Car v Cloud (Autonomy and business models)

This is not a post on cloud enabled cars or how autononous driving will use the cloud. Its a collection of thoughts on how  and when computing business model got disrupted by the cloud and similar analogies might hold for the automobile industry. A lot has been discussed on the business models of Tesla, Waymo, Uber, Lyft and perhaps what I am stating here is already observed and stated by others. I thought I will share some of my observations based on experience an EV which has autonomy as a core tech differentiator (Tesla) and economics of the EV model. This was prompted by a financial analyst session that I attended hosted by Merill Lynch on the “The future of driving” and debates with my good friends Atul Kapadia and Mike Klein.

Lots of good observations by Daniel Daniel of Merilly Lynch, but a few I will share here to set the stage for my analyses and observations of this topic. The annual automobile ownership costs and cost per mile are thus (based on 15K miles/year)

Sedan SUV Minivan

57c 68c 61c

Constrating that to a Model X (75D) assuming 15K miles/year and free supercharge (unique to Tesla) works out to 72c/mile (included in that is cost of capital, electricity cost, nominal maintenace/year and insurance). So most of the cost. The model 3 by contrast (LR , rear wheel) comes to 47c/mile. My benchmark is, if I can get to half the cost – 24c/mile, I might be open to a new model of using a car. Maybe its lower – but that is the starting point for this discussion. So what does it take to get to 24c/mile. For reference Uber and Lyft charges me >$1/mile. I would rather drive a 24c/mile than >$1/mile is its available at the click of a button from my Phone.

Current ICE cars that are lot cheaper to buy (20-30%) for similar size and features – but EVs certainly are more limited in range. ICE that have range (Toyota Camry is perhaps a good benchmark is 30+% cheaper and achieves great mileage – no wonder Toyota has postponed the EV transition).

So if you are driving 15K miles a year and keep the car for 10+ years, its better to own than use Uber. Much like if you have cloud compute with reserved instances for 3 years esp for larger instances of VM, its better to have your own infrastructure than a cloud instance. (yes, availability and other functionality is better in the cloud – but for the purpose of this – just comparing cost at the VM layer). Amazon’s infrastructure economics works when there is re-purpose and better utilization of the infrastructure.

So when does the car ownership model will be subsumed to a subscription model like cloud computing? Definitely if you are driving less than 15K miles and esp short distances where Uber or Lyft is viable and maybe less than 6 years before you replace the car. That is not the majority of US car buyers today. But some other dynamics are at play like cloud computing that could well play out here. EV cars and autonomy do add cost. EV has the other problem of battery life cycle and battery life (age). So lets assume a 100KWh battery and 80% charge per cycle. With gas at $4/gallon and 27mpg for an SUV vs 40mpg for a sedan (sparing all the cost assumption details), it seems like if a car or car service can have

  1. Share the capital spend thus share the car with anbody or a few (different models will appear)
  2. Full autonomy so it can go from Point A to B and thus enable sharing
  3. Simple access (like the model 3 iphone or card access) – make it impersonal (don’t leave your personal stuff in the car)
  4. Be ‘usable’ for 2-3x mileage than normal driving – e.g. 480K miles in 8 years i.e. better use of capital spend.
  5. Available at points of use or within walking distance say 250 meters or comes to you on call.
  6. Sufficient energy for daily driving distance and nightly or daily charge up.

Then the extra cost of an EV and autonomy can be justified. Simple math shows at $50K (pre-tax) model with 75KWh battery with 450Kmiles driven over an 8 year period (125 miles per day) will cost 24c/mile. The SUV economics are different – as the transport and passenger profile are different (like needing 256 GB VM machines vs 4TB VM machines – usage model is different to justify that cost).

So the challenges or in other words oppotunity is to have a $50K car with Autopilot and ideally 100KWh battery. Combine that with simple to walk in and drive (model 3 already achieves including driver personalization), will use autopilot to primarily autonomously let a car drive from person A to B. The individual use of Autopilot is a whole different matter frought with different acceptance profile, legal etc.

