Moore No More – (Part IV – Computing Epochs)

The next Tsunami….

We overestimate what we can accomplish in 2 years and under-estimate what we can in 10 years

Bill Gates

“We are at a time of enormous transition and opportunity, as nearly all large-scale computing is moving to cloud infrastructure, classical technology trends are hitting limits, new programming paradigms and usage patterns are taking hold, and most levels of systems design are being restructured. We are seeing wholesale change with the introduction of new applications around ML training and real-time inference to massive-scale data analytics and processing workloads fed by globally connected edge and cellular devices. This is all happening while the performance and efficiency gains we’ve relied on for decades are slowing dramatically from generation to generation. And while reliability is more important than ever as we deploy societally-critical infrastructure, we are challenged by increasing hardware entropy as underlying components approach angstrom scale manufacturing processes and trillions of transistors.”

Amin Vahdat on Google on SRG creation

We are at the cusp of another systems evolution and rebuild of the entire stack. We have entered the 4th epoch of computing at roughly 20 year intervals, with

  • Epoch 1 1965-1985 (mainframe , CRT terminal (VT100))
  • Epoch 2: 1985-2005 ([Workstation, Unix Servers], [PC, Mac])
  • Epoch 3: 2005-2025 (Cloud, SmartPhone)
  • Epoch 4: 2025- (TBD, TBD)

It roughly follows Moore’s law in terms of impact to systems as shown in the visual below.

The onset of next wave is both obvious and non-obvious based on your vantage point. It was not obvious in 1980 – Ethernet, NFS, RISC, Unix let Sun (and perhaps PCs) and eventually big SMP boxes was the end game along with it Sun’s demise starting the end of dot-com bust. Technology stack drove the use case but they drove out the mini and main frame. So Monolithic to disaggregated back to monolithic (or perhaps modular) within 20 years!!! The business model then was OEM (Capex). Unix/C/RISC was the tech stack at the infrastructure level and use cases or killer apps were HPC, EDA and eventually shared everything databases (Oracle) and 3-tier enterprise apps (SAP). In a prior SoC to SoP blog I mentioned the emergence of cheap servers from both Sun and ODMs – but Sun failed to capitalize on that early lead and trend as it was beholden to margin over market share – a point Martin Casado makes about Dr. Reddy Labs . A classic reference

By 2000 we saw search (Google, 1998), e-commerce (AWS, 1995), hypervisor (VMware, 1998), distributed systems (1U rack mount servers), 10G networks, distributed storage (S3 – 2007) and then cloud happened with that and more. This time use cases drove the infrastructure stack and the incumbment i.e. OEMs (Sun, HPE, DEC etc) missed that transition and some eventually disappeared with last man standing effectively being Dell through consolidation, low cost supply chain (market share over margin) and financial engineering (well executed!). Again disaggregated and now back to monolithic (hyper scaler clouds of today). The business model with Cloud being Opex. (Linux+VM/Java,javascript,c,…/x86) is the tech stack and all the new consumer and enterprise SaaS apps.

The tides of complexity and simplification is on its march again – but this time its use case and technology coming from both ends and there will be new winners and many loosers. Martin’s blog on trillion dollar paradox is a leading indicator of this shift and the pendulum is swinging back to the middle between the old OEM/on-prem model and hyperscale cloud opex model to something new. I am guessing there are healthy mix of agreements and disagreements with this shift inside these hyperscalers. Just when you think they are too big to disappear, think again – every cycle leaves a consolidated entity eventually and new players emerge. To quote Clayton’s innovators dilemma

The third dimension [is] new value networks. These constitute either new customers who previously lacked the money or skills to buy and use the product, or different situations in which a product can be used — enabled by improvements in simplicity, portability, and product cost…We say that new-market disruptions compete with “nonconsumption” because new-market disruptive products are so much more affordable to own and simpler to use that they enable a whole new population of people to begin owning and using the product, and to do so in a more convenient setting.

Clayton Christensen

The questions to answer are –

  • What is the new infrastructure stack
  • What is the new business model
  • What are the new use case drivers
  • Who are these new customers?

Its fascinating to watch Cloudflare which reminded me of Akamai and Inktomi back in 1998 that led to 1U/ small server (@Sun ) – but to be only ignored by the $1M sales GMs. They took the company down with their margin over market share mentality as they were drunk by the fish falling into the boat during pre dot-com days. Strange 20 years later replace Inktomi or Akamai to Cloudflare and emergence of new category of consumption and deployment is the rising tide that is going to lift some new boats and leave some behind.

Fast forward, Its near impossible to predict which technology and companies will be torch bearers by 2030, but we can start with what we got. A few things to keep in mind.

