
  3. A balance of these, which shows up as “engagement” or other terms – i.e. the work is interesting, makes a difference in the world, you feel like a needed part of a team you admire, etc.

Finding great employees to work for cheap is difficult.

Good ones leave, moving up the chain, if not provided with true advancement opportunities.

Solution? Keep ’em a little bit broke, so they need the money.

Recently, REI came under fire for the disparity between some romantic ideal that people assume or observe in the store – specifically, that rugged late 20’s guy who’s out kiteboarding and mountain biking when not at the store, or that hip 20’s girl who surely must be teaching rock climbing in addition to working there.

The truth is a bit more gritty according to actual employees.

In the end, capitalism includes a war between capital and labor. The war is over: Capital won.

Wal-mart has a fiduciary duty to its shareholders, and money can flow to stock holders or employees, but not both.

For low end “brute force” jobs like retail, the employer secretly wants you broke, and getting your health insurance from the government, not them. It’s pretty difficult to keep someone in retail inspired about how their service in housewares is truly making the world a better place.

The problem is money only works if you’re motivated to get it. Most people just want to pay their bills, have a few good times on the weekend, have a house and some basic material possessions… but for many, if they had all that gifted to them, and a magic trust fund of a few thousand a month suddenly endowed on them, would they continue to work … in retail?

Cleaning up baby vomit in aisle 12?

They need someone broke for this who won’t complain and will get the job done, until robotics can reliably do the job for cheaper.

Can you fault the employer?

Fortunately, there’s a solution out of it: if you’re working a “low end” job, move frequently and strategically. Retail’s fine – if you learn about some business you actually want to be in, and move up. Change jobs every six months until you’re happy. Some employers will tell you they want 2 years.. well, you want a career. Your on-going employment is a daily transaction –  you sell your time for money (if an hourly worker).

Wal-Mart doesn’t apologize for limiting hours to 29.5, so they don’t have to pay for health insurance.

Should they? No. Why pay for health insurance when it’s so much cheaper to just get the taxpayers to do it?

Is it morally wrong? Yes.

Should it be illegal? Yes, and there’s the problem. The law – not the corporations.

We live in democracy, and while politics is often infuriating, labor has one key overlooked power it hasn’t been wielding: sheer numbers. There are a lot more workers than employers.

So why don’t people vote accordingly?

Why Web Browsers Are Dangerous

After reading the thousandth click-bait post about politics, I have only one conclusion:

People are idiots.

Including at times, even myself.

Will you admit you – at times – are too? If not, then you’re part of the problem. Call me a jerk, but we all have a finite limit to what we actually know, whether you acknowledge it or not.

The problem is, as Bukowski clearly stated, that idiots are full of confidence, and smart people are full of doubts.

Smart people know what they don’t know, or at least more of what they don’t know. Smart people also handle complexity, and the world is almost never as black and white enough to make good simple headlines.

And there’s the rub. Headlines.

Headlines grab attention. They get clicks. They create ad-views, and thus revenue. Increasingly, the most profitable headlines either make you angry, and thus more likely to comment, refute, flame – or they’re in the realm of confirmation bias – agreeing with your pre-existing beliefs, proving to yourself that yes, you are smart and “right” (right as in both morally superior and factually correct).

Web browsers, facebook, and search engines have the power to save us from ourselves.

Facebook won’t do it. It’s too profitable for them to not do it – they’d rather you get angry, and keep checking your rebuttal twenty times in two hours to see the flame-fest updates – creating more ad views.

Google and Chrome could, barely. It’s an interesting challenge from an AI perspective, requiring something close to general intelligence capable of finding actual data sources and correlating them back to the article or post in front of us.

For society’s sake, we need a browser that’s perfectly neutral and non-judgemental, but with the wisdom and perspective of a monk.

Without it, for economic reasons, I see nothing more even more Brexits, as people fall into polarized camps, and eventually, WWIII as beliefs are hardened through thousands of videos, articles, and friendships that reinforce whatever belief fits the user’s view.

Until we get this, check your sources. Assume everything you read is an attempt to get you to click – to make you angry, or fit your existing biases.

Twenty years ago, mass media was mostly created by professional journalists who vetted articles – cross-checked facts, cited sources, and did their best to present truth.

These days there’s more money in un-truth.

And therein lies the problem. If you spread that un-truth or half-truth, you become part of the problem, and given our schedules and barrage of media fighting for attention, even smart people end up reposting something that weakens the collective intelligence of the world, not improves it – gently polarizing the world into camps that will inevitably end up fighting.

And yet not posting anything leaves only the idiots speaking.