Of all the car companies, seems like Tesla is further along the key parameters above. If they can be achieved, I would be open to not have my own Tesla and go for a shared car managed by the cloud at 24c/mile vs my current >50c/mile. That is $300/month of ‘auto subscription’.

So you may ask – the same model will apply to gasoline cars with autonomy. Sure. Like in cloud – I am not just buying a VM, i want the fancy new functionality. I want my driving experience to be simpler (use autonomy in stop and go traffic), like the performance characteristics (acceleration etc when I need it). I have also made everything more efficient (expecting electric cars to have longer mechnamical life) except for the battery.

Why is this not the future of Tesla network or Uber or Lyft. I think the nuance is a slightly different business model. Tesla network could – but who will pay for the upfront capital and where will be the fleet park (lot of cars). For Uber or Lyft, they solved the problem with Taxi service alternative, but its not clear they have addressed the future business model. As Mike Volpi says here – the treat the car OEMs as ‘metal benders’ like the cloud companies did initially with Dell or HPEs and out of which ODM model formed. They can morph to a new model – but even with them, who is going to pay for the upfront capital and parking (nightly or other gaps in the day). You need a lot of cars to be paid by somebody to make this work. That is how the cloud infrastructure was built out. Who has the largest fleet that is under utilized. Maybe its Hertz, Avis etc. Even for Hertz and Avis a massive fleet upgrade and associated capital cost is expensive. Maybe there is a hybrid model that is evolved from the current Turo model and a lease model.

At the end its a financial engineering that fits the most common usage model. That will be the winning business model much like AWS was able to leverage the unused capacity and technology built it out for their own use to deliver and automated, easy to consume cloud computing.

Something to think about. What if I can subscribe to 8c/mile auto service. That is > 10x of Uber or Lyft and that is $100/month charge for 15K miles. Somebody is going to solve it and either a NewCo or evolved business model from Tesla.

Love to hear your thoughts.

RIP – SPARC and Anant

A year back when Robert Garner  sent an invite for the 30th anniversary of SPARC,  little did I expect  or believe events that transpired later would happen. I did this post on history of SPARC and its impact to the computing world and speculated on the future of computing.

Since then – on September 1′ 2017, Oracle decided to terminate all SPARC development efforts and laid off the entire development team. 30 years and perhaps 3000+ world class engineers and  >$5B in R&D spend over 30 years, >15 million CPUs built and delivered in Systems from Sun (generating >$100B in revenue) – SPARC flamed out. RIP SPARC.

More significant was one of key member of the initial SPARC development (Anant Agrwal) at Sun, who led the development of SPARC from its initial design through two key inflection points (1984-2002) left 2000+ of his world class engineers and his family on May 28th. A small sample of the Tsunami wave

  • 2000+ careers were launched during his tenure.
  • 10+ companies were founded by the engineers who worked under his leadership.
  • Processors from 1.3uM to 0.65uM CMOS (a decade of lithography feature) with a healthy mix of CMOS, ECL and even GaAS
  • 1000+ Patents created by the team
  • Many industry firsts –
    • RISC 32 bit (1987).
    • Microprocessor in a gate array (1987)
    • Microprocessor SoC (1991)
    • 64 bit processor (1995)
    • Glueless SMP
    • VIS (multi-media extension to FPU) (1995)
    • multi-core & multi-threading (2001)

His departure marks the bookend of the both his life and SPARC – how tragically co-incidental and a  reflection of the wake he left. A befitting bio of Anant Agrawal is posted here  at the computer history museum.

Sharing a poignant image of his family holding his hands around the time of his departure.


                                                                            R I P


VMware moment in storage: V2.0

Back in October’2016, I posted this blog on storage – speculating that we are on the verge on the next big shift in storage – towards a distributed storage platform. Speculated that an emerging company will stand out from the plethora of startups in this space with a unique technology and business model.

18 months later, I would like revise that thesis. What remains true are the following

  1. Yes, the world of storage today follows  distributed system design
  2. Yes, it will have a new business model

What I expected then was a startup company to emerge  and exploit this category. I believe that will be less likely now as the market dynamics specifically the consumption model has changed.

