Reading New Thoughts: O’Reilly’s What’s The Future, the Agile Entity, and Prediction

Disclaimer:  I am now retired, and am therefore no longer an expert on anything.  This blog post presents only my opinions, and anything in it should not be relied on.

Tim O’Reilly’s “WTF:  What’s the Future, and Why It’s Up to Us” is, alternately, an insightful memoir of many of the computer industry events that I lived with and to some extent participated in, a look at the oncoming technologies coming from the computer industry and its spinoffs, with particular over-emphasis on Uber and Lyft, and an attempt to draw general conclusions about how we all should anticipate what’s going to impact us in the future and how we should “ride the wave”.

First, I want to add a caveat that I think should become a Law:

The future happens faster than we think – and then it happens slower than we think.

By this I mean:  when new technological breakthroughs arrive, not all are obvious to a particular part of the economy that we attend to, even if (today) they are linked by software technology.  Then, even when they seem like the “new new thing” everywhere in our particular area, they typically take 10-30 years to spread to the world at large.  For example, smartphones and their apps (themselves over 10 years old) are by no means ubiquitous in the Third World, despite the hype.

I’d like to note here several instances of the future arriving “faster than we think”, some profiled in WTF.  Among the ones that I find amazing (and sometimes frightening):

·         We can now alter and replace 20-gene DNA and RNA segments, and hence genes in general, not only for the next generation but also in many cases over the course of a few months for our own.  The work to achieve that happened less than 10 years ago, practical implementation was achieved less than 5 years ago, and the Nobel Prize for that work (led by Jennifer Doudna and her team, described in her book) was awarded this month.

·         Pictures of anyone can be inserted seamlessly in a different scene, making it very hard to tell the truth of the news pictures that we see every day.

·         Understandable automated language translation (e.g., Google), automated voice recognition, and automated picture recognition have been achieved (although “good” speech recognition has still not been reached).

·         Semi-automated bots generate comments on articles and in blogs that are often indistinguishable from the ungrammatical and rambling comments of many humans.  Hence, the attempts at hacking political elections and the increasing difficulty of figuring out the truth of events in the public arena, “crowded out” as they now sometimes are by false “rumors.”

More subtly:

·         Uber and Lyft create “instant marketplaces” matching buyers and sellers of taxi services.

·         Supermarkets now dictate to growers production of specific items with detailed specification of quality and characteristics, based on narrow segments of the consumer market.

Now, let’s talk about what I think are the new thoughts generated by WTF.  In particular, I want to suggest that O’Reilly’s view of how to “predict” which oncoming technologies should be factored into one’s business, government, or personal strategy going forward, how to fit these into an overall picture, and how to use that picture to develop strategy, is really of most use in developing an “agile strategy” for an agile entity.

WTF Key Technologies and Strategies

Perhaps the best place to start is with WTF’s “Business Model of the Next Economy,” i.e. a model of the typical firm/organization in the future.  There are many subtleties in it, but it appears to break down into:

·         Central “Networked Marketplace Platforms”, i.e., distributed infrastructure software that provides the basis for one or many automated “marketplaces” in which buyers and sellers can interact.  In the supply chain, the firm would be the primary buyer; at the retail level, it would be the primary seller.

·         Feeding into these platforms, an approach that “replaces markets with information” – instead of hoarding information and using that hoarding to drive monopoly sales, the firm releases information openly and uses the commoditization of the product to drive dominance of new products.

·         Also feeding into the platforms, a new approach to user interfacing that seeks to create “magical experiences.”  This particularly enhances the firm’s and platform’s “reputation.”

·          Another “feeder” is “augmented” workers – workers enabled by rather than replaced by AI-type software.

·         A fourth “feeder” is “On-Demand” (applied flexibly, as needed) talent (workers given added value by their talents) and resources.  This includes an emphasis on actively helping workers to succeed, including over the long run.

·         A fifth feeder – somewhat complementary to the fourth – is “Alternatives to Full-Time Employment”, where the emphasis is on being flexible for the benefit of the worker, not the employer – the takeaway being that this actually benefits the employer more than WalMart-style “show up when we need you and never mind your personal life” approaches.  The key newness about this approach is that is “managed by algorithm” – the algorithm allows both the employer and employee to seek to manage their needs in a semi-automated fashion.

·         Returning to the business itself, the final feeder to the marketplace platform is “Services on Demand”, which offers to the consumer an interface that is providing an ongoing service rather than simply selling a product.  This is enhanced by “marketplace liquidity,” ways to make it easier for the consumer to buy the service.

At this point I revert to my caveat/Law in the beginning.  This “next economy” model is already operating in parts of the computer industry and related fields, e.g., Amazon, Google, Lyft – the future has already happened faster than we think.  At the same time, there will be a longer time than we think before it diffuses across the majority of organizations, if it does so at all.  Government and law are two obvious places considered in WTF where this model holds great potential, but will take a long, long time to effectively apply.

If the object of the game is to “ride the technology wave” by predicting which oncoming technologies should be factored into one’s business, then the technologies in this model are relatively safe bets.  They are already past the stage of “timing”, where the technology is attractive but it may not yet be time for the market to implement it.  As WTF points out, the trick is not to simply latch on to a strategy like this, but to constantly update the model and its details as new technologies arrive at their “timing” stage. 

Enter the agile strategy.

Prediction and the Agile Entity

The agile process is, on its face, reactive.  It does not attempt to get out ahead of the combined wisdom of developers/process-users and consumers/end-users.  Rather, it seeks to harvest that wisdom rapidly in order to get out in front of the market as a whole, and only for the purposes of each development/rollout process.

An agile strategy (which, up to this point, I haven’t examined closely) should be a different animal.  Precisely because any strategy bridges a firm/organization’s entire set of new-product-development efforts as well as aligning the rest of the organization with these, an agile strategy should be (a) long-term and (b) to a significant degree in advance of current markets.  

In the case of the strategy outlined in the previous section (i.e., implement the “new business economy model”), one very straightforward way of adding agility to the strategy would be to add agility to the software and analytics used to implement it.  One tried-and-true method for doing this is “refactoring” – adding a layer of abstraction to the software so that it is relatively easy to change.

Another method is simply to plan to revisit the strategy every 3-12 months.  The agile CEO I interviewed and reported on in a 5-years-old blog post did exactly that – a 5-year plan, revisited and informed with both his outside feedback and the information he gathered by attending scrum meetings.

WTF adds a third dimension:  attempt to discern upcoming technologies and approaches that are “important”, and then “time” the shift to a new strategy incorporating those technologies/approaches.  “Prediction,” in these terms, means anticipating which oncoming technologies/approaches are important and also the pace of their evolution into “timely” products and services.

I would argue, however, that this is precisely where an agile strategy adds value.  It does not assume that what seems important now stays important, or that an important technology/approach will arrive in the market in the next 5 years, but rather that whatever steps we take towards preparing the way for a new technology/approach must be flexible enough to switch to another technology/approach even midway in the process.  For example, we may move towards augmenting our workers with AI, but in such a way that we can instead fully automate one set of workers in order to augment a new type of worker whose responsibilities include that of the old.  We would be, in a sense, “refactoring” the worker-task definition.

So here’s my take from reading WTF:  It should be possible, using WTF’s method of anticipating change, to implement an agile strategy as described.  Moreover, an agile strategy should be clearly better than usual ones.  Usual strategies and agile processes do not anticipate the future; agile strategies such as this do.  WTF-type strategies anticipate the future but are not flexible enough to handle changes between identification of the future and the time for its implementation; an agile strategy should be able to do so.