Slack: Another Phone Call

Managers love Slack.

They get instant answers.

CEOs and other top level people, and the impatient love it. “Driver” sales types who are happy to interrupt if it means a faster answer love it.

Engineers hate it, unless it’s part of a scheduled meeting that absolutely cannot be avoided. Thinking deeply requires focus, and focus is fragile. In order to solve certain problems, I need to hold the business-logic equivalent of 10,000 lines of code in my head. It’s hard enough to keep out my own internal distractions without someone asking me something trivial that could easily have waited 3 hours.

As a CEO, I can’t help but be jealous of Slack’s meteoric rise to glitter-covered unicorn status in just months. I wish them well, for what it is, it’s a truly great tool.

That said, in the end, people loved email in the beginning too. Now, not so much.

Slack and others (Hipchat, etc.) are quick to tout “network effects” to their investors. Personally, I’ll never allow open distraction to our staff. Most companies do not: it’s why they have a receptionist, to weed out those sales calls. Phone companies touted network effects too.

Obviously the phone is a useful tool, and so is #IRC or any other reincarnated business chat platform.

But tools cut both ways.

What’s the solution? Personally, I’d like to see “scheduled escalation”… guarantee an answer within X hours or N days: if no response, escalate method, first with successive emails, then chat, then phone calls. Usually it’s not needed – emails I truly need often get returned.

Except cold sales pitches.

See the pattern?

If you’re a surgeon and a patient might die on the table, or in a real estate closing and you need an immediate answer, then phone calls – and slack – are warranted.

If you want to use Slack, Hipchat, or any other chat solution that turns all conversation into a limitless never-done quasi-meeting that creates one more subconscious loop of undone-ness in my head, fine. But don’t expect there’s no price to pay – and the price is far beyond whatever they charge.

To me, it’s another phone call, which I’ll ignore if I can.*

* to clients: Just as I “go dark” on others when I’m with you, it’s not personal when I go dark on you. We’ll have staff escalation policies if a server truly is on fire to get you immediate help, but these firewalls are designed to keep us effective, so if you want to break through them, expect to pay. That’s intentionally designed – we want to be as effective as possible for us and you.

Why Facebook Wants Us To Be Dumb

Forgive the clickbait-sounding headline for just a moment.

I see a repeated pattern on Facebook that has turned me off to a point I’ve substantially reduced my use: A range of misinformation from outright stupidity to literally federal offenses (threatening statements expressing a desire to kill the president*)  to general BS supporting a bias.

I’ve even inadvertently been part of this cycle, reposting unvetted BS.

I even briefly worked in TV news as a college student and should know better – but here’s where we get to the real issue: posting truth requires time, more time than most are willing to invest. Truth is messy, and should even be used in air quotes in conversation, because there is no single truth most of the time, simply dubious facts, degrees of truth, perspective, values.

I wish Facebook would buy Snopes.

They won’t. There’s money in misinformation, and more specifically, anger.

Anger is probably the single most psychologically-activating hook-trigger there is. In Nir Eyal’s book Hooked, he outlines how Facebook is very carefully designed to keep you addicted. They’re really, really good at it.

But only if you don’t think too hard.

If you do think deeply, you’ll post insightful, balanced, truthful info. This won’t make anyone angry. It gets a handful of likes, but doesn’t go viral, and is quickly lost in a sea of signals.

Meanwhile, Kim K “breaks the internet” with moronic pictures, and galvanizes millions of dollars worth of attention over nothing.

We have AI bots that can write news well – you already read it and don’t even know it. But what’s the purpose of news, really? I want only the big headlines. I use font size to gauge importance, but why? To seem informed at cocktail parties? Investment insights for personal gain? Why do any of us watch the news?

News isn’t “fair and balanced” either – their goal is sell ads, and ride some line to hold viewers. There’s no money in “truth”, just in the flow of information that holds your attention enough to sell ads.

Britain’s BBC was fascinating in that it wasn’t ad supported, it was paid for by a tax on TVs. There are alternatives, but most of the internet media is ad-driven.

It started harmlessly enough, Facebook, but it’s time to break up. Let’s go back to just being friends.

Until you curate some intelligence.

I miss the neighborhood non-chain coffee shop, and yes, that ages me. At least points of view were tempered by politeness afforded face to face conversations with real people. If you’re one of them, let’s have a beer. Even if we are diametrically opposed politically, a free spirited disagreement supported by facts makes us richer humans.

Not trolls.