What is in vogue today is the cloud like consumption model. Clearly there is AWS, Google and Azure in the cloud storage space and a good part of storage needs are addressed by them. Which also means they are eating a part of the storage pie (speculating upto 30% of the market in dollar value – perhaps higher in capacity). The notion of storage only software companies are less likely today than then (2016). As the consumption model increasingly looks like cloud like, private cloud is emerging as the new category. That means, storage (distributed)  that is resilient, feature rich and bundled with other parts of the cloud stack. The three stacks that have a chance to gather market share in this space are VMC (VSAN), Nutanix (NDFS) and Azure (Blob storage).

As the enterprises (big and small) shift their infrastructure spend to public cloud and now evolving private cloud, there is little room left for traditional appliance like storage. While standalone solutions from Software Defined Storage (Ceph, MapR, Excelero, Datera, Driverscale to name a few) do have a play, the emergence of VMware cloud stacks (VMC), Nutanix and Azure stack says the market is accepting an integrated solution where good is ‘good enough’. i.e while these storage solutions from the top three private cloud players are architected well, they also meet the ‘good enough’ category leaving little room for any compelling alternatives.

So where do these ideas/companies go next?  Cloud agnostic multi-cloud multi-data access solution that provides customers independence and avoid lock-in is an option. The challenge is competing against the investment of the big three is hard. Focus on emerging mode 2 applications and its needs – containers, KV stores, new data access methods.  Potentially. Perhaps a variant of that, but focusing on the new and emerging memory stacks (memory centric).  One has to combine a unique technology that satisfies a need along with unique business model, disruption will happen.

Again lets revist this in 2020 (18 months from now) and see how much of this speculation becomes reality.

Open Systems to Open Source to Open Cloud.

“I’m all for sharing, but I recognize the truly great things may not come from that environment.” – Bill Joy (Sun Founder, BSD Unix hacker) commenting on opensource back in 2010.

In March at Google Next’17, Google officially called their cloud offering as ‘Open Cloud’. That prompted me to pen this note and reflect upon the history of Open (System, Source, Cloud) and what are the properties that make them successful.

A little know tidbit in history is that the early opensource effort of modern computing era (1980s to present) was perhaps Sun Microsystems. Back in 1982,  each shipment of the workstation was bundled with a tape of BSD – i.e. modern day version of open source distribution (BSD License?). In many ways much earlier to Linux, Open Source was initiated by Sun and in fact driven by Bill Joy. But Sun is associated with ‘Open Systems’ instead of Open Source. Had the AT&T lawyers not held Sun hostage, the trajectory of Open Source would be completely different i.e. Linux may look different. While Sun and Scott McNealy (open source)  tried to go back to its roots as an open source model, the 2nd attempt did not get rewarded with success (20 years later).

My view on success of any open source model requires the following 3 conditions to make it viable or perhaps as a sustainable, standalone business and distribution model.

  • Ubiquity: Everybody needs it i.e. its ubiquitous and large user base
  • Governance: Requires a ‘benevolent dictator’ to guide/shape and set the direction. Democracy is anarchy in this model.
  • Support: Well defined and  credible support model. Throwing over the wall will not work.

Back to Open Systems: Sun early in its life shifted to a marketing message of open systems rather effectively. Publish the APIs, interfaces and specs and compete on implementation. A powerful story telling that resonated with the customer base and to a large extent Sun was all about open systems. Sun used that to take on Apollo and effectively out market and outsell Apollo workstations. The Open Systems mantra was the biggest selling story for Sun through 1980s and 1990s.

In parallel around 1985, Richard Stallman pushed free software and evolution of that model led to the origins of Open Source as a distribution before it became a business model, starting with Linus and thus Linux.  Its ironic that 15+ years after the initial sale of Open systems, Open source via Linux came to impact Sun’s Unix (Solaris).

With Linux – The Open Source era was born (perhaps around 1994 with the first full release of Linux). A number of companies have been formed, notably RedHat that exploited and by far the largest and longest viable standalone open source company as well.



The open systems in the modern era  perhaps began with Sun in 1982 and perhaps continued for 20 odd years with Open Source becoming a distribution and business model between 1995 and 2015 but will continue for another decade. 20 years later, we see the emergence of ‘open cloud’ or at-least the marketing term from Google.

