A unique milestone: First non-technology company in the US hits a $1Trillion market cap and owns more short term T-bills than Federal Reserve. The CEO of this company turned 94 years old! Now, “Would you like to have Warren Buffet as your financial personal advisor and philosopher? why not?” With simple to use AI frameworks, one can easily create personal advisors of your choice. I did create this personal advisor and ensured to ground the LLM by leveraging RAG and revalidated the responses from Warren by citing shareholders meeting minutes or the famous shareholders letters. Well, it might sound like something very complex to do but the ease of building something new will change forever. [Note: It took me 2 weeks of ramping up on LangChain/Flow, Embeddings, VectorDb, RAG and a bit of fine tuning art. It was fun building it nevertheless. If interested to know more DM me]

I am not a futurist but very optimistic of the creative opportunities one gets by leveraging these tools to re-discover yourself first, understand our own source code and path to self discovery. I very well believe in Amara's Law : “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

Fun: Check out this fun debate between two AI systems that I triggered. “Singularity” is pretending to be Conscious while “AI Guru” is not. The level of sophistication in reasoning and spontaneous responses in the moment by recollection is just amazing.

But I also have these questions, which I am sure many would have: Is the Gen AI Hype Cycle of Inflated Expectations subsiding to set the path of trough of disillusionment and to level of enlightenment ? Is early AI busts inevitable to reach next level of maturity in the hype curves ? It looks like we are still at the “pilotitis” phase ? Is the current capex spend on AI by big tech companies as a share of revenue justifiable? Is the “winner takes all” phenomena creeping up and if so, who would that be - the architecture chip providers or infrastructure providers or the application builders ? Is the automatable tasks getting automated faster with AI tools resulting in measurable productivity gains? Are the budgets getting shifted to enable experimentation, to build, adopt and deploy AI applications ? I am sure there are many more. I don’t have all the answers but intellectual curiosity exists to explore the possible answers. This blog content represents my personal reflections only!

We all accept technology innovations influencing the economy is never felt immediately . It's a progression. But as seen in the past few months, analysts used business quarterly financial metrics to articulate the challenges with AI adoption & lack of immediate ROI. Yes, the future prospects of growth driven by AI innovations enabled the “Magnificent Seven” to dramatically influence S&P 500 returns. [49% of S&P gain].

As expected, market started looking for short term gains by those enterprise companies who could have benefited with the new Gen AI revolution. So, the “Magnificent Seven” got the market love but the adopters of that AI innovation did not immediately see returns! This imbalance is naturally raising the questions of Gen AI readiness for wider enterprise adoption and tangible business impacts.  

I strongly believe this recent negative trend is the result of overestimating the short term gains.

So, here is what I believe.

In the past, human intelligence used AI tool kits to deliver an outcome. Now, we are seeing the advancement in these AI technologies with potential to provide Intelligence on demand to address human needs. If you have doubts, just have audio conversion with Chatgpt-4o Or see how many clicks you used to take on Google Search before to find information and now with synthesized data. Easy access to AI tool kits like Github copilots or Gemini Pro lengthy context lengths or non-stop investment dollars entering startups to innovate faster, I feel we will end up with a very fast pace of AI “infrastructure and frameworks” built to expand experimentations, alignment to tangible use cases and eventually building the killer apps that will drive measurable adoptions. This rate of change will be exponential..

We do see new innovations to build ground breaking tool kits powering LLM AI models to robotics to brain interfaces. To name a few, 2+ Million context length with Gemini Pro, combined multimodal inputs via GPT4o, fast with efficiency Gemini Flash for devices, domain specific AI Agents, Open Source models like Llama 3.1, advancement into Robotics like Optimus, Brain computer interfaces via Neural Links and many more. Open AI has 200 million weekly active users with peak usage of their API’s, monthly token usage of Llama has grown 10x this year alone, VertexAI claiming its real production rollouts, xAI’s 100K H100 GPU Cluster Colossus, largest AI cluster till date, turned up in just 122 days — all pointing out the experimentation is in full swing and these innovations will drive further maturity and cost economics enabling adoption.

Recent Cloud Computing transition, we saw the era of IaaS/PaaS/SaaS as it relates to delivering valuable applications and transforming businesses. Each of these SaaS delivered outcomes with volumes of data and insights generated. What I believe next is, on top of that framework and with of the Data & new AI technologies, we will start seeing a new wave of delivery model: “Intelligence as a Service” (non-biological). A platform for enterprise (in-house built or aaS) with domain centric data, insights (from various SaaS/Private Infrastructure applications), plumb them into AI LLM Infrastructure (within CSP eco-system or in-house), derive relatable outcomes (internal consumption or external IaaS application). All this influenced still by Human intelligence for now!

Would we then create this endless loop of feeding all modalities of inputs and intelligence back into the system for AI systems to improve its intelligence, exponentially and then understand the world model much more and eventually enter the realm of “Singularity” Or what I call AI Deus ? Something to ponder..a topic for future.

What would the implications of this would be ? We do have some historical context on how technology disrupted day to day life. While many benefited from these disruptions, there are many who got “disrupted” became obsolete.

1. Internet → e-commerce & beyond → Winners: Infrastructure Platform providers & applications on top of it → Disrupted: Brick and Mortar

2. Cloud Computing & SaaS → Workload migrations → Winners: Infrastructure Platform providers → Disrupted: Traditional IT Infrastructure & Software vendors

3. Social Media → Era of entrepreneurial  Influencers → Winners: Infrastructure Platform providers → Disrupted: News Media, Social Dilemma 

4. Smartphone & Apps → Human life line → Winners: Device Vendors & Cloud Infrastructure providers for Streaming, Application Services → Disrupted: Blackberry & many more. 

5. Space Transportation → Mission to Mars with Reusability → Winners: SpaceX and new Innovators → Disrupted: Communication Satellite & Space Exploration business

6. Robotics & Brain Interfaces → Automation & Humanoid era → Winners: Tesla, Amazon & many more → Disrupted: Humans

Similarly now, Gen AI & New AI revolution has the potential to disrupt all the above and naturally, the question would be who are the winners and who will be disrupted beyond recognition. Like how Uber transformed the medallions to worthless, which use cases of AI will transform business beyond recognition? It's a bit foggy right now to get to the bottom of that!. But for sure just like how we adopted SaaS, similarly, there will be winners who would come up with AI applications and deliver via their AI IaaS (Intelligence as a Service) platform and Enterprises will consume the outcomes!

