Filter Bubbles vs The Daily Randomizer

I guess we’re all in our own little bubbles. The abilities of technology nowadays allows us to craft, select and deselect what and from where we receive our information about the world. Of course this may be a bit dangerous. It can create little echo-chambers of our own thinking where the only new ideas we already like are siphoned in.  If that’s not bad enough, it also hardens the ‘us-vs-them’ mentality in an already divisive time.

Wouldn’t it be interesting if we could see how the rest of our world thinks about things? If we could, would it surprise us if we were to glimpse into those other guy’s bubbles and find out they’re not so crazy? Maybe even find out on just a few points we might have a commonality of opinion?

Thinking along those lines, I thought it to be rather interesting to reach into a number of different news sites to grab stories, but present them in as impartial environment as possible – no logos, no taglines, no talking heads or personalities – just batches of news stories. That’s what I’ve done with my Daily Randomizer site.

The site culls batches of stories from a variety of sources (and as balanced as I could with the technology I’m using for this.) It presents five different sections: Top News, Business, Politics, Entertainment and Sports. The hook here is that you can see the news article titles but unless you click on the link, you’ll never know where they’re from! It’s random, and it refreshes every 30 seconds or so.

In my little part to help build bridges between everyone in this crazy world I ask you to give the site a try, keep an open mind and check out the site.

Jeff Foxworthy, Machine Learning and Changing Your Mind

Foxworthy Hal

Right now, I should probably be writing that promised article on what makes a good advertisement, but instead let’s talk about the magic of Machine Learning and people.

We’ve all heard the hip, new cool thing is machine learning. It’s PyTorch or Keras or whatever other flavor that’s out there. I got taken up with it, too. Heck, I even have a certificate around here that says I survived learning how to use Tensorflow. All this means is that I’ve peered behind the curtain a bit to get a glimpse of the little man at the controls.

It is a wonderful technology, to be sure. It allows a great many conveniences that will or have already lead to some pretty amazing insights. Because what it does is essentially multivariate regression analysis – but at a massive scale. That means it basically line-fits large sets of data so that it can determine the statistical probability of similarity to other things – not just the one or two variables Excel can do.

I’m sure right now you’re thinking that was an un-welcomed digression that’s left an accounting term-like mental malaise in its wake. What’s the point? Well the point is this: While the tech is good at boiling down complex data sets to provide you with, say movie choices that you may like based on what you’ve already liked, I don’t think anyone has factored in people changing their tastes from time to time – or the fact that while there may be a large number of people looking at things, there’s only a small amount of things they can look at.

Examples could found in a variety of places, perhaps the most memorable for all of us digitally plugged in would be either online dating, shopping recommendations from online retailers or streaming services like Netflix. By now, I’m betting you have had an experience with at least one of these. That means we’ve all experienced the pain of only getting exactly the things we’ve liked in the past. The trouble is what I liked to watch five years ago may not be what I want to watch now. Or, it has finally dawned on me that the type of person I was into back then is not the best choice going forward, but technology has doomed me to stay in the same dating pool.

When will these machine learning things figure that out? I think we all feel like they never will.

Why? Because the very large data sets that give machine learning tech their hyper-focused funneling power is the very same massive data set that truly has to turn over before our new tastes are included – and that’s only if the programmers of these systems have put in place code that allows for this to happen.



If you look at the machine learning math (and I won’t open that Pandora’s Box), it’s easy to see  watching only one Tom Segura stand up special has no chance of offsetting those ten Jeff Foxworthy shows your uncle watched on your account. Of course, that’s IF they built the software to recheck your preferences from time to time.

To really simple down the math (and yes the math is much more complex than this, Mr. Data Scientist, but it’s an analogy for everyone else), the effects are similar to every time Foxworthy spends 30min explaining what a redneck is on your Netflix account the software multiplies the Foxworthy counter by ten. The same would happen for watching Tom.  So in the easy math world it looks like this:

One viewing of Jeff: Jeff * 10 = 10Jeff

One viewing of Tom: Tom * 10 = 10Tom

Things get bad when that scales – as in your uncle just watched 10 Foxworthy specials on your Netflix account:

10 viewings of Jeff = 10Jeff^10 =10000000000Jeff

Compare this to one 10Tom and one viewing of something else isn’t going to change what the machine learning system is thinking you like, but what more important is that there isn’t 10 Tom Segura specials, there’s only like five.