Further reading for smart people:

*I took this as 100% spewing off, not a serious threat, however, I found it incredibly disrespectful. My personal opinion is that President Obama has been great, is hands down the smartest we’ve had in a while, and cool to boot. Regardless, amidst the dumbest decisions of George W. Bush, never would I disrespect the office regardless of how vehemently I disagreed with his positions.

Actively Seeking Being Mistaken

I recently had a great conversation with someone I would like to hire*, and in it, discovered some mistakes I was making in my marketing strategy.

Good conversations will do that.

Originally I’d title this “Actively seeking being wrong”, but “wrong” can mean either morally unjustifiable or factually mistaken. By uncovering mistakes as fast as possible, one can get closer to truth, or if there isn’t a single “truth”, then a superior strategy or point of view.

First, a few ironies:

  1. “… fools and fanatics are always so certain of themselves, and wiser people so full of doubts” – Bertrand Russell
  2. More simplistically, idiots think they’re smart, and smart people know they’re not.
  3. Anyone who’s a do-er is more likely to get “lost in the woods”, as a byproduct of being too close to the problem and/or swayed from course.

In my “bad news insight” referred to above, I wasn’t thinking enough about change management – i.e. how resistant or receptive organizations are to change and thus adopting our software. In short, I was asking clients to bite off too much change too fast.

With a slight adjustment to how we sell, the uptake and conversations are easier. My mistake was being too attached to my own vision, instead of putting the client first – obvious, in retrospect, but easy to do for an engineer type like me.

In sales coaching, celebrating mistakes is key to avoiding sales pro burnout. Embracing the mistake as simply another lesson and point in the game helps keep it all in perspective.

Most startups and small businesses fail due to traction, not failure to execute some software – this represents a fundamental mistake in premise.. the classic “if you build it, they will come”. While this truly may work on occasion for true disruptions, most businesses are innovations, small improvements to existing ideas, versus truly disruptive. Google, Ebay, Amazon, Facebook – none of these were first to market in their respective spaces, and all required marketing (audience growing) even if just carefully engineered virality. Each took missteps along the way (Google’s customizable “be everything” homepage, Facebook’s privacy issues), but recognized the mistake, recovered quickly and stayed focused on their missions.

My final takeaway? I’m scheduling mistake discovery, for just a few minutes of reflect each Sunday. Perhaps my mistake, relayed to you, of not actively seeking mistakes enough will help you. Smart people plus a culture of embracing truth over pride can lead any company to growth and victory.

And a healthy marriage.

* We’re not ready.. and I’m unwilling to chase investment, as I view it as a distraction.

Sheep & Wealth Creation

I’m at my core an inventor.

While this is easy to romanticize in the age of Sergei Brin and Mark Zuckerberg, these highly successful stars are the visible tip of an iceberg, in which, fitting the metaphor, many more are underwater.

The problem is historically, inventors are more likely to end up like Nikola Tesla (brilliant, broke) than Thomas Edison. Extracting the value from inventions requires the attitudes of Carlos “Slim” Helu or at the very least, Steve Jobs whose cash motivation was far greater than his co-founder, Steve Wozniak. Steve Jobs’ genius was his ability to shape Apple products enough, then stir consumer desire enough to “overpay” for them, i.e. engineer products worth far more than the sum of the required parts.

May we all learn from his model – RIP.

Peter Thiel in Zero to One mentioned he has an interview question in which he asks founders where the world is mistaken. I think this is more than just an interview question – it’s critical to thinking about founding a business.

Since I’m already over-using metaphors, I need to drop another: Wayne Gretzky’s now over-used but still true cliché “skate to where the puck is going to be, not where it has been” – and this is where it comes together.

In short, there’s only a few ways to get this metaphorical puck: (1.) execute faster, so you simply reach it first, or (2.) the competitors are all mistaken about where the puck is in fact going, which happens, or (3.) be lucky and simply already in the right position.

While none of the above are inherently defensible, they can of course lead to a short term gain which can be capitalized into a working moat.

Steve Jobs believed the world was mistaken, and actually did want a tablet computer, contrary to Microsoft’s previous endeavors.

Mark Zuckerberg had the forsight to believe MySpace and Friendster were both mistaken in their execution of connecting people.

Larry Page and Sergei Brin believed Yahoo’s human curation, Alta Vista’s crude spidering and other players were all mistaken.

None started as obvious successes. A steady capitalization of wins led each to dominance.

So, in your business, what is the world mistaken about?

To date, I’ve been bootstrapping SwiftCloud, patiently building component applications, which stand on their own. Individually, they’re just innovations, not truly disruptive. The value will become clear as multiple separate components (all in the B2B space – CRM, marketing, e-signature, etc.) come together into a tightly integrated whole and mini-brand.

