💣Diebold. Disrupted.💣

Are Point-of-Sale & Self-Checkout Systems Effed (Short Diebold Nixdorf)?

Forgive us for returning to recently trodden ground. Since we wrote about Diebold Nixdorf Inc. ($DBD) in “💥Millennials & Post-Millennials are Killing ATMs💥,” there has been a flurry of activity around the name. The company…

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The Great Escape

"The difficulty lies, not in the new ideas, but in escaping from the old ones, which ramify, for those brought up as most of us have been, into every corner of our minds." - John Maynard KeynesThe General Theory of Employment, Interest and Money (13 December 1935).

This past week, Michael Batnick, from Ritholtz Asset Management deployed a version of this quote to make a point about investing; he provided a nice callback to Blockbuster and an equity analyst's repeated bad calls vis-a-vis Netflix (which popped this week after impressive subscription growth - despite negative free cash flow (-$600mm+) and an increasingly levered balance sheet). TL;DR: don't be too wed to your ideas. 

This applies to the actual businesses that investors pour money into too. In today's rapidly transforming environment, businesses must now, more than ever, pivot, and innovate. They can't be too wed to legacy ideas. Recognizing that is the first step. It then becomes a question of execution in the face of constraints.  

Enter Avaya Inc., a privately-held provider of contact center, "legacy" unified communications and networking products and services with 176 global entities, $940mm of adjusted EBITDA, 200,000 customers and 9700 employees. To Avaya's credit, the company pivoted in 2009 away from its historical hardware-based operating model - recognizing the shift towards software-based and cloud-oriented services solutions. It undertook a massive reinvention, adapting its revenue model and streamlining operating performance in a manner that cut $700mm in costs since 2014. To some degree, the company's private equity overlords - TPG Capital and Silver Lake Partners - deserve some credit, too, for working with management and reconciling the need to pivot. After all, they had a $8.2 billion LBO to rationalize. 

But sometimes the constraints are insurmountable. Avaya has $6 billion of debt on its balance sheet; it has $440mm of annual interest expense along with an additional $180mm nut for annual pension and OPEB obligations. And it faces stiff competition from the likes of Microsoft, Cisco, and others, necessitating another ~$400mm in expenditures to fund R&D and other investments. It's been bleeding cash, losing over $505mm in the fourth quarter and over $750mm in fiscal '16.

And so the company is now a bankruptcy filer. Notably, the papers accompanying the filing have zero specificity about the company's go-forward business plan. Its one small victory is a robust DIP financing commitment and milestones intended to achieve a rapid turn in bankruptcy court. But then what? 

We rarely see big freefall bankruptcy cases anymore. Clearly there seems to be disagreement among the various constituencies about how best to proceed with this business in the face of competition and technological headwinds. That said, the company was able to secure $425mm of its proposed DIP facility at the "First Day" hearing on Friday. So there's that. 

"Software is eating the world," we noted last week (per Marc Andreesen). We'll see very soon whether Avaya gets swallowed along with TPG and Silver Lake's investment. Yes, they pivoted. But was it too little too late?

Thoughts? Opinions? Let us know at petition@petition11.com. 

Odd ad to place in the WSJ on the day after bankruptcy.

Odd ad to place in the WSJ on the day after bankruptcy.

A Look Forward

Right before the holidays, Benedict Evans of the venture capital firm Andreesen Horowitz released a fascinating presentation called "Mobile is Eating the World." It's a long presentation - roughly 31 minutes - but well worth reviewing if you have the time. We here at PETITION think there are a lot of nuggets within it relevant to the restructuring industry. After all, technological advancement and disruption help create the industry's client pipeline. Here is a brief summary with some editorial mixed in:

Overview

  • We are halfway to connecting everybody. There are 5.5 billion people over 14 years old, close to 5 billion people with mobile phones, and about 2.5 billion smartphones. The latter number is quickly headed to 5 billion.
  • Mobile has accelerated past the PC, which is now flat-lining at around 1.5 billion units.
  • Each new technology follows an S-curve (creation-to-deployment) and is then passed by a new technology. Mobile is transitioning now from creation to deployment. 
  • With this transition comes a new kind of scale. Google, Apple, Facebook and Amazon ("GAFA") have 3x the scale ($450b annual revenue) that Microsoft and Intel had in their heyday ($150b annual revenue). Microsoft saw 14x growth when it was dominating tech in the 90s and subject to mass regulatory scrutiny; GAFA's growth is 10x that now. 
  • In 1995, Microsoft was not even the biggest company on the stock exchange. Now Microsoft and GAFA are the top five companies on the exchange. 
  • This size drives more capex: $1b of capex in 2000 vs. $30b of capex in 2015. Tech has so much more scale now: GAFA are giants of the ENTIRE economy, not just tech. 
  • Which has implications: Apple is the 10th largest retailer in the world with $53b in revenue across e-commerce and 500 stores. Netflix has the fourth largest entertainment production budget in the world. Amazon has the sixth - even though its content is just a feature to drive its core product: Prime. These "tech" companies, therefore, are fundamentally impinging upon other industries. Another example: Google, Amazon and Apple are now making custom chips for their own products rather than sourcing externally from the likes of Intel. 