Five Tom specials = 10Tom^5 = 100000Tom

10000000000 > 100000

The exponential math here means that there’s really no way watching less than the same amount of something else is going to offset the binge of a previous show, genre, actor or however else they’ve segmented their content.

Sorry, you’re doomed to finding out (over and over again) refrigerators on your porch probably makes you a redneck -unless Segura gets off his ass and makes five more specials.

Of course we’re all really in for it if they’ve also added in counting the frequency between views of the same materials. If that’s the case, binge watching is particularly dangerous.

All of this content railroading is compounded by these services continuing to only show you selections that fit with what you liked previously. There seems not to be any sort of random content display to break you out of your filter bubble. This makes you have to independently locate different selections – which is hard because how would you search for something when you don’t know what it is?

I’m hoping that eventually the powers that be will figure this out, because, while I really like Korean action movies (and you should check them out, too), I’d like to have the opportunity to see Thor: Ragnarok without having to spell all that out with the ridiculous Roku remote every time.


If Electric Bikes are its Future, is the Harley Davidson Brand a Help or a Hindrance?


Initially, I started writing this article from a perspective of the decision making that would go into how Harley would brand their new electric vehicle lines. The longer I worked on it, the more I realized that the real question is does it even matter?

From a product standpoint, going from the current Harley bikes and accessories to electric motorcycles and e-bikes is truly a massive departure from its core. Sure, both have two wheels, stalk the roads and require helmets (or should), but beyond that, they’re two completely different beasts.

The current Harley offering is more of a lifestyle built around an amazingly large, expensive machine that fights tooth and nail to stay rooted in the past – and certainly not the pinnacle of efficiency, either. The electric bike world is, for the most part, exactly opposite.

harley riders

So too are the target demographics for each product group. For the current rider, it’s the sigil of open-road freedom and luxury that only a large amount of disposable income can easily afford. It’s also something of a recreational item with only the thinnest of connections to utility.

Conversely, what today’s electric bike market looks like is based in the desire for cost-effective and convenient [city] transportation. It becomes even more true when Harley starts looking outside of its core US market. Further, those that will be in the market for e-bikes and electric motorcycles will be of younger generations that have proven they don’t need a vehicle to help support their idealized selves – or one to connect them to new people and experiences. They have the mobile devices and social media for that.

So for all the effort Harley has put into building this current incarnation of the brand, it would appear to completely stand in the way of how they need to be perceived for the proposed pivot into these new markets. I’d further posit that it’s so far apart that you couldn’t even marginally connect the current brand to a new one like, ‘YYY …by Harley Davidson.’ Might as well try saying, “The Future, by Old-timey.” in that ‘Kitchen of the Future’ voice from the ‘50s.

An electric motorcycle-focused brand from the firm would have to be completely different than what they have today to the point that it would need to be essentially a completely different company. The question becomes: does Harley have the gumption to do it well?

The Calculus of Ordering Stuff Through Alexa.
Amazing! An image that exactly fits with my article. Thanks GeekWire!

It’s recently come out that the vast ownership of Alexas pretty much don’t use them for shopping, even though this was Amazon’s primary reason for launching the product. Having watched and read a number of people postulating that this voice-driven platform will be the next far reaching commerce platform, I have to say that I’ve never really seen how Alexa-like devices were going to command so much sales.

Thinking about it more indepth, I think the main reason for not seeing sales through the platform may be the calculus people do between the desire for specific products versus the speed of receiving said product.  The crux of the formula is if I can’t have something right now, then I want to have more command over what I’m getting, but if I can get it right now, I’m far less picky.


Imagine this setting: you want pizza. Right now. And perhaps some sort of pizza is available essentially immediately. Who knows, maybe you’re jonesing for a slice and you’re near the food court at the mall (if people still eat at the food court at the mall?) The desire to have exactly a specific brand and style of pizza may not be as strong as being able to just get some pizza right now.

The result would be completely different if you were at home and had to either travel to get some pizza or wait about the same amount of time to have pizza delivered. In this instance, I’d bet you’d consider much more closely your options. The nearly all-bread pizza at the mall would probably not make it to the recall set.


