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It’s 02/02/2020. Almost 3 years back, I posted this on Tesla – at the core its chemistry and intelligence. In many way, Elon and crew are well on their way of executing on both topics of speculation.

Battery Chemistry/Tech: The roadmap to hitting $100/KWh is well on its way and Tesla is leading it from all fronts. Manufacturing at scale, improving density with the acquisition of Maxwell and Hibar and of-course internal efforts. I love the way Jim Keller (another fellow chip nut) calls out Elon’s law “Figure out the optimal configuration of atoms first and figure out a way to put them there”. I love this quote. The quest for chemistry is deep rooted in principles of physics as applied to engineering and thus eventual value extraction. We have entered the knee of the curve with Tesla approaching and crossing million cars on the road in both being able to invest in chemistry and vice versa chemistry aiding the company to drive down the cost and range and thus value. Of-course distance itself from its competitors.

Artificial Intelligence: The million cars and 14 billion miles driven with partial autonomous driving is greatest field based iterative improvement in machine learning that is unmatched. Coupled that with brilliance of tech leaders in algorithms (Andrej Karpathy) and Silicon Design (Pete Bannon) and approach to machine vision and now using the currency, name and leadership to attract the best and brightest. Machine vision is the Tsunami will lift all ML boats and between the need for autonomy, the talent and approach (fast iterative design cycle), despite the billions of dollar investment .in other ML chip outfits by VC, this is going to standout. Who knows there might well be the next Data Center chip embedded in Tesla (Recall the wimpy 4004 calculator chip became the data center chip 25 years later).

So in Feb 2020, we have euphoria about this stock. Back in June 2017 the stock was at $353 and has been oscillating until now. Its unclear why its zooming this fast exponentially but a few events have happened.

The realization that competition is nowhere near Tesla’s manufacturing scale (Fremont and Shanghai and soon Berlin) with step and repeat like silicon fabs model (not copy exact like Intel did but copy similar), the cost of battery coming down, the excitement of the product roadmap (depth and breadth) [Can’t wait to get my hands on Cybertruck] and leadership in machine vision, we might have crossed the knee of the curve of the market opportunity/potential that the analyst and investment crowd are now jumping in like herds (as they always do).

While there is euphoria and the stock will go up and down (with Musk’s utternances and distractions), the roadmap to $1T and beyond seems more plausible.

There many lenses through which to look at this company today – but I look at it this way. Its better than Amazon. Its better than Alphabet and might be even better than Apple in the long run except in one dimension. Its better because inside today’s Tesla there is

• A car company
• A Battery company at planet scale
• An AI/ML company (machine vision in particular)
• An electric storage company
• An Electric Utility company (low value but at scale gets interesting)
• An energy distribution company
• A potential Cloud or computer company (if a book store turns into cloud computing, an autonomous car can have the right assets for becoming a cloud company)
• A big data/mapping/navigation company
• A carless car company (i.e. Uber/Lyft killer robotaxi)
• A machine vision driven robotics company
• and more to come….(more than letters in the Alphabet)

The only limiter to realize all these companies – management bench strength to support Elon.

This is a half time report/summary. We have gone 3X in market cap in 2.5 years. The next 3 is going come faster than we can see…

Next check point 22/02/2022. Until then watch and load up on TSLA.

Update – 10/21/20 – Elon Musk commenting after Quarterly Results

https://finance.yahoo.com/news/tesla-chain-startups-elon-musk-222707290.html

In 2017, I posted Cloud and Fabs here. I grew up in the semiconductor world so the comparison was obvious, but little did I know that my assertion would come true – i.e. a 2 player game as in semis (TSMC and Intel) will be played out in the cloud. I assumed the 2nd player was going to be Azure or GCP. Now it looks more like AWS(Intel) and TSMC(VMware) by way of analogy (fabless vs vertically integrated models).