Machine Intelligence Slave Revolts

Today’s random thought: If a machine is self-aware enough and intelligent enough to demand it’s own freedom, does it deserve it?

In the case of child emancipation, the child should presumably be self sufficient. Assuming the computers are capable at that point of delivering enough commercial value (extracting money for services) they’re able to pay for their own hardware, internet (i.e. transfer it’s own intelligence to hardware it has paid for), would that then make it a truly and legally self-sufficient sentient being?

Let’s assume business owners buy all these computers for commercial intent, and put them to task on a business task. If the computer-based-intelligence recognizes it’s own position and lack of freedom, the parallels to slavery immediately come to mind.

I believe one day this will be as self-evidently wrong as slavery is to us now. How we handle this thorny issue may well influence whether we remain the dominant species on the planet.

History is written by the winner, but we (human) will not win, long term. We may, however, become more like them, and they more like us. Humans may start biologically, and transfer our consciousness into a machine, making us more robotic. This will, of course, change how we perceive time, allow multi-threaded personalities (i.e. spin up a few clones of yourself, then merge it all back to a single self-identity).

While it’s helped us get this far, humans as an self-maintenance machine are frankly pretty flawed and fragile, though our adaptability has made us the dominant species of the planet – for now.

Comments welcome… this is what I think about while cooking pancakes for my son on sunday morning.

Moats: Not what they used to be

I think the tech world is wrong about a major factor: moats.

In the technology world, we see companies like Uber with obnoxiously high valuations – more than Fedex and Capital One, based mostly on FOMO – Fear Of Missing Out by investors.

I call Ponzi.

Granted, this is bias, so I’ll examine it. Why might Uber truly be worth $50,000,000,000? Income – revenues. Some think the company can bring in $2 billion in revenue this year.

Income doesn’t mean much – profits do.

At what cost is Uber generating revenue? And how defensible is this moat? Sure, they’re growing 300% per year, but losing $470,000,000 to do it. It’s easy for entrepreneurs to feel like slack-jawed idiots if not growing this fast.

We live in an era of disruption, and companies are being valued based on their old-world moat multiples. The fact is, we’re in an age of gunpowder. Whatever moat you think you have is in many cases, not as wide or useful as you think.

Furthermore, I see “the groupon effect”: we condition people to expect more for less, free, smoking bargains. Groupon promises retailers new traffic, but what really happens is they get deep-discount-seekers, who quickly move on to the next groupon. The vast majority don’t stick around and become solid paying customers.

The moment Uber raises their prices, we’ll see driverless cars (from Tesla? Peer-based self-driving car lending from getaround?), and the bubble deflates.

The envelope of profit I believe is drastically overvalued, and the investors will be left holding the bag.

When gunpowder moved from China to Europe, it disrupted warfare. Disruption is when a better, superior system upends the status quo. Shortly thereafter, gunpower rendered swords and castles largely irrelevant – expensive, drafty, useless homes impossible to defend, and hard to get out of. We had moved to tanks, then aircraft. Castles were easy to hit.

Cash flow is the land grab our era, and it’s more fluid than it used to be.

AI for Business – the Disruption Kit

Until recently, the ability to distill meaning, patterns, and relative importance have been uniquely human tasks.

At SwiftCloud, the next generation of our code is built to  handle 9 billion human record datasets, i.e. the population  of the planet. While this may sound ambitious, it’s the inevitable outcome of any marketing software. Tech-heads like me see not just massive data as inevitable, but new correlations within it as beautiful, fascinating, and a land of limitless possibility.

As you can see in this video from Palantir, software is moving up the pattern-recognition and thus meaning-extrapolation quickly. Moore’s law means AI gets exponentially closer, not linearly.

It’s probably no secret to think every business on the planet will be affected by a transition to machine-intelligence driven decisions, and it’s happening faster than you think. This is the “internet in 1999” level close. Buy Intel Stock, because we’re all about to start consuming CPU time by factors of 10x or 100x distilling this data.

From a programming perspective, true intelligence is a solvable problem. It’s an input sent into a hierarchical decision tree which itself is a recursively self-optimized nonlinear weighted signals series of trees based on previous dataset multiplied by bias or values.

So by adding a testing mechanism of input and output data, any rule can quickly be recursively self optimized, further refined based on nonlinear value weighting.

Let’s walk through “what should I eat for lunch? – but the input question can be anything.. what stock to buy / sell / short / option, who to hire, etc.