Applying this thinking to summoning Alexa to buy whatever pizza product Amazon desires to send you ends up within the aforementioned ‘formula’ – because any option Amazon has is well outside of instant gratification. Regardless of the ease of absentmindedly bellowing for pizza, you’re still waiting for delivery by conventional means – even though it may be through a relatively quick Ubereats or Dominos. If you have that time available, then you’ll want to be more specific than ‘Alexa, order me (any) pizza.’ That’s the rub of buying through the device. Factoring the probability of getting exactly what you want against how long you’d wait doesn’t seem like a good bet.

Sure, there are some purchases that could work well with the platform. Playing or buying digital media is a good bet. Even buying apps would be. On the other hand, tangible items (like the bulk of what’s available on Amazon)  we’ll have to wait for. Waiting builds expectations – as well as the magnitude of disappointment when Amazon guesses it wrong. Maybe this is the reason we’re seeing Alexas with screens coming out and delivery drones. Amazon has to either fix the specificity issue or it has to solve the instant gratification problem to really make buying on Alexa work for the masses.

Looking at What Makes a Good Ad


In today’s age of digital metrics and multi-channel engagement, the conventional ad is sometimes thought of as an ineffective dinosaur. This thinking comes from the experience of running ineffective ads and seeing nothing tractable from the effort -especially in the face of digital offerings that can at least send back some sort of metrics. Having worked with a number of clients to build advertisements over the years, I’ve learned that making an effective ad is something that’s a bit of a mystery for many.

To shed some light on effective ad development, I’m going to be doing a series of posts that speak to the more important aspects of ad design by using actual advertisements as examples.

Some examples will be shown for the good things, some will be for the not so good things. I’m going to try and mix up the kinds of example ads and the target markets they aim at. At the end of the day, it’s not to chastise the bad or the lost. Regardless of the sorts of products or consumers aimed at, the breakdown of these ads should help future ad creators in making their work a bit more impactful – and for the right reasons.  The goal is always informative.

To summarize the core tenets of what makes a good ad, I’ve put together an entry-level list of the most important aspects. I’ll go into detail about these aspects in successive posts.

What makes a good ad:

  • It speaks the target audience’s language
  • It’s placed where the target audience will be
  • It has asks a specific action of the viewer
  • It has one specific and focused message
  • It’s designed to impart that message within a blink of an eye
  • It’s built to impart and support the brand positioning of the firm

While this series is designed to be primarily aimed at conventional advertising, by no means does it disqualify the salient points from being completely true and useful for the digital/social world. In fact, regardless of today’s abilities in A/B testing, availability of analytics or even healthy slatherings of machine learning or other buzz-friendly technologies, the core tenets here will still have much more impact and utility to crafting an ad that gets the customer results desired.

The End of “Request Quote”


The B2B industry has long relied on the phrase “request a quote” for the opening of a business sale. As far back as when people wanted to buy things from people not right next door, the phrase has been in place. It was in place when there was just phone books, magazines and even personal printed versions of the big green Thomas Register – and it was there when the Thomas Register and the rest of us went online.

For a while the phrase, and then the button that leads to a form (that we all were confident would never be responded to), was working quite well. But technology eventually catches up to everyone and B2B is no different. Most suppliers and manufacturers have seen the benefit of merely having a presence on the internet. And they should, as studies have shown that even the B2B world sees 93.7% of purchases start with search. So what’s my issue with putting up a quote form rather than a cart?

The issue is that the B2B shopper is also a B2C shopper. If we all dig down, we all know it to be true. The B2B shopper (us) has had something like two decades of point-click-buy experience. It’s no longer a wish or desire, it’s a necessity for consumer-focused firms to be able to close that sale with as little friction as possible. With this in mind, I would posit that it’s almost impossible to close B2B stock product sales by hoping the customer picks up the phone instead of navigating to a site with a buy button.


The question becomes, “So why isn’t every B2B supplier ditching the quote button for the buy button – especially with stock components?”

One of the most lauded reasons is that the capability to quote parts rather than just price them is that it gives a black box aspect to the sale. This layer of obfuscation helps firms adjust prices to the scale, complexity – or even the sort of customer that’s inquiring. Small run? Well it costs virtually the same to service a small order than it does to do a big run. Therefore the short run costs more because the time could be spent on large orders. High complexity? It takes more brain power. The price goes up. A customer who has deep pockets or a short time frame? Price goes up. That flexibility in pricing is hard to give up, for sure.