  • Storage: Cloud was enabled by foundational under-pinnings in storage (GFS, S3) – distributed storage. 1980s systems as anchored by Sun Microsystems was successful because of NFS. So a common pattern with emergence of new stack is storage is an important and critical tier and those who got it right tend to rule the roost (Sun, AWS).
  • Network: Over the 2 epochs, networks (TCP/IP) dominated with Ethernet winning by Epoch 1, riding the Silicon curve in 2nd epoch and now we are at the cusp of breaking it. Its no longer a unitary world. The network was the computer in Epoch 2. It was even more important in the cloud era and its going to be much more critical in the coming epoch – but its not the same networking model as you would think though.
  • Compute: ILP (Instruction level parallelism) was the enabler in Epoch 2 via RISC. TLP (Thread level Parallelism) was enabler for Epoch 3 (Multi-core / threading) and thus poses the question – what is this wave going to present itself with. IPU, DPU, TPU are all names for coherent “datapath’ accelerators and that boat is sailing.
  • OS: Unix was the hardware abstraction layer that gave way to hypervisors that is giving way to K8s and I would say more like Serverless/Lambda i.e. you need both a HW abstraction and resource management layer.
  • Developer Abstraction:
  • Asset utilization: Turning cost line items in your P&L and turning into business is Amazon’s claim to fame. Asset light vs Asset heavy arguments (Fabless vs Fab arguments). So taking one use case paid for by a class of customers and leveraging that asset investment into another business is common practice and cloudflare is playing that game. This is an important element of disruptions and disruptors. Famously – Scott (Sun CEO) used to implore on some of us – investment of SPARC is already paid for – why not open source or give it away? Looking back 20 years later he was right, but not taken seriously or executed.
  • Marginal value of technology and value extraction: The cost of transistor going to near 0, led to the entire computing platform in the 1980s and 1990s. The cost of transporting a byte going to zero led to the internet and other upper level services and value extraction. The cost of storage (HDD) going to zero led to the cloud (Yahoo offering mail storage, Google Drive and S3). The next wave is going to be cost of transaction or inferencing going to zero will lead to the next set of disruptors and value extraction.

What is the new infrastructure Stack?

Network is the computer starts with the rack: This has been around for sometime, but finally a confluence of technologies and use cases i.e. a perfect storm is brewing. Like the beginning of every epoch, we are starting with ‘disaggregated systems’ (or perhaps modular) the computer and the computing platform starts with refactoring the computer as we know to simpler forms and assembled at upper layers of the stack to enable the evolution of lower level components. The physical rack as a computer is not new, but the software layers to make that viable is more needed and viable now than before. It starts with basic building blocks which are now SoP ( systems on a package ), CXL (Computer Express Link), (x)PU and emerging memory components. At the height of the moore exponential (epoch 2), the CMOS roadmaps drove higher perf, lower cost and lower power all three. Like the CAP theorem, now we can get only 2 out of 3 and to keep on the same Moore curve, SoP is one of the ways to achieve that same performance curve. Given more data parallel workloads, performance is achieved over more silicon area, power is distributed over a larger surface (density) and cost is managed by re-use of many sub-componets. Its expected by end of the decade the total cost and power as % of the single system dedicated to ‘control path’ execution will shrink as the ‘data path’ component will grow driven by ML/AI and other emerging verticals.

Systems on a package is enabler of disaggregation of the system. It starts out with addressing performance, cost and power, but when combined with emerging memory tiering, CXL at a rack level its a key enabler of disaggregating a single node, but aggregating at the rack level.

Three trends – Memory is getting disaggregated, computing is going more datapath and disaggregated to meet the scale requirements (Nvidia with NVlink and A100, Google with TPUs and IB), Network is going 2 tier to handle latency vs bandwidth di-chotomy and new frameworks are getting established to be the contract with the application developers. Lets explore each one in a little bit more detail. The physical disaggregation has to be aggregated at the software level i.e.

Memory vs Storage: While memory has been tracking Moore’s law, scale of data processing is much larger in scale and thus the emergence of Hadoop and other distributed computation embedded within storage model. But we are reaching a point with emergence of interconnects like CXL enabling coherent tiered memory with manageable latencies (1-3 NUMA hops) but significant increase in capacity. When combined with new workloads that demand not infinite storage space (consumer SaaS), but moderate size (some enterprise SaaS, edge, ML, ADN to name a few), but need much higher performance (latency) that we can re-imagine systems at the rack level with memory only access modes with associated failure domains handled entirely in software.

A visual of that is shown below.