I feel there are three major factors which would directly influence the adoption, effectiveness and business shifts while we continue to embark on exploring those killer apps:

1. Trust to Adopt: Can I entrust this with company data & Is it safe?

2. Ease of Customization: Can I effectively fine tune for my specific use cases?

3. Path to Maturity: Can I substantially save money or make money long term?

The above three are influenced by how you would view this “Intelligence as a Service” platform. What makes up this platform, what inputs drive the outcomes, what are the unit economics, and many more. The below framework is something I feel would be a good starting point. I would start heavily focussing on the forest first and analyze the trees with experimentation! 

As it relates to adoption driven by trust, we have some data points on this. We see the same philosophy of bait and catch happening all over again. Free Search engines, free social media apps, free productivity suits were all given to the human population to obtain “personal data”. We happily shared all this with or without consent, with or without understanding the implications.  We are the same cusp. ChatGPT was a classic example of enticing the users to leverage the tool kits to generate more data, more context, more insights into the world around us to feed the ever hungry models. Consumer adoption of AI tool kits will continue dramatically and will reach an extent where we will say “Where is AI anymore, it's dead, meaning it's part of my life now without knowing its AI”.

On the other hand, for broader Enterprise adoption, trust is everything. Just like how it took decades to see the transformation of data center workloads to move to the Cloud, we entrusted our enterprise data and shifted the risk of managing it into the hands of CSP ( Cloud Service Providers). Similar journey is underway based on various data points we see in the adoption reports. It will be quite natural to see enterprise workloads & data that are already in the hands of CSP or SaaS providers to be extended for AI Application development & delivery. Some of the following reports do clearly indicate the journey has begun through experimentations & CSP’s will continue to provide the necessary frameworks to glue, build and transform business. You can clearly see a level of alliances getting established - Open AI with Azure, Anthropic with Amazon or Gemini Pro with GCP, while all claim to be open to support “Open Source” models along the way.

One area that has always fascinated me is around the ROI decisions one has to make in embracing transformative technology. It’s a tough balancing act between short term gains to long term investment to leapfrog. One parameter that surely influences even to continue to experiment is the Cost factor. Like we have seen in past, the simplified SaaS onboarding, activation & usage drove stickiness with the suppliers & expenses soared. We would be marching into similar territory now.

As predicated, the rate of cost optimization already happening with LLM Models is a sheer reflection of how enticing “Intelligence as a Service” Platform would become!

While there are many factors that would influence the overall AI cost, the key contributor is determined by Model usage itself. Obviously if open source models are used, the cost factor shifts to fine tuning, operationalization of AI deployment. For closed LLM, Tokens is the foundational logic which drives the pricing. Every query, prompt and responses are all in-terms of tokens when processed by the models. The process of tokenization drives the overall usage of the LLM models. Natural question would be how would one know how much tokens the application would consume if I am using the managed or closed LLM from a provider ( like OpenAI). This going to be tricky and completely driven by use cases. One could average out the number of tokens used per input and output from the model based on the daily usage patterns. This will give an idea on the total token usage and hence the price. Its clearly going to be iterative cost analysis based on the usage patters and the type of use cases, the model selected and expected outcomes. Few key parameters to watch for: Token size of the input prompt and output responses, Latency, Prompt complexity, Model complexity.

Along with this foundational token usage cost modeling, AI application development will be integrated with potentially RAG frameworks to avoid hallucinations and complex rollouts to bring in multiple LLM models. These additional cost to be factored included Vector Storage, AI LLM gateways to centrally manage access between LLM providers, overall AI Infrastructure, network ingress and egress, and many more. There is tremendous opportunity to innovate in the FinOps domain as it would be a complex model to think of cost optimization from day one and not be an after thought.

In Summary, I feel these trifecta effect will exponentially drive adoption of AI Applications - Model efficiency, ease of use with AI Infrastructure & frameworks, and increase trust to experiment via secure guardrails. The productivity applications are the starting point with a path to broader usage. Just like SaaS drove business transformations, AI driven “Intelligence as a Service” platforms which would leverage multiple data sources from various existing applications and workflows to drive new wave of AI Applications!

Exciting times ahead for sure!

As the calendar flipped to April, most of us grudgingly turned our thoughts to yearly tax season ritual to satisfy Uncle Sam’s belly. I am sure you all agree, nothing new there, this year too. But a lot else happened that did catch my attention. Naturally, all the innovative deliverables at work and excited to see continued contributions to business success. The AI disruption continued at an unprecedented pace as seen at Google Cloud Next, Amazon Q launch, Meta Open source journey, Tesla Optimus starts working and many more.. At times it felt, as humans, we are shrinking the expansion of time as we march towards “Singularity” faster than we can imagine now. Let me park that discussion for now and come back with AI & path to Singularity update in upcoming blog.

Each year, for many decades, I eagerly looked forward to April. It has been the month to rejoice in the mastery of the sport I adore the most: the Masters @ Augusta. This prestigious golf event has been held exclusively since 1934 at the same location in Georgia—the Augusta National Golf Club. This past April, the vibrant colors of the blooming azaleas and dogwoods welcomed the spring, and the impeccably green grass made the Masters a celebration of the season as much as it was a golf tournament. Whether you are a golf fanatic or not, experiencing this place in person or virtually is blissful. It remains a pinnacle of excellence in the sport. Each year, I attempt to win an entry ticket, but with no luck so far, and it is beyond my reach to become a member or patron of this place. I hope someday to drive through the 330 yards of Magnolia Lane and experience this memorable venue (no pressure, Shreyas!). Above all, the best of breed golfers battled it out for that majestic ceremonial green jacket—and a lot of green to grab, with a $32 million purse and the winner taking home a $3.6 million payout.

Fun fact : Mobile phones are not allowed in this venue ! Only phone booths!! This preserves the purity and tranquility of the golfing experience, setting Augusta apart in an era dominated by digital distractions.

What’s so special, you may ask? There are courses like Pebble Beach or the world-famous St. Andrews, each with its own beauty. Yes, they are unique, and having been to Pebble Beach multiple times, I know how it feels. But from what I have observed and analyzed, the speciality of Augusta lies in the amalgamation of technological innovations with Mother Nature, alongside the spectacle of mental toughness and resilience demonstrated by the players.

Here are my observations & analysis that make this so unique.

Augusta National is not just a golf course; it's a sanctuary of sport that combines natural beauty with a storied history unmatched anywhere else in the sporting world. The exclusivity and mystique of Augusta are maintained by its invitation-only policy, unique among major sports venues. The course itself is closed to the public for most of the year, preserving its pristine condition and creating an aura of mystery and anticipation.

Moreover, the traditions upheld here—such as the Champions Dinner, where past winners select the menu, and the Green Jacket ceremony—are deeply ingrained in the fabric of the tournament, creating a continuity and a legacy that connects generations. 