Another is the perception of insurmountable complexity in setting up an online ordering system. The fear is that it requires a room full of people and a stack of servers humming along in the basement, killing the AC and sucking the life out of the bottom line.In reality, the barriers preventing companies from opening up ecommerce operations is the lowest it’s been ever. Personally, I have been able to go from bare web server to storefront in under two days. Granted, it’s no Amazon or McMaster Carr but if I can do it, anyone can.

Perhaps the scariest reason of them all is,”we’ve always done it this way.” Sadly, I’ve heard this more than I should, especially in the B2B world.

What’s the cost of all this fear? Lost sales. The B2B purchasing manager who has gotten used to buying all manner of products for themselves and others with a finger touch (and probably on their phone) knows that there’s a firm out there that has a site which will tell them the price. Of course, that site is also the one that has a ‘buy’ button so they can just get it now, not wait a few days to have a quote compiled, received, compared, agreements signed and so forth for an off-the-shelf-part.

Don’t believe me? Studies have shown 57% of the buying process is done prior to engaging with Sales – that’s before requesting a quote. That’s probably because 80% of companies implementing B2B e-commerce believe that their customer expectations have changed due to B2C practices.

Maybe the scariest fear should be the feeling of prospective buyers skipping over a quote site for the vendor where they can simply press ‘buy.’ What’s even scarier is that buyer will never find out a part is cheaper, faster to ship or better in some regard, the sale was passed over because there wasn’t a ‘buy’ button.

A different take on GoPro’s Woes

A harrowing story caught on a GoPro – Check it out.

I read an article the other day on TechCrunch that I’d summarize as “Forces are holding Silicon Valley back from creating viable hardware companies.” One of the reasons that sticks out in my mind most from the article is that the largess of the incumbent players in the consumer hardware market make it extremely difficult to enter said market. The article used the recent stock gyrations of GoPro and by extension, the talk that the firm may be up for sale as proof of such a hurdle in the market.

I don’t know if GoPro is up for sale and I don’t know if they should sell. Heck, I don’t even know if you should buy or sell the stock – use your best judgement, not mine. However, I was interested if their financial data would show some sort of barrier to entry issues that has caused financial issues with the firm. For this exercise, I took a look at the quarterly income statements and balance sheet data (Courtesy of AmigoBulls), as well as the quarterly stock price for the firm (From Yahoo Finance) for the term of March 2013 to August 2017. The firm’s statements would indicate it went public in somewhere around June 2014 with an initial price of $24 a share, a little while after it officially adopted the name GoPro. The financial statements also contain data prior to the IPO that I assume is part of the regulatory filings necessary before the initial sale.

When you have a look at GoPro’s numbers it appears as though it’s a company that’s doing not terrible. Below are the Net Sales, CoGS, and Net Income.

sales cogs chart

Through January 2016, the firm seems to be growing quite well, with the rather predictable Christmas bumps in the numbers.  There’s no spectacular growth here so it’s probably moving to maturity in the market with the current products it has.

The sales to cost of good sold remains more or less congruent through the period. That would indicate there hasn’t been any shenanigans with suppliers or absurd price pressure from ones with larger economies of scale. I’d assume that after January 2016, the low price competitors have entered the market and had begun swipe away the low hanging fruit, hence the drop in net income. By the next year, it would seem GoPro had puzzled out some responses to the invaders.

retained earnings chart

Looking at the Balance sheet, it would appear that these numbers would support a re-targeting of the firm by investing in areas where GoPro could best compete. This could be reflective of the drop in retained earnings and the increase in assets and liabilities. I’d assume the retained earnings were re-invested rather than being returned to shareholders during this period.

Going back to the income statement, it would show that R&D pulsed at the time sales dropped and had leveled off in the second quarter of 2017 where I’d assume those development efforts moved to production and instigated the need for capital spending support, thus the uptick in assets and liabilities at the end of the previous graph.

While I’ve dug out nothing more to go on – call it looking for Occam’s Razor…or laziness – this would indicate to me a rather stable firm with a management group that’s on their game at some level. But more importantly, these findings fail to indicate any tremendous pressure from a large established consumer electronics firm bent on crushing outsiders.

So where’s this insurmountable barrier for Silicon Valley hardware startups?

I think the real culprit can be found when looking at the movement of the stock price. Here’s the quarterly stock price for the firm for the same time periods above:

stock price chart

When you put the stock price up against the previous income statement graph, the issue becomes clear. In the beginning, the stock price seems to be independent of the movements of the underlying company metrics but falls more inline around January 2016.