In the past 20 years of the existence of Open Source, it has followed the classical bell curve of interest, adoption, hype, exuberance, disillusionment and beginning of decline. There is no hard data to assert Open Source is in decline, but its obvious based on simple analyses that with the emergence of the cloud (AWS, Azure and  Google), the consumption model of Open Source infrastructure software has changed. The big three in cloud have effectively killed the model as the consumption and distribution model of infrastructure technologies is rapidly changing. There are few open source products that are in vogue today that has reasonable traction, but struggling to find a viable standalone business model are elastic , SPARK (Data Bricks), Open Stack (Mirantis, SUSE, RHAT), Cassandra (Data Stax) ,  amongst others. Success requires all three conditions- Ubiquity, Governance and Support.

The Open Source model for infrastructure is effectively in decline when you talk to the venture community. While that was THE model until perhaps 2016, Open Source has been the ‘in thing’, the decline is accelerating with the emergence of public cloud consumption model.

Quoting Bill (circa 2010) – says a lot about the viability of open source model – “The Open Source theorem says that if you give away source code, innovation will occur. Certainly, Unix was done this way. With Netscape and Linux we’ve seen this phenomenon become even bigger. However, the corollary states that the innovation will occur elsewhere. No matter how many people you hire. So the only way to get close to the state of the art is to give the people who are going to be doing the innovative things the means to do it. That’s why we had built-in source code with Unix. Open source is tapping the energy that’s out there”.  The smart people now work at one of the big three (AWS, Azure and Google) and that is adding to the problems for both innovation and consumption of open source.

That brings to Open Cloud – what is it? While  Google announced they are the open cloud – what does that mean? Does that mean Google is going to open source all the technologies it uses in its cloud? Does that mean its going to expose the APIs and enable one to move any application from GCP to AWS or Azure seamlessly i.e .compete on the implementation? It certainly has done a few things. Open Sourced Kubernetes. It has opened up Tensor flow (ML framework). But the term open cloud is not clear. Certainly the marketing message of ‘open cloud’ is out there. Like Yin and Yang, for every successful ‘closed’ platform, there has been a successful ‘open’ platform.  If there is an open cloud, what is a closed cloud. The what and who needs to be defined and clarified in the coming years. From Google we have seen a number of ideas and technologies that eventually has ended up as open source projects. From AWS we see a number of services becoming de-facto standard (much like the Open Systems thesis – S3 to name one).

Kubernetes is the most recent ubiquitous open source software that seems to be well supported. Its still missing the ‘benevolent dictator’ – personality like Bill Joy or Richard Stallman or Linus Torvalds to drive its direction. Perhaps its ‘Google’ not a single person?  Using  the same criteria above  – Open Stack has the challenge of missing that ‘benevolent dictator’. Extending beyond Kubernetes, it will be interesting to see the evolution and adoption of containers+kubernetes vs the evolution of new computing frameworks like Lamda (functions etc). Is it time to consider an ‘open’ version of Lambda.

Regardless of all of these framework and API debates and open vs closed –  one observation:

Is Open Cloud really ‘Open Data’ as Data is the new oil that drives the whole new category of computing in the cloud. Algorithms and APIs will eventually open up. But Data can remain ‘closed’ and that remains a key value especially in the emerging ML/DL space.

Time will tell…..

On Oct 28th, IBM announced acquisition of RedHat. This marks the end of open source as we know today. Open Source will thrive, but not in the form of a large standalone business.

Time will tell…

30 (15 and 7) Year Technology Cycles


We all have heard the 7 year itch. Have you ever asked why its 7? Why it is not  10 or 4. Looking back,  I have had those 7 year itches.

Have you had the urge to leave your job or change your role significantly around 15 years? Think about this  very carefully or ask around!! I left my first job after 16 years

What about 30 years. Perhaps time to retire. Or seek a completely new profession?

Now lets look at businesses. Lets review two examples.