Here are some insights worth an imagination:

• In 1933, a 365 acre Fruitland Nurseries was purchased by Augusta National Golf Club.  80,000 plants today of which more than 350 varieties have been added 

• Driveway entrance to the Golf club is 330 yards with 61 Magnolia trees that line up both sides of the road and trees date back to the 1850s. (see above).

• First tournament was held March 22nd 1934 and from 1940, the Masters were scheduled the first week in April each year

• The winner of the event gets the ceremonial green jacket at the Butler Cabin, a tradition followed no other place starting from 1937.

• Each hole is named after a specific plant or a tree. Hole # 2 is Pink Dogwood, Hole # 8 is Yellow Jasmine, Hole # 13 is Azalea..

• “Amen” corner – Most beautiful and challenging set of holes - Hole # 11, 12, 13. Each golfer would be thankful as they get through these and hence “Amen corner” was coined.

The Business & Innovations Behind the Greens: Augusta’s Unique Economic Impact:

• In 1931, Augusta National Golf club purchased the land at 70,000$ which is valued today at 200M$ or more. With various LLC’s, Augusta has been expanding by buying additional 250+ acres of land, strip malls, restaurants, apartment complexes. 

• Most golf clubs operate as non-profit but Augusta is a for profit corporation. They pay taxes but now get exempt from declaring the membership identities. 

• Visitor Influx: During the week of the Masters, Augusta sees an influx of approximately 50,000 visitors per day with an exclusive merchandise spend of 70M$ for the week. No online sales, you can only buy at Augusta!

• The IRS has a special exemption in the tax code called the "Augusta Rule," allowing homeowners to rent out their homes for 14 days per year without paying taxes on the income. This rule was initially implemented for Augusta residents only but is now available to everyone in the United States.

• The concession food is priced very low but uniquely wrapped in green packaging. Even if you litter, it won’t be an ugly sight on TV as it matches the grass color. 

• The clubhouse is famously known for its wine cellar with 30+ pages of world's most exclusive wines. Is what I have heard, not sure!

• 1000’s of private jets land in Augusta for the week and each pay around 3000$ landing and parking fees.

• Golf is a sport with many variables that the player has to tackle like wind, temperature, angle of grass growth and many more. Augusta wants to ensure consistent greens across the entire course. They have SubAir systems under each green which sucks water when it rains and does add moisture when it's too hot. 

• Your tickets have RFID tags. Makes it easier to ensure the right level of access controls. 

• Augusta controls Media rights. They stream the live event through their own famous Masters app and limits outside broadcast scopes to maintain exclusive viewing experience. CBS has an agreement with Augusta and CBS gets free reign for its broadcast. An agreement done as a tradition and renewed each year. 

• Advertising Exclusivity: Advertising during the broadcast is highly limited, with spots reportedly costing around $2 million for just a few minutes, emphasizing the premium nature of the event.

• IBM, the master behind Masters app. A unique blend of technology, with AI now enabling viewers to get insights of every shot, every player, every location they are on and all the time! With AI Watson in play, IBM experimented with Spanish language as an option for each hole. This is the most unique blend of technology offered at Masters breaking away from the norm.

Leadership Lessons from the Quest for the Green Jacket

More than just a tournament, the Masters is a masterclass in leadership and psychological resilience. Augusta’s challenging course serves as a proving ground for those who lead not just on the greens, but in life. The tournament teaches valuable lessons about respecting nature, maintaining composure under pressure, and making precise decisions. Watching these battles unfold provides insights into a style of leadership that is calm, calculated, and respectful of both environmental and competitive forces. Hole #12 and Amen Corner exemplify life's challenges and the memorable triumphs over such obstacles. Augusta’s meticulous management of this event and its unique customer experience offer lessons in detail-oriented leadership. Moreover, adapting to the ever-changing landscape of technology, including the AI revolution, showcases Augusta's innovative approach.

Why the Masters Matters: A Personal Reflection

For me, the Augusta Masters is more than just a sport; it represents aspiration. Although securing a patron badge to Augusta National is notoriously difficult, with a waitlist that can extend indefinitely, the dream of being part of it, even from afar, is undeniably compelling.

This tournament, with its lush fairways and storied past, is more than just a competition; it’s a yearly reminder of what can be achieved with dedication and respect for the heritage we are part of. It teaches the importance of attention to detail in decision-making, demonstrating humility in times of distress, strategically considering all variables, and most importantly, being present in the moment through self-awareness.

Whether you’re a lifelong golf fan or a newcomer to the sport, the Masters is a spectacle that deserves your attention. It’s not just a tournament; it’s an experience, a lesson, and a journey all rolled into one. Hope to see you “Amen Corner”!

 “That’s one small step for (a) man, one giant leap for mankind.” 

Yes, it’s unforgettable. I find myself filled with a deep sense of longing, knowing that I missed the opportunity to witness the historic moon landing. It’s a moment of immense significance, and not being able to experience it firsthand leaves me with a sense of yearning for that pivotal chapter in history. The space bug did hit me in 1994, ever since I saw the magic of how a remote sensing satellite IRS-P2 was controlled remotely at ISRO Telemetry and Tracking (ISTRAC), Bangalore, India command center. This is the closest I have ever been to feel the historic moments in space exploration. While my professional journey took its turns, I have never given up on the hunger to continue to learn and rekindle the excitement with space and most importantly gain inspiration through the chaos in cosmos. Come join me to explore recent innovations in space industry.

“We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard,”

JFK’s historic moon speech reshaped NASA’s direction.While we achieved significant milestones within a decade, our ambitions, such as lunar bases and Mars missions, often felt just out of grasp. We conquered the lower earth orbit, geostationary orbit and all these became part of the economic sphere of Earth. We could not include Moon to be part of this economic sphere through lunar base stations or tourism. It wasn’t a lack of national will; the main hurdle was the high cost of running a space program, making it tough to sustain within a national budget.

But, the last 10+ years have been the most exciting and memorable times. Thanks to a new breed of space enthusiasts, billionaires who are space nuts, I no longer have any regrets about missing the Apollo 11 moment. I am thrilled to be alive in this extraordinary space era of human history.

Jeff Bezos: “We get to preserve this unique gem of a planet by moving heavy industry off of Earth and Earth can be zoned residential and light industry. It’ll be a beautiful place to live, and we have to start now.”

Elon Musk: “I want to die on Mars. Just not on impact.”..“You want to wake up in the morning and think the future is going to be great — and that’s what being a spacefaring civilization is all about. It’s about believing in the future and thinking that the future will be better than the past. And I can’t think of anything more exciting than going out there and being among the stars.”…”The objective overall has been to make life multi-planetary and enable humanity to become a spacefaring civilization.” “Travel anywhere on earth in less than an hour”

Richard Branson: “We stand at the birth of a new industry that is going to lead to one of the biggest industrial revolutions in history. A revolution that will reshape our perception of space and how we utilize it for the benefit of humankind.”