Here’s my favorite graph so far.

EPS chart

What you see here is the average trailing PE for consumer electronics (and office) products as offered by Sterns in red. It’s the PE for the entire industry. The BLUE line is when you take the first quarter share price of the newly public company and use it to calculate the three quarters BEFORE launch from the provided EPS. After launch the blue line is calculated by using the real EPS divided into the correlated share price. The smaller spike before January 2015 is the IPO issuance.  

My takeaway is it’s not that Silicon Valley cannot produce a competitive hardware company, it’s that they can’t price a hardware company for IPO adequately at best, and perhaps they don’t really understand the investment requirements that physical goods companies require at worst. What’s worse is investors to this day use that hype induced IPO spike as a barometer of the company’s real worth going forward – (a tribute to the power of behavioral finance and perhaps bad VC profit harvesting practice) damning the firm to under-performance perceptions going forward.

Further, GoPro may be a fine company at the scale it is now – and perhaps this is the scale it always should have been considered at. Pumping its stock price to the stratosphere served only to hurt the firm in the long run, which is why what seems to be a perfectly fine company has to fend off rumors of a potential sale rather than incrementally building itself – which is what most solid companies do that are not founded in Silicon Valley.

In the end, GoPro, TechCrunch’s pinata, may actually be priced pretty closely to fair right now, too bad it wasn’t in the beginning…but your metrics may vary.


Is It the Product or the Service Around It?

Please visit Grand Auto Exchange in beautiful Waukegan, IL

Service oriented companies sometimes have it pretty easy. Service is their entire offering.

On the other hand, the product company studiously compares feature to feature, specification to specification in hopes of finding an edge with the customer. Maybe new positioning would help differentiate the product in a competitive market. Maybe pricing could create some much needed sales momentum. With everything that goes into marketing a competitive product it’s easy for a company to get caught up with how an item performs against others but neglect the encompassing service aspects that could make or break a sale.

A look into Cadillac’s new leasing focus is a good example of a product-focused firm’s heretofore careful work on product at the cost of the services surrounding buying process. As the article states, Cadillac has been producing an entire line of vehicles that can go toe-to-toe with European luxury brands but sales haven’t been reflective of those advancements in design, experience and technology. No real penetration into the luxury market Cadillac once owned a long time ago.

The cars were re-designed, the showrooms were overhauled, and the brand was re-positioned younger. The actual price of the car was competitive, but the lease costs – the method the target customer uses most to acquire vehicles – were not in line with the market. This seemingly sublime, box-checking component of the sale may be the aspect that has hindered Cadillac’s new market penetration it has worked so hard for.

Not having worked at Cadillac, I could only postulate why this aspect was overlooked, but I’ve seen similar things happen in other markets – especially in highly competitive B2B markets where sometimes it’s exactly everything but the product that sets the offerings apart.

If the firm builds products, it’s critically important to consider every aspect of the customer’s sales journey. Sometimes those mechanical bits that a little product myopia doesn’t see becomes the most important aspect in a sale. Responsiveness, delivery performance and even sales or warranty terms can be the make or break point for a sale. The savvy have to keep focus on all these aspects if the firm is to remain competitive.

The Subtle Genius of “Okay Google”


It struck me today that I don’t think I’ve heard anyone speak about these voice services from a particular marketing perspective. Sure, there’s been talk about marketing the devices, the services themselves and even how selling something would work on a platform where there’s no visual aspect to it. But let’s talk about something different. Let’s talk about branding.

The overall goal of branding a service, product or company is to basically connect the name or representation of the company to (typically) positive aspects that set them apart from their competition. It also comes in two flavors, the perception of what the company experience would be and perhaps even more important, the connection to the actual qualities experienced afterward.

Ideally, this allusion is also instrumental in nudging the buying process into the company’s favor. For instance you have DeWalt, a brand distinct from Black & Decker whose products are aimed at professional builders. Thus the branding works to exude an almost industrial-quality ruggedness. Or there’s Breitling who positions themselves as the timepiece of the elite sports enthusiast by connecting the products to pursuits of the wealthy like pylon racing or high-end auto racing. DeWalt tools are pictured at the job site, and Breitling is plastered all over events like Le Mans. That’s their best shots at reaffirmation and connecting with the target audience.