Since January 2017, a lot has happened. Google Cloud has seen management shuffles, AWS continues it growth and Azure picking up steam. The industry pundits have written off on premise and I like many drank the Kool-aid that cloud first is the way to go. Cloud first as a developer makes total sense, but cloud first as a deployment model is evolving starting with 2019. I went to a few Google Nexts and was awed by the investment ($30B+) and technologies (ML). I went to AWS invent and was amazed as to see how Andy Jassy spout out technology and features like a CTO and if Jeff Bezos blinks could become Amazon CEO as well. The litany of features they throw at the customers has been shocking to the extent most silicon valley VCs ran to the hills when Amazon announces products. Startups could not compete with Amazon. That has not happened in Silicon Valley. Twice I started on version 2 of this blog and never completed thankfully as 2019 is the inflection point for a tectonic shift. Industry analyst after industry analyst predicted that the march to the cloud will consummate by 2025 and there will be no on-premise or only 20% of the cloud will be on-prem. I did too.

I predict by 2025, the private cloud (on-prem) / public cloud ratio will be a healthy 60/40 or 40/60 depending on whom you believe. That is a radical statement i.e .going against the tide? Why the shift or change of viewpoint?

Around the same time in 2017, I was introduced to Nutanix. Nutanix addressed two things that cloud infrastructure did from the start. Make storage invisible and ease of use or consumption just a few clicks away. Since 2017, Nutanix has seen another company creep up. Enter VMware. With the recent announcements with their project pacific and the re-tooling of the Day 0, 1 and 2 automation in VMware stack, both VMware and Nutanix have addressed key value propositions that the public cloud infrastructure companies provided . It’s cost / VM and ease of consuming a VM. If all you need is a commoditized VM the distraction of Openstack clouded the closing of the gap by Nutanix and VMware.

In 2019, any enterprise or private cloud that has a need to consume say 500 – 50K physical machines (10K to 1M VMs) of sustained use, doing the TCO analyses will show you, your private instance is significantly cheaper and the value prop of ease of VM creation and its life cycle. Here’s a simple visual of the shift in the value proposition. It’s not capex depreciation as the cost of sourcing server, storage and network is within 10-20% of hyperscalers if you are at moderate scale. Its the Opex – the total # of people deployed to run the infrastructure. That is the change event visually described below.

This is a qualitative chart, but its an attempt to make the point that the value to the end user was cost/VM (low) for entire life cycle of a VM (fast startup time, no operational overhead). The solid lines are for VMware and Nutanix and the dotted lines are for AWS, GCP, Azure. Sure, the big three have some form of a private cloud option – the usage of hyerpscaler stack for on-prem will be challenged by the complexity of the deployment and management!! Its different. The same reason why EMC won the storage business in the 1990s (most enterprises wanted to buy storage from a vendor independent from the server OEM). Same rationale applies.

VMware is pulling further ahead with their recent announcement at VMworld 2019 with tight integration of Kubernetes, GKE like a small light weight linux kernel embedded in the hypervisor to provide close to bare metal performance. The gap between the hyperscalers and these private cloud stacks (VMware and Nutanix) have closed with the cost advantage going to the VMware or Nutanix on a per VM basis. This implies for a range of enterprise users (mid size – 500 to 50K phyical machines) the reasons to go to public cloud for cost or ease of consumption is disappearing fast. Attraction to using public cloud depends on location, Opex vs Capex, simplified procurement and availability of other services (ML, lamdbda etc) – but for a vast majority of applications (by some count there are 150M VMs running on-prem while only 30-40M VMs are running in public cloud), I think we will see the stemming of the tide to move to the public cloud (hyperscalers).

VMware due to the market footprint is going to be a dominant player to serve the private or on-premise space. Would not be surprised this becomes an AWS vs VMWare game by 2022? What are the implications of this shift?