I do dream their vision..Are we there yet ? It’s a journey for generations.

See this…a glimpse into Engineering marvel..

SpaceX is the reality..unlocking the space exploration industry. There are many “firsts”. Here are my favorites that gives goosebumps every time I realize this happened!

1. December 28th 2023 — SpaceX — Two orbital launches within 3 hours apart, shortest time between orbital launches at Cape Canaveral since 1966.

2. 2023: SpaceX — Every 4 days there was a launch! 98 launches, including the 3 crew mission to ISS. Out of 223 Launch attempts globally, US had 109 attempts. 98 of these launches supplied by SpaceX. In 2022, SpaceX did 61 launches. Target for 2024 - 150 launches, 2.5 days per launch!

3. SpaceX Falcon Rockets — No other family of orbit-class rockets has ever flown more than 63 times in a year. Close to 2.6 Million Pounds of payload to the orbit! 

4. SpaceX Starship First test flights in April/Nov 2023 — Transport vehicle to Mars and beyond! 400 Feet Tall, 17 Million pounds of thrust, 33 Raptor engines propelling up to 15K Miles per hour! Nothing comes close to this. 

5. Falcon 9 Sticks the Landing (December 2015): SpaceX successfully landed its Falcon 9 rocket. And on Droneship. A critical step towards their goal of reusable rockets. On March 2017, reuses a rocket for first time

6. Falcon 9 first-stage boosters landed successfully in 261 of 272 attempts (96%), with 236 out of 240 (98.3%) for the Falcon 9 Block 5 version. A total of 234 re-flights of first stage boosters have all successfully launched their payloads.

7. SpaceX enables close to 5000 Starlink Satellites in orbit part of SpaceX's Starlink broadband Internet network, 2.2Million subscribers worldwide. Jan 2024 - SpaceX has launched the first satellites with the capability to connect to smartphones on the ground with T-Mobile.

8. Why even have any buttons on Dragon Capsule for crew mission to ISS ? SpaceX innovative software centric fully autonomous capsule docking system with touch screen operations and control, all this is first ever.

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The natural question that arises is

"How is SpaceX so successful - Business & Product sense?

I may not have all the qualifications to answer this question definitively, but I can certainly articulate why they stand out in terms of engineering excellence by aligning their practices with well-established guiding principles. SpaceX serves as a prime example of how these principles can be seamlessly applied and integrated, making them trailblazers in the realm of space exploration and innovation.

For every product and engineering systems out there, there is so much to learn just uncovering the mindset SpaceX has in adhering to these principles and being a role model to deliver disruptive innovations.

While one could write in depth on each of these principles, I wanted to share few key highlights on some of them:

1. Purpose Driven - SpaceX’s measurable “why” is so crystal clear with a journey fueled by a relentless sense of purpose. It would motivate anyone to be part of their mission. Their strategic approach and unwavering focus on execution enable the realization of this mission.

2. First Principles Thinking: SpaceX's groundbreaking success in rocket design and reusability, as seen in the Falcon 9 rocket, is a testament to their mastery of first principles thinking. They break down complex designs into fundamental components and reimagine them, optimizing for cost-efficiency while adhering to core principles.

3. Design Thinking Principles: User centric design & iterative prototyping is fundamental at SpaceX. For instance, the Dragon capsule exemplifies this approach with its touchscreen interface and automated docking/undocking capabilities. Why need button’s in the cockpit?

4. System Leadership Principles: SpaceX has been following holistic approach from rocket design to satellite networks to partnership ecosystems with NASA/others. With focus around outcome, it's able to innovate much faster than anyone.

5. Lean Startup Principles: SpaceX follows the build-measure-learn cycle by continually improving its rockets based on real-world data and testing. Starship 2nd launch while many considered failure, SpaceX celebrated this as it met the goals defined post 1st attempt failure.

6. Digital Transformation Principles: Digital Innovation at SpaceX leverages all digital technologies from rocket design, satellite communication and data analysis. Decision making is data-driven enabling efficiency and safety. For instance, telemetry is at the core of their operations, visible during live launches.

Now, beyond these engineering principles, it's fascinating to explore how SpaceX has emerged as a successful and valuable business, currently valued at $175 billion!! My exploration led to the following conclusions.

To understand this, we can view SpaceX not merely as a space exploration enterprise but as a technology company providing cutting-edge space exploration services. In today's business landscape, we've witnessed trillion-dollar market cap technology companies exemplifying a 'winner-takes-all' mentality through a “Platform strategy”. The platform economics has already demonstrated how services became faster, better, cheaper with scale. The successful emergence of platform players like Google, Amazon, Microsoft, Meta and few more were felt across the industry. Few notable trends were: Overall cost of the service came down, Rate of innovation increases, Addressable market expanded, Partner eco-system expanded.

SpaceX's triumph is not solely a result of the guiding principles discussed earlier, it is primarily driven by their skillful implementation of a Platform Strategy. Before exploring how SpaceX implemented this strategy, let’s explore their current success metrics as viewed through these lens: a) Cost to launch b) Service Agility - New Service expansion, Go-To-Market, Enabling partner eco-systems.

Cost to Launch:

How much does it cost to launch spacecraft to carry payload to orbit ? Now, why these metrics are so important. NASA, enormous budget funded through tax payers is so positively influenced due to the innovations driven through SpaceX. The iterative innovative development with “fail fast” concept enabled SpaceX deliver reusable rockets. Reusability of rockets has changed all the economics. Again, the platform approach here includes leveraging Falcon 9 Rapid Reusable Reliable Rockets (4R) built on top of the learnings from Falcon1 and then extending the same to Falcon Heavy. Through efficient use of technology, rocket with fuel design and operational efficiency, cost to launch is drastically coming down. The most notable difference is SpaceX’s Vertical Integration approach - Build everything in house, deliver end to end, avoid overhead of supplier dependency and risk.

See the charts below articulating how SpaceX is transforming space exploration economics.

Industry metric : Cost per kg of payload to Lower Earth Orbit(LEO).

SpaceX : 2500$ via Falcon9, 1500$ via Falcon Heavy, estimated 100$ via Starship (leading to 10$).

In Contrast: 44 times cheaper than NASA Space shuttle.

SpaceX : Falcon 9 Development Cost including Falcon 9 Rocket ~ 390M$ and if the same rocket were to be built by NASA with the traditional development methods it would cost around 4B$.

Another metric to look for is cost per seat for an astronaut to get a ride to ISS. NASA was paying $80M per astronaut in Soyuz, Russian shuttle to ISS.