So how do the voice services do in this regard? There are a number of services out there but I’ll focus on the big four: Alexa, Siri, Cortana and Google Assistant. The first three are all brand new names in the personal electronics space. Google chose to go for something of a brand extension. Interestingly, the “…Assistant” part of the Google service doesn’t show up much. The other’s are also the call word to activate the service. One summons the Google Assistant with “Okay Google” Big deal, right?

Looking a bit more closely, the first three product names have no real baked in connection to their respective master brands. Each of the firms had to work diligently to connect the dots and construct not just the connection but the individual names themselves. It’s a much more difficult time to think of Cortana or Alexa as a component of the greater ecosystem of their respective companies. It’s been tough sledding to get people to recall shopping on Amazon with their Echo products. I’m sure it’s been rather dreadful trying to explain the utility of Cortana with Microsoft products and Siri’s, well, Siri – it seems to be around to help sell the phone you already have.  

Then there’s Google’s foray. What’s so genius about saying “Okay Google” is that the company’s brand name is on the tongue of every person who uses the device. That’s something to really let sink in.


It’s a branding coup unlike anyone has ever seen. Google has figured out a way to have customers say its name over and over again – and (most times) in the exact moment when their main service is needed most. The name ‘Google’ is synonymous with search and now when people use their nearly omnipresent Google device they also repeat over and over again the brand of the company. Every utterance for the service is a reaffirmation in Google as the search of choice.

Previously, the best outcome was having a customer hear your brand name, there’s having them see it and there’s having them read it. To have the customer in their head connect the name of the company with its prime product over and over and over again from thinking to saying and being rewarded is almost the greatest psychological trick marketing could achieve.

Will Google be victorious in the space? I don’t know. From a branding standpoint, they’ve got the best foundation out there.

Is the arrival of VR the end of two dimensional office working?


Pie Graphs! Bar Graphs! Fish Graphs!

Curiously, VR is rearing its head at the same time big data and artificial intelligence technologies are washing ashore in businesses across the globe. As I’m sure it’s been hammered into everyone’s head, Big Data is the answer for everything (if you tend to believe the hyperbole) – unless AI is the answer. When looking at any number of Big Data articles, a common refrain is that more nuanced results come from more causes that combine in ways that were heretofore impossible to calculate, much less visualize with current technology.

Behold, the obligatory ‘Big Data’ image.

What does Big Data look like? The more-or-less tangible manifestation of it is typically large databases which have a number of interlocking tables that connect data in ways that a piece of paper would have a hard time containing. It could also be large amounts of unstructured data or perhaps real-time streams of the stuff. Any one of these aspects make dumping data into a spreadsheet quite difficult to possibly impossible prospect. That also means we’ve essentially started butting against ends of what two dimensional spreadsheets can do without doing an excessive amount programming behind the scenes.

The world isn’t as simple as what spreadsheets can display, either. Or, more to the point, perhaps we’ve already harvested the bulk of the easy correlations and causations that can be seen. A great analogy is the bounty of insights found simply from moving data from paper records to the computer and having the capability to apply basic math capabilities to that data. The simple ones sound like knowing right now what the balance sheet looks like. Or even better, being able to show percentage values of where a firm spends money. Maybe even plotting product quality data to find unseen trends. That was cutting edge in the 60s, 70s, and 80s, but what was cutting edge yesterday is just not enough in the business of today and certainly not in the future.

Perhaps the next step in office applications is when we not just view but operate on these data sets in their multidimensional world rather than working to transcribe them into dumber formats. The ability to enter that 4D space with VR allows us to have that opportunity.

Lawnmower man, the movie from only 1992 – Oh Pierce, sorry to bring this up!

Increasingly we’ll also see artificial intelligence seep into our workplaces as well. It won’t enter through the Hollywood portrayals, it’ll come in small ways. Smarter applications that solve the easier problems and eventually round up insights on the usual subjects. Those usual subjects are the same ones that we spend a lot of time creating complex spreadsheets for. Humans won’t have to do that anymore. We’ll need to focus on where there’s more ambiguity, sensitivity and creativity for as long as it takes before our AI overlords to catch up.

All this means we’ll increasingly see ourselves operating on projects of increasing complexity during our workdays. How better to do so than to bring the benefits of VR to the business world. I could only guess what these applications will look like but I’m sure that they will allow us greater ease in manipulating greater density data – because that’s what the future looks like for the human worker.