By mid-decade one can imagine 2+ PB ‘memory’ semantic with tiers of memory enabled by CXL interconnect. Any page in such a locally distributed system (hyperconverged is the wrong word – so not using it) can be Sub uS. There is tremendous investment in PCIe, but the economic motivation for these new apps are the drivers for memory-centric computing model. At moderate scale handling (tail) latency is also of value. (Ref: Attack of the killer microseconds)

Coherent Co-Processors: Historically accelerators have been in their own non-coherent space (starting with GPUs ever since graphics has been a category) and ofcourse NICs and and FPGAs and some specialized HPC accelerators. Coherency and ‘share-able’ memory simplifies the programming model significantly and the great unlock is CXL as until now in the mainstream market, Intel did not open up their coherent interface to other providers for some very good technical and perhaps business reasons. With the new workloads (ML in particular) demanding more compute and the mapping of the ML algorithms to sparse linear algebra (over-generlized – but remains true for the past 3-5 years) reflects on the shift in both time and power spent in compute cycles.

The 1-2uS and 10uS are interesting numbers in the memory – storage visual above to demark the split between memory model and storage/IO model. That lends itself to two types of networks – in this case they are synergistic, one can subsume the other and each one solves the needs below the sync-barrier or RDMA barrier and the other provides reach, throughput and carries the 40 year investment. The beauty of CXL with as one of the operating modes is it can transport IP packets within the same basic payload and thus provide compatibility while providing unique capability for rack scale needs. A rack will be the new unit of compute, the new unit of aggregation at the software level and is a good demarcation point for the foreseeable future. 2030 is 10 years away and a lot can change in that 10 years (whole new categories of companies will emerge).

By 2030 these three shifts will enable us to create the ‘fungible computer’ atleast at the rack level.

Disaggregation of silicon into component parts but aggregating them at upper layers of the stack is the key shift that is enabled by new workloads that are more data parallel, re-imagined processing pipelines, memory centric computational models at specific cost and power plateaus. SoP, CXL, (x)PU (x is your favourite IPU, NPU, DPU, MPU ……the alphabet soup of ideas).

This image has an empty alt attribute; its file name is image-14.png

The natural value shift and sedimentation is on its march once again. Unix was the high value at the dawn of RISC and OS (Solaris, Linux) sedimented down in value over a 20 year period. Multi-core and Cloud reset that with hypervisor and scale management frameworks with natural sedimentation that has happened. Emerging ML-IR and layers that map, aggregate, schedule and resource manage these disaggregated components will be most value and will take its due 20 year cycle to sediment itself until the next wave of innovation happens.

All of this is technical direction – reality might not follow technology visions. Going back to the proposition stated up front – this epoch we are going to see new efforts that drive both technology (bottoms up) and use cases (tops down). To enable that a new delivery and consumption model is needed.

Enter the bottom feeder once more…The upstream push by a select few global colo providers when married with right open and/or proprietary management stacks and many elements of the system design above used by some of the new players (e.g. Cloudflare, crypto companies – decentralized model is a first class property) will ride the next Tsunami to challenge current incumbents with the new technology stack (edge native) and new business models. The business model is interesting and will require a blog of its own at the right time, but its starting to emerge. To remind ourselves, we shifted from Capex to Opex to now……..(leave it as an exercise or perhaps share your ideas with me (DM) – love to hear them…).

In Summary I am reminded of Jerry Chen’s framework Systems of engagement, Systems of Intelligence, Systems of record. As more things change its continues to fit within the overall framework.

Tier 1 SystemWebEngagementNLP,CV, ML ?
Tier 2 SystemApp TierIntelligenceServerless ?
Tier 3 SystemDatabaseRecordCrypto ?

The pendulum is starting to swing away from the current cloud to either the new middle (cloud exchanges) or perhaps continue to find the other end of its swing (edge). We will know for sure by 2025, long before 2030….

Back in 2001 led the charge of building basic blocks for the emerging distributed systems world. Alas, failed to see the use case driven approach that led to innovation elsewhere. Lessons learnt – See more of this from me this decade and if interested lets talk, engage and collaborate.

In closing to what prompted me to pen this was Amin’s note – While the ‘cloud’ has innovated a lot in hardware systems, most of it was derived from HW innovations that were kicked off in the prior epoch. This coming decade will be unlike both prior epochs as we don’t have more of moore and its highly distributed and perhaps decentralized but more hardware innovation is at play than the prior epoch (cloud era).

SoC to SoP

A reflection of moore’s law, personal history and coming Tsunami of Systems

This blog was prompted by Pat Gelsinger in his recent keynote talking about Systems on Package (SOP). That brought memories of Systems on a Chip (SoC) – back to Circa 1991. While this term is common in the lingua franca of chip nerds these days, it was not the case back in 1991. Perhaps one of the first SoCs on the planet was one in which I was lucky to be involved with that also helped bootstrap my professional life in Silicon and Systems. It was Microsparc-I (aka Tsunami) while at Sun and that had a few firsts. All CMOS, first SoC and had a TAB package. All-in-one.