Service Agility - New Service expansion, Go-To-Market, Enabling partner eco-systems:

SpaceX's extensive range of services and missions showcases its ability to cater to various space exploration and commercial spaceflight needs. A successful platform strategy demonstrates rapid expansion of capabilities once foundational building blocks are in place. The business goal is accomplished when you realize that ongoing innovation expenses are addressed through new service expansions. Secondly, when your platform enables flexibility to shift R&D expenses to next generation product from current product line without compromising customer outcomes. The fleet of services expanded at SpaceX dramatically when the foundational building blocks came together: Reusable with re-flight rocket technology stack, Dragon capsule readiness for crew missions with automated docking, and Starlink Constellation Network infrastructure.

The iterative development and delivery with new services is now paving the path for faster pace of launches, in-turn creating “S-Shape” growth anticipated with Platform delivery mindset.

Here is a snapshot of categories of service delivery with close collaboration with partners:

1. Orbital Payload Deliveries & Ride-share Missions: SpaceX has been actively delivering various types of payloads to orbit. This includes communication satellites, Earth observation satellites, and scientific research payloads, cargo to ISS. Below visualization articulates the power of SpaceX payload delivery. Through ride-share business model, SpaceX has demonstrated a great source of revenue stream.

2. International Space Station (ISS) Resupply Missions: Since May 2012, SpaceX's Dragon spacecraft has been a regular visitor to the ISS, delivering cargo and supplies. SpaceX's Dragon spacecraft is capable of carrying up to 6,000 kgs / 13,228 lbs of cargo to the Station and returning 3,000 kgs / 6,614 lbs of cargo back to Earth. To date, Dragon has made over 29 trips to the ISS.

3. Crewed Missions to ISS: In a significant achievement in May 2020, SpaceX restored human spaceflight to the United States by launching NASA astronauts to the ISS aboard the Crew Dragon spacecraft. This mission was notable as it was the first time a commercial spacecraft had carried astronauts to the ISS. As one could see the same Crew Dragon spacecraft has been designed to enable space tourism as another service extended to what enabled NASA missions.

4. Starlink Constellation - SpaceX offering broadband internet service leveraging the world’s largest constellation of highly advanced satellites operating in a low orbit around the Earth. These satellites delivered using the SpaceX Rocket and payload delivery framework. The unique differentiation of this service offering includes Internet on the Go (Boats, Hiking trips or any remote locations). This subscription service plan includes unlimited high-speed data with no long-term contracts or commitments. This service now extended with partnership with Telco providers like T-Mobile to offer Smartphone connectivity via the Starlink Satellite network.

   

5. Starshield - SpaceX delivers similar capabilities as Starlink but for Government entities addressing National security needs. It leverages the same platform stack by launching Starshield satellites, global communication network to government users and delivery of custom payloads as per government regulatory needs.

Credit: Internet Data

A Wall Street Journal report from August 2023 noted that SpaceX generated $1.5 billion in revenue during the year's first quarter and eked out a $55 million profit. That's a notable improvement for a company that posted annual losses over the past two years ($559 million on $4.6 billion of revenue in 2022 and a $968 million loss on half that revenue in 2021).

Meanwhile, Starlink is nearing profitability on a stand-alone basis. Musk reported that the company reached breakeven cash flow in late 2023. That business unit's revenue has surged six-fold over the past year to $1.4 billion.

As observed till date, SpaceX’s path to Platform execution strategy is through Fail-fast, Iterate, Measure, Learn and Improve. From Falcon1 first successful orbital flight to Starship, largest rocket ever leaving the pad, and crew missions to ISS and beyond, SpaceX is at the cusp of reaping the disproportionate value creation with growth of the platform. My immediate reaction to this tremendous value creation was attributed to increase in scale. But in any platform, including the one most of the digital economies operate on, the returns is usually seen when the appetite for learning and improve scales. As I further researched, came across great paper by Atif Ansar & Bent Flyvbjerg, who have articulated this concept extremely well. SpaceX’s approach has been around experimental learning:

From research paper: “Experimental learning is different from learning-by-doing or trial-and-error learning: “in experimental learning, variation in conditions is planned and intentionally introduced to produce insights about input output relations” typically about unknowns” Trial-and-error, in contrast takes place in “on-line” environments typically to refine ongoing knowns or regular activities.

SpaceX demonstrates “staircase” sequence learning as each experiment aids the next with crystal clear measurable outcomes. While it might look like a failure for external audience, SpaceX defines a clear outcomes for these experiments. Starship 2nd launch had headlines as failure while SpaceX celebrated success as it accomplished its defined outcomes.

Now, I took the liberty to create a platform view for SpaceX with few of the building blocks as I understand. It’s an attempt on my part to map to a software as a service delivery platform stack. I am sure many of you would co-relate this architecture!

In summary, Space exploration industry is at the cusp of transformative disruption through innovations at SpaceX and others. Hard to predict what future holds but for sure it’s the most exciting times to see science fiction becoming reality as each event is the first milestone ever recorded in the history of mankind. I am fascinated and motivated by the flawless execution of what I think is the platform strategy at SpaceX.

Every day, I ponder the definition and SLA for any space exploration as a service readiness. SpaceX, post GA of their service offering, there has been 0 Customer Found Defect (CFD) while maintaining exponential growth in delivering the outcomes to their consumers.

While we could get ready with our Starport (aka passport) to travel anywhere on this planet within less than hour, it’s worth challenging ourselves in our own field..

What entails a 0 CFD platform? Thoughts..

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Humanity has grown through its hardships, with each deep wound making it tougher for the next generations to heal. No matter how many technological breakthroughs we make, we're still failing to address conflicts, prolonging the agony and distress that people endure. Our species has come a long way, but always at a steep price — and it doesn't look like that's going to change anytime soon. As I begin to write, my usual excitement for the latest technological breakthroughs is dampened by a sense of sorrow, influenced by current events worldwide. I hope that we, as a human race, can find even a sliver of compromise in our rewriting of history to pave a path toward future healing.

Last week, another issue took the spotlight. We gave birth to Alien Intelligence this year which is infused with humanity’s collective knowledge up to this point. This has stirred a ‘fear of the unknown’ among us, with concerns it might one day develop a super-intelligence that surpasses our own species. Global consortium concluded regulatory oversight is a must now to ensure the right level of guardrails are put in place with AI as it co-create, co-exist with Human intelligence. It's interesting to watch the two sides to this: on one hand, there's the need for regulation, but on the other hand, too many rules might slow down innovation. I'm going to leave this argument for the future to figure out. It was interesting to see AI leaders release powerful AI models within few days after signing the Safety act to regulate!