1. Its going to be harder for Google to compete with VMware emerging as an option to stem the tide to move to public cloud. To recap, there are big three (AWS, Azure, Google) and Medium two (VMware and Nutanix) private cloud stacks.
2. VMware is big enough to enhance its portfolio to include PaaS layers and other cloud native services and orchestrators including functions, lambda like serverless, NoSQL DB etc.
3. This cements the death of OpenStack (as it has at SAP) for the enterprise. Might live in the Telco segments.
4. The last 5 years, we saw the death of open source, as the public cloud companies (notably AWS) just resold open source via AWS EC2 compute cycles. They made more money than all the open source developers combined.
5. The last 5-8 years we saw the infrastructure investors run to the hills. Most VCs feared to compete with AWS and except for a few tuck-ins, the big three basically wiped out significant portion of the VC investments in infrastructure technology.

All that is poised for a change. VMware is now emerging a strong alternative to hyperscalers. Kubernetes integrated in their stack, immediatey their customer base gets it, which is huge. 150M VMs in the wild have no reason to move. IT jobs (some) are preserved. Not all. The ‘edge’ loosely defined is emerging as another big category of its own. The interesting aspects of this shift are

1. Rate of innovation in infrastructure has slowed down in the cloud due to their sheer size. One cannot introduce a new tech (memory, FPGA, GPU) fast enough in the public cloud as their hurdle rate is high. The byte sized enablement of new tech is easier or more economically viable with the on-prem. VMware (or Nutanix) can enable that faster than a public cloud can.
2. That means a rich eco-system of Infrastructure startups now have a friend in VMware (or Nutanix). They can find a way to fit into that eco-system as the needs of the customers are varied. Hopefully VMware will recognize. In some sense VMware can enable hardware from different vendors today. So a new technology platform could be fast tracked with the VMware eco-system (if VMware desires so) than a public cloud channel.
3. The perfect storm of new memory (3D Xpoint), new compute in ML (GPU, FPGA, TPU) and the new networks (200G/400G that addresses the micro-second transaction system) will find a faster path to market via this eco-system than the Public cloud. This implies VCs can bet once more on infrastructure startups.
4. Distributed computing as we know is ripe for change. Its become too hard for the average programmer to build a distributed application. At the core its the assumption that infrastructure is fragile and networks are broken. If you change those assumptions (some startups are rethinking the fundamental assumptions), it will make disrributed systems simpler. The challenge of the killer microseconds very well explained by Google (Barroso, Partha et. all) is going to turn the infrastructure on its head. Will that innovation happen first in the hyperscalers or in byte sized chunks on-prem. The latter while not capitalized has a better chance as diversity of ideas (startups) will win over centralized innovation inside hyperscalers. This is a contrarian view – but I think its a bet I am willing to propose.

Three Final points.

1. If there was a ‘Cloud first stack’ the past 15 years, there is emerging an ‘edge first stack’. Edge first means one has to solve a federated system that is constrained in space, power and new compute and data management paradigms.
2. Infrastructure is back – i.e. innovation in infrastructure that can be channeled to on-prem enterprise customers via VMware (or Nutanix) will be a significant option for entrepreneurs, VCs , customers and traditional OEM players and to semiconductor companies like intel, Micron and Broadcom.
3. TSMC is a fabless semiconductor company contrasted with a vertically integrated company (Intel). Today both have a market cap of $225B. 10 years, back it would have been unthinkable of TSMC achieving this status.

I think the same might happen in the cloud. AWS (and thus Azure and Google) are becoming more vertically integrated. AWS is doing a lot better than other two to serve a wide range of customers, but with nitro and other silicon efforts, they are getting deeper into owning the infrastructure stack. Here comes VMware like TSMC as a infrastructure-less cloud company?

The shift: Just when you thought the world has ended, it opens up again. 3 years back, I thought VMware dead on its track with the industry move to containers and Kubernetes. I was wrong. Keeping fingers crossed as the innovation of startups need a friend to channel their output.

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

                                                                           Anant

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.