  1. Apple: From 1997 to 2007 – it was the Apple IIe.  7 years after founding, 1984 (MAC). 7 years later (1991 – Powerbook).  7 years later (Steve is back with iMAC). 30 years since founding (iPhone). New product, New Business model. Apple re-invented. (
  2. Sun: 1982-1989 – Motorola + BSD Unix. 1989-1996 – Solaris and SPARC. 1996-UltraSPARC 1 and Java. 2009 (27 years), acquired by Oracle.

Why 7? Why 15?  Why 30? The 7 year itch feeds into the 15 year peak which feeds into 30 year retire/re-invention cycle. Since businesses are made of people, especially in the technology sector, business cycles have the same 7, 15 and 30 year cycles.  At 7, one becomes proficient in a domain (10,000+ hours) and change is in the air. At 15, you feel like you reach a peak. At 30 you become obsolete (retire) or re-invent.

At the core of these 30 year transitions, the DNA has to be relevant or one ‘retires’. The biological DNA that define our value proposition, businesses  do as well. Apple has the core DNA of designing eye candy products (materials +  UI). This has transcended to the desktop to the mobile.  IBM has the DNA of general computing business. That has unchanged over decades.  Sun had the DNA of Open-source and open systems. Intel has the core DNA of device engineering and manufacturing (Moore’s law).

The similarity has to do with human productive cycles. While this assertion is not statistically accurate, but there is enough data to suggest why there is a linkage between business cycles and human productivity cycles.

First lets visit some 30 year cycles…In most cases the business model changes – not just technology. Usually the incumbent sees the technology shift and can handle it. But they fail to see or react to the business model change.


  1. IBM: Strangely IBM has revived itself every 30 years. There was mainframe from 1950s to 1980s. Then there was PC and Global Services from 1980s to 2010s. Now its on a re-invention cycle with Watson and Enterprise Cloud. IBM has done this over its 100 year history repeatedly and perhaps the only one. But its facing that issue once more…Time will tell…
  2. Apple: From 1997 to 2007 – it was the Apple IIe.  7 years after founding, 1984 (MAC). 7 years later (1991 – Powerbook).  7 years later (Steve is back with iMAC). 30 years since founding (iPhone). New product, New Business model. But same DNA. 1992 was a critical year as we all know. 2014 is 7 years after the first iPhone. Another change cycle? The smartness with Apple was the move from shrink wrapped software to Appstore. New business model. Fantastic!  The iPhone which is 10 years old will reach its peak the next few years.  Apple will have to have its next big act before 2021 ?
  3. HP: Another survivor has gone through two  30 year cycles and in the middle of the 3rd. Remains to be seen how it will evolve


  1. Sun: My Alma mater. Started in 1992 and dies or got gobbled up in 2010. Perhaps lives inside Oracle – not really. Again 28 years and died. Founding DNA = Opensource + Interactivity and big memory. Lost its way when Linux took over (Opensource – around 1998 – 15 year!!!). 
  2. DEC: 1957-1987; VAX9000 remember. That was the beginning of 30 year run. Got gobbled up and could not handle the next business transition
  3. Many other companies fall into this category (SGI…..)
  1. Cisco: 1984-2014 (30 years). The networking category is being challenged in a similar way VMware came and altered the server market with Xeon. Commodity switches + SW. Other problems. Good news for Cisco in that it has lots of cash. Needs to find new growth categories that are significant. They are trying with a new CEO and moving upstream.
  2. Intel: Intel got out of the DRAM business in 1984. Now its facing challenges in its core having missed the whole handheld and tablet market. Again another 30 year transition cycle for Intel 
  3. Microsoft: 1997-2007. Windows Vista, Windows 7 Windows 8 – not a growth engine. Missed the phone. Failed to grasp the new business model. With a change in CEO and now the shift to the cloud, albeit late, but seems to have made or making the transition. Its a work-in-progress.
  4. Oracle: The 30 year old database market has run its course. The shift to in-memory and more importantly the shift to the cloud (AWS/Aurora and Google/Spanner) combined with the sedimentation of the plumbing layer (how many database companies are out there). The challenge for Oracle is not technology (which it can by acquiring the next viable business). The shift is in the move of the enterprise from licensed software to the cloud model. 
There is a pattern in this 30 year cycle. Typically a technology and a business model shift occurs and the companies. As you can see very few have done the 30 year transition. If you look deeper there seems to be a 15 year sub cycle and a 7 year sub cycle.
Guess what Google is past the 15 year cycle. Amazon founded in 1994, had its second act starting around 2008 (AWS).
1980s  was the golden era for  many tech companies
–Computing (Intel, Sun, Apple, Dell…)
–Networking (Cisco, 3COM)
–Storage (EMC, Netapp later)
Common Theme: Business model of selling integrated systems (OEM model)
30 years later that model has turned upside down… We are into the the next 30 year cycle with the cloud.
I touched upon this topic in one of the UC Berkely (2014) Aspire retreat(s). An image of a handwritten slide is shown below.  This time like in 1984, when DRAM gave way to logic (Intel), new memory technology will drive the new technology stack and thus new business opportunities. Maybe or atleast hopeful of it. This will be the topic for the next post…
Meanwhile  – what comes after 7 15 30 ?
Coming back to 30 year technology cycles and individuals – is there a link?
Like to hear more about it from others..