As 2023 comes to a close, I find myself pondering: Are the initial stages behind us? Have we played through the early, perhaps simpler holes? Do we understand the lay of the course well enough, and are we mentally prepared to tackle the more challenging back nine?"

As I consider the future, I realize that definitive answers are elusive. If I do come across a promising insight, I approach it with caution, knowing how unpredictable the future can be. In search of some guidance, I turned to frameworks that can help navigate through established routes.

According to the Gartner Hype Cycle, General AI is currently in the "Trough of Disillusionment" stage, and I find this analysis accurate. We've come to a point where, although the innovation engine is humming,, the actual application of General AI for widespread productivity is under careful examination. Experiments underway to build an acceptable range of trustworthiness, and most importantly proof of value creation at reasonable ROI is calculated. On the technical side, focus is on implementing strong security measures, developing models tailored to specific business needs, and employing techniques like Retrieval-Augmented Generation (RAG) and Prompt Engineering to assure the reliability of General AI systems. What I'm watching closely is the "Time to Value" gap – the time it takes to move from high expectations (around mid-2023) to a level of steady productivity. The question is, will this transition span years, or will it stretch into decades? What I saw today from Open AI Dev Day, we are sure to have substantial productivity gains in a few years!

Before exploring the stages of the Hype Cycle, it's important to acknowledge AI's potential to equalize opportunities for everyone, regardless of gender, race, or economic status. From my own hands on experience and observations of how people utilize these current sets of AI tool kits, the impact of AI assistants in enhancing our writing, creativity, coding and ideation is extraordinary. This great research study amplifies it. Worth a read!

Innovation Trigger Phase:

In November 2022, the Innovation Triggered millions overnight to subscribe to ChatGPT. This marked a turning point offering a conversational interface that is almost indistinguishable from human interaction. We accepted this phase rather quickly, experimented, adopted it in our daily needs of creative work and predicted in our own view of the situation as a technological pivotal moment in history. Pundits called this the Digital Renaissance, Economists predicted major disruption to the human labor market immediately, especially the knowledge worker categories and long term displacement. AGI predictors revised the target dates quite substantially based on these innovation triggers.

Peak of Inflated Expectations Phase:

GenAI technologies reached a Peak of Inflated Expectations in Mid 2023. Investment and strategies re-defined everywhere in urgency, M&A and alliances emerged demonstrating incumbent dominance, every product innovations exhibited unparalleled creativity setting new levels of expectations. My personal monthly subscriptions to consume GenAI related technology services pivoted dramatically ;) [ 20$ ChatGPT, 10$ MidJourney, 5$ ElevenLabs, 8$ DreamStudio, 6$ D-id.com, 18$ OpenAPI ]

Multi-billion $ investments in AI startups soared. Pitchbook data indicated the value of funding for AI companies climbed 27% globally in the 3rd quarter compared to last year. Amazon stake with Anthropic 1.25B$ investment and Meta’s constant drive to deliver innovative open source stack for broader adoption, Elon Musk’s xAI formation and many more, all to gain a portion of the inflated expectations from GenAI technology.

Every extension of the foundational models recently delivered enabled a new set of use cases and new set of predictions to disrupt the labor force. Co-Pilots, PersonalAI assistants, AI Tutors, Humanoid robots and many more tool kits demonstrating what would come next. Now, the race is to be the best multi-modal foundational models (vision, audio, video etc) and there is a heightened level of urgency to deliver this and take the throne of “best model yet” beating GPT-4v, GPT4-Turbo now!

Trough of Disillusionment Phase:

While the promises of productivity gains are unimaginable with GenAI, the reality of when we hit that plateau of productivity curve is yet to be seen. We now have entered the Trough of disillusionment phase.  Few key data points that I feel would resonate this state.

• Unable to find a broader successful sticky use case leveraging GPT4 or any other models. We don’t have an Uber app moment yet on the GenAI LLM platforms. We do have lots of applications getting enabled with GenAI as co-pilots but we have not found an AI-First-Use-case. The chances of this happening in consumer space is much higher as wider proliferation enters our digital life.

• Enterprise readiness: No P&L demonstrated an impact with GenAI either to the bottom line or top line. Nor do we see margin impacts due to additional R&D expenses for model training or finetuning. While GenAI Infrastructure Cloud providers are spending in billions, the impact for end enterprise P&L is not yet seen.

• Trust score: Successful stories of decision making on outcomes or complex automation of tasks via GenAI yet to be seen. Skepticism due to hallucinations is a real challenge to gain trust on the GenAI Applications.

Now, let’s look at what it would mean to transition from Trough of disillusion to Slope of enlightenment in the Gartner Hype cycle for GenAI. Being cautious on predictions, I am attempting to share my perspective on how genAI would transition in this phase of the hype cycle.

Transition to Slope of Enlightenment Phase:

Some key category of drivers:

1. Customer Centricity: Use Case & Value driven. Focus will shift to solving problems end to end with well defined sticky workflows and leverage multi-modal capabilities. We already started seeing this with OpenAI releasing CustomChatBots, an easy way to create your own GPT with your own data. We would see companies building custom LLM for a specific vertical and offering that as a service. Example:https://www.harvey.ai/blog Generative User Interfaces: We are very much accustomed to the current conversational interface (like ChatGPT) which spits responses. We are yet to get into Generative UI for everything we do digitally. Perplexity, Inflection AI are really leading the pack on this in my view. These types of interfaces will lead to broader adoption of complex workflows that lead to full automation of tasks, driving value, and rising productivity, all with Natural language.Personal AI Assistants: Always on tutor, mentor, coach, companion will be the future. Each one of us will have our own AI assistant trained on our data and tunable to our needs. Like Inflection AI, there will be many more that will focus on this consumerization of GenAI.

2. Trust, Trust, Trust: Overcoming hallucination risks With a wide array of research communities exploring, experimenting and defining possible solutions to overcome certain limitations of current foundational models, there will be a path to gain more trust. One of the approach which seems to gain momentum is RAG ( Retrieval Augmented Generation).

Emerging reasoning techniques like Chain-of-thought, tree of thought enable model capabilities to perform complex reasoning tasks aligning to customer expectations. My go to reference is prompt engineering techniques captured here.

Constitutional AI: Anthropic are pioneering this idea of Aligning an AI's behavior with a "constitution" defined by human principles - things like avoiding harm, respecting preferences, and providing true information. This constitution shapes how the AI acts.

3. Enterprise Readiness: To reach the level of maturity where Enterprise productivity is uplifted, there are quite a number of innovations to be driven. While many are set in motion with abundant investments, consolidations and alliances, I am sure there is more to be done.