Image result from
MicroSPARC – 1 in a TAB package (Circa 1991)

This chip was in the system. Good to know its in the computer history museum archives.


The label Sun 386i was a joke. Used to have Sun 386i platform and the joke was, this was faster and cheaper than any PC then.

MicroSPARC-1 on the board

That was the beginning of my semiconductor run in my professional life. It started with an ECL machine for SPARC we did back in 1987-1990, which got shelved eventually as it was going to be hard to manufacture and sustain volume production. Some of us without a job, were asked to work on a ‘low cost’ SPARC and work with TI on their 0.8uM CMOS process. While the rage then was BiCMOS (SuperSPARC for Sun) and Intel Pentium. It showed Intel despite being a tech and manufacturing power house, has made mistakes in the past, not just recently…We will come to that

The First SoC (Microprocessor SoC) had many firsts back in 1991.

  1. It was all CMOS (when BiCMOS and ECL were still ruling the roost
  2. It was all integrated (Integer Unit, Floating Point Unit, Icache, Dcache, MMU/TLBs, DRAM controller (SDRAM) and Sbus Controller (Pre PCI).
  3. It was in 0.8 uM CMOS (TI) and in a TAB package (as seen above)
  4. It was entirely Software driven tool chain – the physical layout was done with Mentor GDT tools – programmatically assemble the entire chip form basic standard cells and GDT P&R tools, Synopsys synthesis, Verilog. All SW driven Silicon – A first. There is a reference to it here. This led to the entire EDA industry rallying around the way Sun designed microprocessors and a whole sleuth of companies formed around that (Synopsys, Ambit, Pearl, CCT->Cadence and many many more).
  5. It was the beginning of the low cost workstation (and server) – approach $1000 and ‘fastest’ from a clock rage (MHz – when that was the primary performance driver in the early years).
  6. From 1991 through 2003 by the time I left Sun, was involved in 8 different generations/versions of SPARC chips and looking back, the Sun platform/Canvas not only helped me be part of the team that changed the microprocessor landscape, we changed the EDA industry and by late 1990s brought ODM manufacturing to traditional vertically integrated companies to completely outsource systems manufacturing.

A visual of the height of Moore’s law growth and the success I rode with that Tsunami (Co-incidently the first chip for me was named Tsunami). From 0.8 uM 2LM CMOS to 0.65uM 10 LM CMOS. From 50 MHz to 2 GHz, 0.8M xtors to 500M xtors.

1991-1994 – Microsparc – The first CMOS SoC Microprocessor that extended Sun workstations and servers to the ‘low end’ and drove technology leadership with EDA companies named above in driving many ‘SW driven VLSI’. We built the chip with the following philosophy ‘construct by correction’ vs ‘correct by construction’ – which was the prevailing methodology. In modern parlance of Cloud – its DevOps vs ITops.

1995-1998 – UltraSPARC II and IIe – With the introduction of 64 bit computing, we continued to lead both on architectural performance (IPC), semiconductor technology (lead CMOS @ TI along with IBM until Intel took control of that by 1998), Clock Rate and many system level innovation (at Scale Symmetric Multi-processor, glue-less SMP at low cost, Media instructions). This was the Ultra family of compute infrastructure that was the backbone of the internet until the dot-com bust (2001-2003)!

1998-2001 – UltraSPARC I & E series: Created 2 product families and both drove new business ($1B+) for Sun. The Telco/Compact PCI business went form $0 to $1B in no time, the extension of workstations and servers to $1K and glue-less SMP (4-way) for <$20K, another industry first. The beginning of NUMA issues and pre-cursor to the dawn of the multi-core era. UltraSPARC IIi (codenamed Jalapeno) was the highest lifetime volume CPU for the entire lifetime of SPARC.

Clock Rate (SPARC vs x86)

While clock rate is not a good representation of actual device technology improvements, its the best first order relative metric I can share here given the dated history. Suffice it to say as you can see, until 1998 we had good technology (CMOS) FOM improvements per node until 0.18uM (Intel coppertone), when Intel decided to boost its performance by 60% when the industry average was 30%. That was the beginning of the end on two fronts – Sun + TI having enough capital and skills to keep up with the tech treadmill against Intel (althought we introduced copper for metal ahead of Intel) and the decision to start shifting architecture from pure IPC and clock to multi-core threading. Recognizing this, I started the multi-core effort around Circa 1998, but it took another 5 years to bear fruit. I digress.

As a side note: Look at Intel technology improvement performance lately. I would never have in my wildest imaginations thought this would happen.

2001-2003 – Dawn of Multi-core and threading: While the results of these happened in 2001-2003, the seeds of this were sown in both multi-core in the form of dual core UltraSPARC IIe and eventually Niagara (UltraSPARC T Series).