New Ways to Compete - Artificial Intelligence & Machine Learning

  • The scale of 5 billion mobile users and the scale of GAFA are leading to new ways to grow and compete.
  • And machine learning is steroids. As just two examples of the rapid progress in machine learning, image recognition has gone from a 28% error rate to 7% and speech recognition from a 26% error rate to 4%. This is all enabled by mass data and more powerful computing power. 
  • And so everything in tech is being refocused from mobile to mobile+AI, particularly with the realization that there are cameras everywhere, capturing images that serve as data that are now more intepretable than ever.
  • GAFA is rushing to build the engineering and cloud storage systems to enable optimization of this data. 
  • Meanwhile, technology design is removing friction, questions and administration which, in turn, changes choices. Think Amazon Echo. So, better design and frictionless decision-making is feeding more and more data.
  • All of this gives GAFA the power to (further) change other industries...

Example 1: E-commerce

  • Everything the internet did to media will happen to retail, where there'll be a breakup of old bundles and aggregators (albums, magazines, newspaper, store, shopping district, mall). And so now we consume in different ways.
  • So far ecommerce mostly just gives consumers stuff we already knew we wanted.
  • E-commerce is 10-12% of US retail revenue, with Amazon representing at least 2-6% of that: but it mostly just gives you what you already know you want. Despite this limitation, Amazon is now the fourth largest apparel retailer in the USA: not online, OVERALL. Walmart, Macy's, TJ, Amazon, Gap, Kohls, Target, L Brands, Nordstrom, JC Penney (by '15 revenue). And those reading PETITION regularly know how well some of these names are faring - or NOT. 
  • The internet lets you buy, but it doesn't let you shop. No real suggestion or discovery.
  • To fill this gap, the first response to this is advertising and marketing which is $1 trillion a year, $500mm is ads (digital and Google ads).
  • But now we ask the Amazon Echo to buy more soap and this means we may never make a brand decision again. This disintermediates the ad agency, Walmart and P&G, etc, and changes the whole chain of how something gets to you, the consumer.
  • Meanwhile, new businesses can get something to you with way less investment.
  • Machine learning can give you "scalable curation" based on the data that you feed it.
  • Today you have to go to a store to know what you'd like without seeing it. Now you can use machine learning to give this to you.
  • Data is working through retailing: supply chain and logistics moved to advertising and digital metrics and then demand based on data, social, etc. Walmart used logistics to change what retail looked like. Amazon now doing that with AI. $20b retail opportunity potentially disrupted. 

Example 2: Cars

  • Cars are becoming like phones with all of the important aspects becoming commoditized and the key being the software.
  • Removing the engine and transmission destabilizes the car industry and its suppliers - but it doesn't change how cars are used much.
  • Autonomy, however, changes what cars are and changes cities.
  • Electric is about the battery cost curve. Complex proprietary gasoline engines and transmissions disappear and replaced by simple commodity batteries and motors, 10x fewer moving parts: all aspects of auto manufacturing and energy use are implicated by this development. 
  • Scale, design and brand still matter but the real value moves up the stack into the software and move to autonomy. Leading tech companies now spend as much on capex as car OEMs. 
  • Where are we now on the 1-5 autonomy scale: we are at Level 3. Level 5 is 5-10 years away. Batteries and sensors increasingly are commodities. The key is the software and the AI-powered data to feed it.
  • Once you have that and take the steering wheel and engine out you have totally new types of vehicles and new uses. Obvious impacts: oil production and safety (1.25mm annual road deaths). Second order effects: what happens to engine servicing industry, machine tooling industry, storage, gas stations, gasoline taxes, municipal parking revenues, police forces? What happens if there's no parking or congestion? What happens to housing, logistics, commercial real estate, trucking, ownership of cars, insurance? 
  • And what incumbent companies and municipalities file for bankruptcy as a consequence? This is not science fiction: society will soon need to address these questions...