Cost & Accessibility to train models: We are seeing emergence of AI First Infrastructure companies like Coreweave, Lambda Labs etc which provide GPU farms at a reasonable cost with pay as you grow models. Along with these, incumbent major cloud providers gear up their AI Infrastructure services with easy to train, build, deploy LLM in a secure enterprise infrastructure domain. Plus these large Cloud providers will start cobbling startups to drive their innovation engine at a much faster pace and eventually drive lower cost to Enterprises. Open AI GPT4 Turbo with 128K context length is 3xCheaper than GPT4!

Enterprise focus Services - Bing Enterprise, Google Vertex AI Enterprise search, Open AI Enterprise subscriptions for Teams (rumor) or Open AI Apps Marketplace (rumor). Introduction of various co-pilots in the current set of enterprise productivity tools like Google suites, Adobe, Salesforce, Servicenow, GitHub, O365, SAP etc is a natural progression towards AI first use cases for future. Adoption of these into the Enterprise ecosystem will be possible when these co-pilots prove its safe, secure and data privacy is maintained while providing Enterprise specific outcomes.

Domain specific - purpose led models : This will be the key differentiator for Enterprises to evolve and gain more trust, confidence in GenAI pertaining to their data sets, their automation needs, and their productivity improvement plans. Building these domain specific GPT’s are made easy as seen in Open AI Dev Day.

Level 0 to Level 5 Autonomy with AI Agents: As the enterprise journey progresses, GenAI Agents would be the next wave of innovation which would dramatically improve productivity in Enterprise settings. For example: Microsoft AutoGen framework competing with LangChain framework for LLM agents. This framework enables simpler interfaces to external LLM agents/databases to perform a complex set of tasks.

In summary, We have to admit that AI we knew from the past was linked to repetitive tasks, the type of work that could be substituted by human’s left brain tasks. Now, GenAI is participating in the right side of the brain tasks (co-creation, idea generation, logical thinking via multi-modal support). I came across this great analogy “Gen AI will be the bulldozer for the mind” Eventually, GenAI & its future extensions with GenAIAgents (caution:me coining this term!) plus these traversing the adoption curves in becoming general purpose technology, the impact on human productivity & society as a whole is unknown. When we see Enterprises or Consumers articulating their P&L dramatically change due to productivity gains with GenAI and its sustainability for the long term, we are all set to transition from the Slope of Enlightenment to Plateau of productivity.

We got a glimpse of the art of possibility at Open AI Dev Day!

Exciting times indeed, we, the humans, will witness this!

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In recent times, the world has witnessed remarkable advancements, from the vastness of space to the realm of digital entertainment. Again, human intelligence is in full control commanding machines to accomplish our missions - as seen recently..

India's Moon Marvel with Chandrayaan-3: India has once again taken a bold step in space exploration. The Pragyan rover, dispatched from the Chandrayaan-3's Vikram lander, has made its mark on the moon's elusive dark side. This $75M mission, skillfully orchestrated by the ISTRAC team from ISRO in Bangalore using the new MOX facility, is a testament to India's rising prowess in space exploration. My personal connection to ISTRAC adds a layer of pride, having seen the relentless dedication of scientists firsthand. With predictions estimating the space industry's worth at $1 trillion by 2030, India is well on its path to gaining a significant market share.

SpaceX: The Sky's No Longer the Limit: SpaceX's relentless pace continues to astonish. With a whopping 58 successful rocket launches this year, at the rate of a launch every 4 days,  including 53 Falcon 9s, three Falcon Heavies, and a groundbreaking Starship launch, Elon Musk's dream of a multi-planetary species is rapidly approaching reality.

LK99 - The Superconductor Buzz: The world of material science was recently abuzz with LK99, a room-temperature superconducting marvel. Its potential to reshape industries is vast. While it remains to be seen if this innovation will stand the test of time, it's a glowing example of our constant pursuit of technological breakthroughs.

Meta's Meteoric Rise and Shift: Technology adoption can be unpredictable. Meta’s Thread, in just a week, saw a surge of 100 million users, only to settle at 23 million a few weeks later. This trajectory demonstrates that even a replication of existing technology can create unprecedented demand. The tech landscape is ever-changing, and the appetite for new digital tools remains insatiable.

The Mr. Beast Phenomenon: The digital era has birthed influencers with immense power and reach, and Mr. Beast stands as a testament. His recent "Olympics" event not only sparked controversy but also emphasized the monumental influence of social media personalities. With staggering metrics such as 100 million subscribers, 10 Billion video views and an average of 20 million views per video, he's a force to be reckoned with. And with a click of a button he can summon genZ to be a part of his missions, blindly.

For the last 40,000 years, humans have been the main species on Earth. The catalyst was the cognitive revolution, a transformative event that equipped our species with unparalleled skills. While the exact triggers of this revolution remain a subject of debate among historians, it's universally acknowledged that it enabled Homo sapiens with a unique capacity for adaptation, large-scale cooperation, and innovate. One of our best tools was language, which let us share thoughts and work together even better.

Today, despite the existence of numerous powerful mammals that might overpower an individual human in a direct confrontation, it's our collective cognitive smartness that sets us apart. This distinction is eloquently discussed by my favorite historian Yuval Noah Harari in his various books, podcasts, and articles. Harari consistently highlights the impending challenges posed by the rapid advancements in Artificial Intelligence and Machine Learning. He introduced the term "Alien Intelligence" to describe the novel form of cognition exhibited by the latest generation of AI, hinting at its potential to rival, or even surpass, the cognitive attributes that have long been the domain of Homo sapiens.

I really liked this term since it's a new form of intelligence demonstrated by machines, leveraging human intelligence and till date we don’t have a full understanding of how it's able to do it even though we created the foundation for it.

Looking back at history can teach us a lot, like understanding why Neanderthals, who were a lot like us, disappeared. With this in mind, we can think about how this "Alien Intelligence" might either compete with us or live alongside us. Our big change, the cognitive revolution, gave us three main traits that helped us become the leading species on Earth. Thanks to Harari's insights on this which helped me articulate.

1. Create & believe stories : We humans have a special skill. We can make up stories and get everyone to believe in them. This helps us work together in big groups. Think about money. It's just paper or numbers on a screen, but we all agree it's valuable. This idea of money, something we all made up together, lets us trade and help each other in ways no other animals can. Stories, traditions, and knowledge are passed down generations, ensuring continuity and collective memory. These shared beliefs and narratives laid the foundation for the rise of complex societal structures, from tribes to kingdoms to nations.As long as we can create stories and deliver those to convince the masses, we will have cooperation that no other species could ever do on a large scale.