The next 10 Year years is going to be as dramatic as the 1990s for completely different reasons at the system level. While Moore’s law has slowed down, the SoP is an important and critical technology shift to enable one to keep up the effective Moore curve. With Moore you got performance, power and cost at the same time./ We won’t get all three, but we can strive 2 out of 3 – i.e. Performance at constant cost or power.

SoP (Systems on Package) is an important milestone and glad to see Intel leading that and so is AMD and rest – but this can be a compelling way to construct the new system. In the next blog we will explore why the next 10 years is going be disruptive at the system level, but SoP like SoC and CMOS+Moore law was the Tsunami wave that raised a lot of boats including my career, many companies success and changed the industry and computing stack in a fundamental way.

I expect many firsts or changes or disruptions from design methodology to now customization by customer of various heterogenous silicon components (CPU, IPU, FPGA, memory elements and a lot more). Associated with that will be tools to assemble this, but also tools to make these look like one monolithic’ fungible computing element to the end user.

Virtualization to-date has been dominated by leveraging multi-core and improving utilization by spawning of many VMs that subdivide the machine into smaller chunks. New software layers either above or below the standard frameworks like Lambda (Server-less), PyTorch/TF (ML/AI) or Crypto will drive new ways to effectively use the dramatic increase in total silicon real estate including tiering of memory, scheduling code chunks to accelerators in coherent space (via CXL), new intra-rack and intra-node connectivity models via CXL and many more to come. Strap in for that ride/discussion. HW is getting more disaggregated from aggregation that started back in 1991 via SoC to now with SoP , Software will have to do the ‘aggregation’.

As I signoff, will share some more images from the 25 year anniversary of SPARC is captured here in this montage below.

$TSLA – Marching towards $10T by 2030……

First Trillionaire and 10 Trillion dollar company.

This is my 4th post on the topic of $TSLA and never thought I would do one in 2021. My predictions was a valuation of $1T by 2030. That will come and pass rather soon.

My first post on $TSLA was back in June, 2017 where the core value long term I thought was Chemistry (Battery) and Intelligence (Full Self Driving/Autonomy). That continues to be the case with Elon’s battery day (Sep’20) & Tesla Autonomy day on April 2019.

So why $10T? That seems to be even more ridiculous than the $1T. Since Feb’20 to now it has gone by 4x and $600B market cap. While there are lots of bears, there are lots of bulls as well for the TSLA case.

Bull Case #1: The bull case is presented by Ark Invest (Source: Ark Invest). Having crossed 500K in 2020 and total of 1M+ with 2 additional factories (Austin and Berlin) yet to come online, getting to 1-2M by 2025 is highly likely and approaching 5M might be difficult, but then Elon has beaten the odds and the market is expecting him to with the demand.

2020Example Bear Case 2025
Cars Sold (millions)0.55Example Bull Case

Average Selling Price (ASP)$50,000$45,000$36,000
Electric Vehicle Revenue (billions)$26$234$367
Insurance Revenue (billions)Not Disclosed$23$6
Human-Driven Ride-Hail Revenue (net, billions)$0$42$0
Autonomous Ride-Hail Revenue (net, billions)$0$0$327
Electric Vehicle Gross Margin (ex-credits)21%40%25%
Total Gross Margin21%43%50%
Total EBITDA Margin*14%31%30%
Enterprise Value/EBITDA1621418
Market Cap (billions)673$1,500$4,000
Share Price**$700$1,500$4,000
Free Cash Flow Yield0.4%5%4.2%
Ark Invest Projections

What’s interesting is TSLA has single-handely taken out the $35K- $100K market which the Germans dominate and Toyota tried hard to penetrate with incremental engineering and marketing. TSLA changed the game and will perhaps go as low as $25K but not lower is my guess. TSLA will license IP (Chemistry and Intelligence) and let others make the cars. The entire $35K to $100K is now ‘owned’ by TSLA and its going to be harder for most makers other than the BMW or Mercedes and they will be supported by ardent fans latched onto the brands. 2018 data for segmentation of the various categories is shown here.



As you can see from above, 62.8% of the market will be covered by Tesla with Model 3, Model Y, Model S, Cybertruck and perhaps upcoming new China sourced $25K model. That includes the SUV, Midsize, MPV, Pickup, Executive, Sport and Luxury segments. The total market size is 54M cars and if Tesla can get 20% of that category – which actually is possible (we are in winner take all world these days with Amazon, Google, Apple where its tech driven) relative to conventional wisdom of highly fragmented and splintered market for automobiles.

