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.

anant

                                                                            R I P

                                                                           Anant

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.

 

Slide1

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

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

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

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

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

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

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

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

Time will tell…..

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

Time will tell…

30 (15 and 7) Year Technology Cycles

30_year_img

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

Tesla – Chemistry & Intelligence

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

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

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

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

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

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

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

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

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

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

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

Block Device is dead. Long Live the block device

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

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

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

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

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

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

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

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

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

 

 

Leadership Principles

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 Power 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.