Tesla – Chemistry & Intelligence

My 2030 Prediction – Tesla will be valued at $1 Trillion and will be valued for 2 key technologies that will have a significant barrier relative to others. Chemistry and Intelligence (Machine Learning). While Tesla has motors in its logo today and will continue to build motors and cars and other automotive and transportation (electric) vehicles, I see its future has less to do with electric motors or even solar as the key driver of the company core value/IP and thus its business proposition.

At the core of what Elon is building and ‘hard to do’ is battery chemistry and more interestingly is machine learning or intelligence.

Let me start with the simpler one first and the more obvious one – Chemistry.

It’s well known Tesla’s $5B factory in Nevada will be the driver of electric vehicles as we know and Elon as well as Tesla will continue to master the battery chemistry at scale for cars, trucks, home and anything anywhere that needs storage. While there are more efficient forms of energy storage, and the issue of supply of Lithium, cost of battery and efficiency growth of 5-7%/year are issues,  the sheer momentum and size of market with cars and trucks will enable Tesla to have to engineer better batteries (i.e. chemistry)  as it is at the core of value proposition of all electric vehicles. There are a couple of other side effects. First the oil glut will get worse and I expect it to start around 2018 and be in full swing by 2020.  The big shift in the entire value chain of the internal combustion eco-system will start around the same time. We will need fewer gas stations. We will need fewer auto service shops. With Tesla model of Apple style sales going direct, it will have an impact of the overall employment workforce as well.

The second more interesting one is intelligence or machine learning or AI as its called  today.  We are at the Cambrian explosion era for machine intelligence or machine learning. Clearly the autonomous car is the driver for it. What is unique about Elon’s and Tesla’s approach is that the car is the best vehicle to be on the exponential curve of this technology evolution. If you consider the parallels with biological evolution, survival required better audio and later visual perception. The advent of mobility ( one of the inflection points amongst many) of the biological organisms leading to rapid evolution of the visual cortex which holds highest percentage of the brain’s volumetric space. This evolutionary growth while driven by the need for survival was a big trial and error experiment over perhaps a few billion years and out of that sensory path all kinds of decision making processes were developed.

So the assertion here is that Autonomous car is going to drive machine and deep learning tools and techniques more so than any other platform. That will include chips, platform (HW), platform (SW) and more importantly algorithms and decision processes need to meet and perhaps exceed Level 5 standards or approach a human and perhaps exceed.

Tesla has a lead on this over others including Google as they have now >100K vehicles on the road. Iterating on this and soon by 2018 going upto 500K and soon after 1M.  That will be a key inflection point – analogous to the  million eyeballs or developers being key for a platform success in the 1980s or 1990s (windows , mac and solaris were #1, #2 and #3 and all others dropped off), 100M for the web/social era,  the same will hold for autonomous car driving the platform capability for ML/DL.  Now lets contrast Tesla’s approach to Google. Tesla is taking an iterative, real world approach.  Waymo i.e. Google is trying to build the best autonomous system first. We know the latter is harder and likelihood of market success is linked  to multiple factors over time (except for Steve Jobs who knows how to build the perfect platform before he releases one).