Bull Case #2: Its what I mentioned in the last year. One has to look at TSLA as a business of businesses. Expect in the next 5 years, either take the Alphabet (GOOG) route or via other routes (Spin-offs, M&As, SPACs ….) derivative businesses will emerge and stand on their own. To re-cap

  1. A car company
  2. A Battery company at planet scale
  3. An AI/ML company (machine vision in particular)
  4. An electric storage company
  5. An Electric Utility company (low value but at scale gets interesting)
  6. An energy distribution company
  7. 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)
  8. A big data/mapping/navigation company
  9. A carless car company (i.e. Uber/Lyft killer robotaxi)
  10. A machine vision driven robotics company
  11. and more to come….(more than letters in the Alphabet)

Elon himself quoted a version of this back in Oct’2020

Bull Case #3: Chemistry and Intelligence. Every tech category goes through vertical integration and horizontal stratification. I speculate Elon will build down to a $25K car and below that, he will ‘license’ IP (Chemistry and Intelligence or battery tech and autonomy tech) to get worldwide reach. It would not make sense to have factories all over the world for all geos – but a strong IP revenue model ($1-$2K/car) could be had and also enable new players in countries to become car companies – i.e. more local manufacturing and distribution. And not just limited to cars, for all kinds of transportation and perhaps energy sectors. From the chart above the remaining 35% of the segments (Compact, Sub-Compact, City-car) would belong in this category. Of the 86M cars sold in 2018 (I suspect its less in 2021), 30M cars would be in this category. If 20% of the manufacturers pay TSLA $1K-$2K – lets assume $1K – that is $6B of pure profits which is subsidized by the higher end. The TSLA brand will be more valuable and trusted over VW, Mercedes, BMW, Toyota by 2025 that most people would buy a car ‘Tesla powered’. At this rate of battery cost decline (see blow), these manufacturers who cannot afford R&D or manufacturing at scale would do well to buy it off TSLA. This is akin to INTC holding onto x86 and not having an IP model which let ARM into its turf. Imagine if INTC had both a vertically integrated model of CPUs and a licensing model for some components – AAPL would be in Intel’s camp and so would the big three hyperscalers. Pat Gelsinger is trying to get Intel back into that game in 2021 (which we will address in a different blog post). But if TSLA were to choose both models, a vertically integrated model for some categories and an IP or sub-component sale to other categories, they can cover the entire spectrum and make the brand even more ubiquitous and higher moat.

TSLA is handicapped relative to VW and Toyota on manufacturing scale and distribution reach. The aggressive ramp of manufacturing of the car and IP model will broaden the reach and create other businesses (Robo Taxi, Energy Storage/distribution, Cloud computing for AI and many more to come).

Bill Gates famouly said ” We overestimate what we can do in 2 years and underestimate what humanity will achieve in 10″. One has to do a version for Elon. He over-promises what’s coming in 2-3 years, but delivers on a 10 year vision. If you look back what he has said in 2011/2012 – and see what has been accomplished – its not that far off.

We will revisit this blog in 2025 if we have crossed the Ark Invest marker and if TSLA is barrelling past $3-$4T and march towards the first $10T company on the planet (or maybe a collection of companies).

TSLA : A business of businesses marching towards $1T

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

Cloud and Fabs – 3 Years Later (Its AWS and VMware)

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.
Image result for TSMC vs Intel market growth comparison

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.

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 (57c) SUV (68c) Minivan (61c)

Constrating that to a Model X (75D) assuming 15K miles/year and free supercharge (unique to Tesla) works out to 69c/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 39c/mile. My benchmark is, if I can get to say ~25c/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 25c/mile. For reference Uber and Lyft charges me >$1/mile. I would rather drive a 25c/mile than >$1/mile is its available at the click of a button from my Phone – i.e. a 4x reduction in cost of ‘shared’ transportation. That is very disruptive to current Uber/Lyft models.

A Simple EV Model is available for any or all of you to review. I have compared a Model 3 owner (LR) vs a model 3 shared (SR) vs Model X and Camry (owned and shared). Some assumption of fuel cost, maintenance and insurance costs are included as well. Feel free to copy and use it or modify to your hearts content. Simple EV model:

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 (effectively 67% as you do not want to go below 20% either). 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 26c/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 25c/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…

1987 – 2017: SPARC Systems & Computing Epochs


30 Years of SPARC systems is this month in July’1987 when Sun 4/260 was launched.  A month before,  I started my professional career at Sun – to be exact June 15, 1987. 16+ years of my professional life was shaped by Sun, SPARC, Systems and more importantly the whole gamut of innovations that Sun did from chips to systems to operating systems to programming languages, covering the entire spectrum of computing architecture. I used to pinch myself for getting paid to work at Sun.  It was one great computer company that changed computing landscape.