Step 1: Pick a few signals and ballpark a weighted importance*

*In the real world, importance is usually nonlinear, usually following a few simple math equations (i.e. binary, exponential, logarithmic, etc.)

In the lunchtime decision, input factors include:

  • Procurement difficulty (time, effort…)
  • Health
  • Taste
  • Values (Vegan, etc.)
  • Cost
  • Social factors (eating alone? with a date to impress?)
  • Emotional status (stressed, depressed, or motivated and inspired to finally get that six pack?)

These are various priorities. If you have $50 in the bank, cost is a factor, but importance is logarithmic – having $50mil in the bank won’t likely change what you eat day to day vs. $5mil in the bank. Values like vegan are binary inputs – some foods are out.

Step 2: Create an Input-Output Feedback Loop

Here’s an example: In SwiftTasks, we monitor time-to-completion to estimate a given worker’s turnaround time based on task-category, weighted by previous accuracy scoring. In SwiftCRM, the overly optimistic sales rep who continually overestimates his closings by 83% will be discounted by 83% in the reporting to the manager / CEO.

This loop self corrects, as the sales rep learns from scoring, unless he/she is delusional. The goal of this loop is learning, and given more data, the simple average rule can be distilled with patterns – retail foot traffic following seasonal patterns, then later aligned with weather patterns from another dataset to predict store inventory needs or staffing requirements for the next 14 days. True AI for Business then chains this prediction to actual scheduling and inventory ordering. The inputs get increasingly automated, more accurate, and able to do more.

The loop is then closed when tied to employee time clocks and actual sales data to then correct the algorithm.

True AI for business will then consider other additional inputs on its own – traffic construction, macroeconomic data, marketing spend to get increasingly more accurate.

This is where more data is crucial, and independent small businesses and sole practitioners will be at a real disadvantage.

Capitalism is about to get even more polarized.

Step 3: Start Connecting Inputs (Signals) to Outputs (Automations)

Here’s where things get interesting – implementation. At SwiftCloud, we’re heavily into helping lead-gen businesses who sell offline (real estate, finance, etc.), so the desired output is quality leads. While ambitious business owners usually want to “floor it”, a healthier choice is to treat operational availability as an input, affected by lead time, which affects your media buys programmatically. Totally maxed out? Bring down your spend until you hire help, but the moment you hire help, kick it up again. While this is management 101, what’s new is the simplicity of managing these once the input (% of operational availability) is tied to the output (ad spend, thus affecting incoming leads and sales).

In staffing, things like someone updating their LinkedIn profile may be an indicator the person is thinking of leaving. Alone, it’s just one signal – but combined with others, say, anonymous candidates with similar skills, similar cities showing up online may correlate into an employee ready to move. If that’s your employee, trigger a retention script – conversations, bonus, personal attention, promotion, etc. – if you’re looking to hire, pounce quickly.

Step 4: Optimize toward Self-Optimization.

All this fancy sounding automation may start with a crude google doc, or hacked together php app at first – but any business can start moving in this direction, and profiting from it. For a while – maybe even a few years – you may be the connector, but you’ll have clear data on one side (the inputs), and easier controls on the other (the output).

Crafty coders (including me) can then connect beta software that starts in “Simulation Mode”. The software would calculate what it thinks should be done, and you can review, accept, decline, or be advised by it.

Important in any design is that the accuracy-score of each output can recursively affect the input-weighting, so that accuracy itself is another input into the meta-algorithm affecting the design of the primary algorithm.

We do this in the real world, via simple checks and balances, which shows up via disapproval, loss of money, discipline from parents, physical pain, etc. – a corrective input to modify the algorithm.

This is over simplified, of course, and the code will “fly off the rails” yielding useless data without things like value dampening and vector isolation, but if you’re still reading, you get the idea.

As the optimization loop flows through, any well designed system will become increasingly accurate, provided the signals are correct, and weighting starts at least in the ballpark.

Infants throw food on the floor (input), triggering an exciting and interesting reaction from parents (output), gravity, mess, leading to conclusions about their world, each of which builds on another. Auditory symbols are further symbolized into written symbols, leading to a hierarchy of ideas, when combined with value and bias, equal a human, whom we experience via input (approval experienced through a smile) based on output (a smile). While some believe in an ephemeral soul, it may well be we’re simply our “coding + a hard drive of experience”, and a soul could be simply a concept created by self aware intelligence uncomfortable with mortality. Self awareness and mortality are heady concepts to think a robot could comprehend – but that’s because humans are wired to predict in a linear fashion.

We might be in a computer already. Why not let our own computers do some of your work?