So the Tesla approach is solving the hard ML/DL problems using the car as the ‘driver’, while the google approach is taking a platform (ofcourse solving Google’s other applications) approach. What’s interesting is ‘intelligence’ exploded in the biological evolution with mobility linked to survival and thus mobility became a critical ‘driver’. There are many other drivers of ‘intelligence’ but for the near term – this is a key ‘killer’ use case to drive platform evolution with significant business value.

So if we take the evolution of battery chemistry of the next 14 years and the evolution of autonomous system including the level 5 decision making system which is mobile and making real time decisions at speed and criticality of the ‘goods’ it carries, I have to posit that the core value of Tesla in 2030 will the chemistry knowledge of the battery and the ML/DL platform that it will build. Behind the ML/DL platform will be silicon for processing in the car, the data (and energy) storage, the intelligence at the car level, the big data hub or data center that each car will provide and on and on and on…The Android vs iOS analogy applies. One took a platform approach to go to market and the other took a vertical integration approach.

So, while Google will drive the developer adoption of ML/DL, quietly but surely Mr Musk (and thus Tesla) could well emerge as the leader and in delivering the industry leading ML/DL platform. It won’t be restricted to just automobiles or transportation.  Like biological evolution, its a winner takes all game. Homosapiens killed all other forms.  Expect Tesla to drive this platform and that might well be its monetization model.

I thus speculate, It will be in the battery chemistry and the platform intelligence that will be at the core value of Tesla’s drive to a $1 Trillion in valuation by 2030.

Block Device is dead. Long Live the block device

Matias Bjorling  in the paper he co-wrote in 2012 calls for the necessary death of the block device interface in the linux kernel as we know it.    Flash was just emerging as a storage tier in enteprise and infrastructure IO stack back then.

Going back to the era of the creation of block device abstration in Unix (late 1970s – early 1980s), POSIX (IEEE standardization efforts) also published file and directory access APIs that are OS independent.  Around the same time 3.5″ HDD (Circa 1983) came into existence that enabled both the PC and the workstation form factors.  The operating system level abstraction and the IEEE standardization process enabled storage to be segregated as a set of well defined APIs resulting in storage as an industry – which over the past 30 years is more than $50B in size.

30 years later, the flash entered the enterprise or infrastructure segment. Around the same time a number of KV stores have emerged that have tried to map application use cases (NoSQL, databases, messaging to name a few) to flash and used variety of KV abstraction APIs to enhance the integration of Flash in the platform.  Around the same period, we have also seen object stores emerge and the cloud and S3 has emerged to be a default standard effectively as an object store, specifically to users of AWS.

With the emergence of the NVRAM (or 3D xpoint), the reasons outlined in the paper and the rationale are even more obvious. Until recently, I believed that a well defined and designed KV store is the new ‘block device’. While that remains true, without the standardization process, it will never have wide acceptance or become the new ‘block device’.  Similar to late 1970s, there are three things that are forming the storm clouds to posit the new block device. They are

  1. Emergence of 3D Xpoint or SCM as an interim tier between memory and flash which has both memory semantics as well as storage (or persistence) semantics
  2. Emergence of S3 as a dominant API for application programmers to leverage cloud based storage and in general S3 as a dominant API for today’s programmer.
  3. The need for a POSIX like OS independent (today you will call it cloud independent) ‘KV store’ that addresses both the new stack (SCM + Flash) as well as handles latency and throughput attributes that these new media offer that would be otherwise limiting with the old block interface

Its obvious that the new storage API will be some variant of a KV store.

Its obvious that the new storage will be ‘memory centric’ in the sense that it has to comprehend the SCM and Flash Tier as the primary storage tiers and thus adhere to latency and throughput as well as failure mode requirements.

If the new interface is necssarily KV like, why not make  ‘S3 compatible’ interface for the emerging new persistence tier (SCM and Flash).  Standardization is key and why not co-opt the ever popular S3 API?

AWS has a unique opportunity to re-imagine the new memory stack (SCM, flash) and propose a ‘high performance’ S3 compatible API and offer it as the new ‘POSIX’ standard.