The SPARC story starts with Bill Joy without whom Sun would not be in existence (Bill was the fourth founder, though) as he basically drove re-inventing computing systems at Sun and thus the world at large.  Bill drove  technical direction of computing at Sun and initiated many efforts – Unix/Solaris, Programming languages, RISC to name a few.  David Patterson (UC Berkeley, now at Google) influenced the RISC direction. [David advised students who changed the computing industry and seems like he is involved with the next shift with TPUs @ Google – more later]. I call out Bill amongst the four (Andy Bechtolsheim, Scott Mcnealy, Vinod Khosla) in this context as without Bill, Sun would not have pulled the talent – he was basically a big black hole that sucked all talent across the country/globe. Without that talent, the innovations and the 30 year history of computing would not have been possible. A good architecture is one that lives for 30 years. This one did. Not sure about the next 30. More on this later. Back then, I dropped my PhD on AI (was getting disillusioned with then version of AI) for a Unix on my desktop and Bill Joy. Decision was that simple.

From historic accounts, the SPARC sytem initiative started in 1984. I joined Sun when the Sun 4/260 (Robert Garner was the lead) was going to be announced in July. It was  VME based backplane built both as a pedestal computer (12 VME boards) as well as rack mount system replaced then Sun 3/260 and Sun 3/280.  It housed the first SPARC chip (Sunrise) built out from gate arrays with Fujitsu.


This was an important product in the modern era of computing (198X-201X). 1985-87 was  the beginning of exploitation of Instruction level parallelism (ILP) with RISC implementations from Sun and MIPS. IBM/Power followed later, although it was incubated within IBM earlier than both . The guiding principles being, compilers can do better than humans and can generate code that is optimal and simpler with the  orthogonal instruction set. The raging debate then was “can compilers beat humans in generating code for all cases”?. It was settled with the dawn of the RISC era. This was the  era when C (Fortran, Pascal, ADA were other candidates) became the dominant programming language and compilers were growing leaps and bounds in capabilities. Steve Muchnik led the SPARC compilers, while Fred Chow did with MIPs. Recall the big debates about register windows (Bill even to-date argues about the decision on register windows) and code generation.  In brief it was the introduction of pipelining, orthogonal instruction sets and compilers (in some sense compilers were that era’s version of today’s rage in “machine learning” where machines started to outperform human ability to program and generate optimized code).

There were many categories enabled by Sun and the first SPARC system.

  1. The first chip was implemented in a gate array, which was more cost effective as well as faster TTD (Time to Design). The fabless semiconductor was born out of this gate array model and eventually exploited by many companies. A new category emerged in the semiconductor business.
  2. EDA industry starting with Synopsys and their design compiler were enabled and driven by Sun. Verilog as a language for hardware was formalized. It was an event driven evaluation model. Today’s reactive program is yesterday’s verilog (not really, but making a point here that HDL forever was event driven programming).
  3. Create an open eco-system of (effectively free) licensable architecture. It was followed by Opensource for hardware (OpenSPARC) which was a miserable failure.

The first system was followed by the pizza box (SPARCstation 1) using the Sunrise chip. Series of systems innovations were delivered with associated innovations in SPARC.

  1. 1987 Sun4/260 Sunrise – early RISC (gate array)
  2. 1989 SPARCstation 1 (Sunray) – Custom RISC
  3. 1991 Sun LX  (Tsunami) – First SoC
  4. 1992 SPARCstation 10 (Viking) – Desktop MP
  5. 1992 SPARCserver (Viking) – SMP servers
  6. 1995 UltraSPARC 1, Sunfire (Spitfire) – 64 bit, VIS, desktop to servers
  7. 1998 Starfire (Blackbird), Sparcstation 5 (Sabre) – Big SMP
  8. 2001 Serengeti (UltraSPARC III) – Bigger SMP
  9. 2002 Ultra 5 (Jalapeno) – Low Cost MP
  10. 2005 UltraSPARC T-1 (Niagara) – Chip Multi-threading
  11. 2007 UltraSPARC T-2 – Encryption co-processor
  12. 2011 SPARC T4
  13. 2013 SPARC T5, M5
  14. 2015 SPARC M7 (Tahoe)
  15. 2017 M8…

The systems innovations were driven by both SPARC and Solaris or SunOS back then. There are 2 key punctuations in the innovation and we have entered the third era in computing. The first two was led by Sun and I was lucky to be part of that innovation and be able to shape that as well.