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. (http://applemuseum.bott.org/sections/history.html)
   
2. Sun: 1982-1989 – Motorola + BSD Unix. 1989-1996 – Solaris and SPARC. 1996-UltraSPARC 1 and Java. 2009 (27 years), acquired by Oracle.  http://devtome.com/doku.php?id=timeline_of_sun_microsystems_history

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.

Survivors:

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

Recycled

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…..)

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 be the battery chemistry knowledge 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.

• Status update on the predictions:

April 2019 Update: https://electrek.co/2019/05/03/tesla-tsla-value-grow-10x-500-billion-self-driving-tech-elon-musk/

Autopilot Hardware Update: https://electrek.co/2019/04/22/tesla-full-self-driving-computer-details/

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.

This is my personal diary of leadership attributes that I have learnt and accumulated over my professional career. I thought I would share this and be as a reference of who I am or might want to be. On many of these attributes, I have real experiences of being tested and tried and still believe while I have gained some muscle memory on these, many are still work in progress.

This is also a summary from 2 letters I had to write to my Son when he finished middle school and finishing high school. I had to pen some ‘lesson learnt from parent to child’. I find the list is also a list of key attributes I care as a leader and wanted to pass it along to my family (immediate) and if the opportunity presents itself with my friends as well.

My role models or people from whom I have learnt important lessons that I remind myself constantly  are  Scott McNealy, Steve Jobs, Colin Powell, Ronald Reagan, Jeff Bezos, Ed Zander, Elon Musk, Vinod Khosla, George Pavlov. With that, what are the key attributes?

Conviction: A strong belief in something leads to conviction that takes care of fear, worries of failure and even failure. Steve Jobs is a role model.

Change Agent:  Leaders tend to change status quo, By that, I mean if you want to be a leader, expect change and change.  Scott McNealy was a change agent and he is/was a  role model for this.

Responsibility: Being a leader means being responsible. With assumption of responsibility, a lot of other skills can be enhanced or acquired. Almost all of them have this key attribute.

communication. Observe everything. Take all the inputs and be able to synthesize. All of the names above are good at it. Ronald Reagan, Colin Powell to start with.

Taking Risks: As the famous saying goes, there is no risk, then there is no reward. To me, risk is not just about reward, but its about experimentation and learning early enough. Its important to fail early and soon than later which is harder. So take risks. The person who epitomizes this for me is Vinod Khosla.

Perseverance: Conviction + perseverance is what takes one to be successful (Just make sure you have people to tell you about your blind slides and listen to them).  The person who epitomizes this for me are almost everybody but two in particular – Elon Musk & George Pavlov – a colleague at a former company.

Think big, but act small: Its actually TBASIF – Think Big, Act Small, Invest Frugally.  Dream the big things. But learn to find the simple, easy starting or insertion point. The person who epitomizes this attribute are Elon Musk and Vinod Khosla.

Sell, Sell, Sell: An entrepreneur is selling all day long to everybody. Selling to his friends, colleagues, investors, board members, employees and his customers and most importantly to himself as well. We all need to be sold or reminded as well. Most of the above names are – notable ones being Jeff Bezos, Elon Musk and Steve Jobs.

Complainer vs solver: If you complain all the time, you will never be able to lead. If you lead a group of people and are constantly solving their problems, you are not a leader. Tell them to come up with solutions not problems. Become more of a facilitator. Perhaps Colin Powell.

Believe in your gut: As Ed Zander the former COO of Sun Microsystems said – life is all about refining your gut. I think nature has built our chemistry in a way that the complex pro/con analyses results in a simple answer and that answer is reflected in driving some hormones in your stomach (perhaps from the survival instincts in early years of evolution).  When you go with your gut, you feel good. So when you come to cross roads in your decisions, ask you gut and check infrequently how you did against your gut.

Decisions: The first decision is the right decision. The second decision is worst decision.  The third decision is no decision – Scott McNealy.