1984-1987 was the dawn of the ILP era, which continued on for the next 15 years until thread level parallelism became the architectural thesis thanks to Java, internet and throughput computing. A few things that Sun did was very smart. That includes

  1. Took  a quick and fast approach to implement chip by adoption of gate arrays. This surprised IBM and perhaps MIPS w.r.t speed of execution. Just 2 engineers (Anant Agrawal and Masood Namjoo) did the integer unit. No Floating point. MIPS was meanwhile designing a custom chip
  2. It was immediately followed by Sunrise a full custom chip done with Cypress (for Integer unit) and TI (for floating point unit). Of all the different RISC chips that were designed around the same era, SPARC along with MIPS stood out (eventually Power).
  3. That was the one two punch enabled by Sun owning the architectural paradigm shift (C/Unix/RISC) as compute stack of then.

Industry’s first Pipelining, super scalar (more than 1 instruction/clock) became the drivers of performance. Sun innovated both at the processor level (with compilers) and system level with symmetric multi-processing with operating system to drive the ‘attack of the killer micros‘. A number of success and failures followed the initial RISC (Sunrise based platform).

  1. Suntan was an ECL sparc chip that was built, but not taken to market for two reasons. [Have an ECL board up in my attic]. The debate of CMOS vs ECL was ending with CMOS rapidly gaining speed-power ratio of ECL and more importantly the ability of Sun to continue with ECL would have drained the company relative to the value of the high end of the market. MIPS carried through and perhaps drained significant capital focus by doing so.
  2. SuperSPARC was the first super scalar process that came out in 1991 working with Xerox, Sun delivered the first glue-less SMP (M-bus and X-bus).
  3. 1995 was a 64 bit CPU  (MIPS beat to market – but was too soon) with integrated VIS ( media SIMD instructions)

After that the next big architectural shift was multi-core and threading. It was executed with mainstream Sparc but accelerated with the acquisition of Afara and its Niagara family of CPUs. If there is a ‘hall of fame’ for computer architects, a shout out goes to Les Kohn who led two key innovations – UltraSPARC (64bit, VIS) and UltraSPARC T-1 (multi-threading). Seeds of that shift was sown in 1998 and family of products exploiting multi-core and threading were brought to market starting in 2002/2003.

1998, in my view is the dawn of the second wave of computing in the modern era (1987-2017) in the industry and again Sun drove this transition. The move to web or cloud centric workloads, the emergence of Java as a programming language for enterprise and web applications enabled the shift to TLP – Thread Level Parallelism. In short, this is classical space-time tradeoff where clock rate had diminishing returns and shift to threading and multi-core began with the workload shift. Here again SPARC was the innovator with multi-core and multi-threading. The results of this shift started showing in systems around 2003  – roughly 15 years after the first introduction of SPARC with Sun 4/260.  In that 15 years, computing power grew by 300+ times and memory capacity grew by 128 times, roughly following Moore’s law.

While the first 15 years was the ILP era and the 2nd 15 years was about multi-core and threading (TLP). What is the third? We are dawning upon that era.  I phrase it as ‘MLP’- memory level parallelism.  Maybe not. But we know a few things now. The new computing era is more ‘data path’ oriented – be it GPU, FPGA or TPU – some form of high compute throughput matched by emerging ML/DL applications. A number of key issues have to be addressed.



Every 30 years, technology, business and people have to re-invent themselves otherwise they stand to whither away. There is a pattern there.

There is a pattern here with SPARC as well. SPARC and SPARC based systems have reached 30 year life and it looks to be at the beginning of the end , while a new generation of processing is emerging.

Where do go from here? Defintely applications and technologies are the drivers. ML/DL is the obvious driver. Technologies range from memory, coherent ‘programable datapath accelerators’, programming models (event driven?), user space resource managers/schedulers and lots more. A few but key meta trends

  • The past 30 years – Hardware (Silicon and Systems) aggregated (for e.g. SMP) the resources while Software disaggregated (VMware). I believe the reverse will be true for the next 30 i.e. disaggregated hardware (e.g. accelerators or memory) but software will aggregate (for e.g. vertical NUMA, heterogenous schedulers).
  • Separation of control flow dominated code vs data path oriented code will happen (early signs with TPUs).



  • Event driven (for e.g. reactive) programming models will become more prevalent. The ‘serverless’ trend will only accelerate this model as traditional programmers (procedural) have to be retrained to do event driven programming/coding (Hardware folks have been doing for decades).
  • We will build machines that will be hard (not in the sense of complexity – more in the sense of scale) for humans to program.

CMOS, RISC, Unix and C was the mantra in 1980s. Its going to be memory (some form of resistive memory structure) and re–thinking of the stack needs to happen. Unix is also 30+ years old.



Just when you thought Moore’s law is slowing, the amalgamation of these emerging concepts  and ideas in a simple but clever system will rise to the next 30 years of innovations in computing.

Strap yourself for the ride…



wrong tool

You are finite. Zathras is finite. This is wrong tool.

----- Thinking Path -------

"knowledge speaks but wisdom listens" Jimi Hendrix.

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