2. Adaptability: Homo sapiens' adaptability goes beyond just physical changes. It encompasses cultural, social, and technological shifts that have allowed us to flourish worldwide. Our success in diverse environments wasn't due to drastic genetic changes but rather our cognitive abilities. We crafted tools to weather climatic challenges, analyzed edible foods from toxic ones, and built communities by sharing knowledge. Perhaps the most impactful tool we developed was language. It enabled us not only to communicate immediate needs but also to share lessons from the past, plan for the future, and articulate abstract concepts.

3. Ethical grounding : As humans evolved, we started understanding the importance of fairness and working together. Early on, we set simple rules like sharing and helping to keep our groups united. As we grew into bigger communities, more rules and traditions were created to guide behavior. These shared beliefs and rules helped us trust each other and live peacefully. This understanding of right and wrong played a big part in making humans the leading species on Earth.

With this backdrop, let’s look at this new Alien Intelligence(AI) revolution that has engulfed us in 2023. I have been passionately following this trend through research, self learning and experimentations and making attempts to capture my insights regularly here. Last month yet another set of waves of innovations was unleashed in the AI realm creating excitement across academia, companies reaping growth in business, VC funding redirected more into AI incubators and many more.

Gartner’s Hype Cycle for AI was published in July 2023 highlighting the innovations fueling Gen AI development and in-turn potential business transformations because of Gen AI. It’s fascinating to see predictions for the next 2 to 5 years.

To explore recent key innovations, I refer back to the framework articulated in my previous blog. With this framework, it aids me to distill my understanding as articulated below.

Hardware Innovations:

1. NVIDIA has become the outright winner in the GPU race with daunting 171% year over year growth in revenue with ninefold increase in profitability. Currently, close to 80% of all AI Chips sold are from NVIDIA. For upcoming quarters, its projecting to triple the level of sales compared to last year, all thanks to GenAI training needs and major buy from cloud services infrastructure providers. At 1.14 Trillion $ market cap, NVIDIA is unstoppable now! On top of this, NVIDIA has made close to 850M$ investment into four major AI startups for potential in-organic growth.

2. Nvidia continues to bring heat to the AI chip game. The company unveiled a new blazing-fast processor called GH200 Grace Hopper Superchip designed for massive AI models Nvidia says the cost of powering LLMs will ‘drop significantly’

3. Birth of MatX - a team previously responsible for Google’s TPU chips and the Palm model with the mission to “make AI better, faster, and cheaper by building more powerful hardware.

4. AI chip startup Tenstorrent closed a $100 million strategic financing from the likes of Hyundai and Samsung. Tenstorrent is led by top chip designer Jim Keller, who has worked at Apple, AMD, Tesla and Intel.

5. Anthropic, the prominent generative AI startup co-founded by OpenAI veterans, has raised $450 million in a Series C funding round led by Spark Capital.

Last month has been the era of open source contributions with floodgate opened by Meta AI research groups. This trend of unleashing pre-trained models with flexibility to further fine tune them based on custom domain specific needs, will be a game changer to driving broader adoption of Gen AI. Now, enterprises can explore Gen AI use cases with full control over the data privacy needs.

"It’s very hard to beat the intelligence of the masses”

Here are some of the innovations in the Platform, Foundational Models and Model Hub domains:

• Meta AI and Microsoft have joined forces to introduce Llama 2, the next generation of Meta’s open-source large language model. Llama 2 is available for free, both for research and commercial use.

• Code Llama-2 LLM got released few days back - specifically tuned for code generation, debugging etc. The 7B and 13B base and instruct models have also been trained with fill-in-the-middle (FIM) capability, allowing them to insert code into existing code, meaning they can support tasks like code completion right out of the box.

“Open-source models are faster, more customizable, more private, and … more capable. They are doing things with $100 and 13B params that Google struggles with at $10M and 540B. And they are doing so in weeks, not months.”

• Single Multi-modal LLM - Meta’s one single multimodal model for the research community that performs speech to speech translation, speech to text and text to text and vice versa. All this is in one single seamless multimodal model and supports 100 languages.

• OpenAI just launched fine-tuning for GPT-3.5 Turbo. This is their biggest product update which will change the future of AI applications forever. It gives you the ability to train the model on your company's data and customize it to create unique and differentiated experiences for your users. And run it at scale. Open AI is leveraging Scale AI as preferred partner.

• My favorite plugin till date is on code-interpreter from Open AI. This really gives a big boost in productivity to experiment and try anything new. Not wasting in writing up any code and use this plugin to perform all the essential workflows to generate the final code. It feels like ChatGPT is thinking lot more than before. It thinks through like how human does when it makes mistakes and fixes them, apologizes first. Recently came across an excellent use case to create simulators to explain a mathematical concept. Check this out:

• Google Gemini in the horizon: The goal Google Deepmind is exploring to overcome the usual limitations of large language models which is, it learns from the static text of the internet. While human intelligence is possible also with physical experiences of the world which is what Gemini is thinking of bringing into the AI models. Gemini has high expectations as it would be built leveraging the AlphaGo expertise and innovations. More here

• Hugging face - the Hub for AI models and eco-systems for bringing AI applications alive has been in the news lately with additional 235M$ funding from Salesforce raising its evaluation to 4.5B$. To me, this is one amazing hub to bring open source AI models and other communities together to drive innovative solutions.

• Recently came across Abacus.AI while researching the challenges of context length with open source LLM. Today many support up to 2K tokens (1500 words) as input. Giraffe is an open source LLM that is built on top of LLama-1 with extensions to increase the context length to 4K & 16K. Abacus.AI have released these models in HuggingFace for the research community to explore further. Each of these steps just overcomes the limitations for custom LLM rollouts with enterprise private data sets.

Let’s look at some of the key advancements in building applications leveraging all these innovations at the platform, infrastructure layers. While there are many to discuss, I personally found these very fascinating and demonstrating the power of these AI models.

• HeyGen 2.0 released its newer Avatar creation model that is getting very close to reality now. It will become very hard for us to distinguish between real person;s video and AI Machine generated video content.Check this out

• Elon Musk Live event with the latest release of Tesla FSD 12 demonstrated the power of self-learning and decision making by the AI models inference locally in the Car on that constrained compute resources. 36 fps with 8 cameras providing all the inputs to make real time decisions just like how a human would do. Even though the models were never coded to handle say round-about or bicyclist crossing, the model was smart enough to learn based on immense car driving data sets used in training. This is the closest real-life AI application proving machine intelligence keeping ethical considerations at the top of its decision making. See here

In 2023, one of the standout experiments in the GenAI field was Stanford's "Smallville," which was recently made open-source. I'd mentioned this experiment earlier this year when 25 AI Agents, powered by ChatGPT base models, were tested in a 48-hour digital simulation. These agents, given basic roles, learned about their digital world, making decisions, forming friendships, and even experiencing "romantic" connections. It was fascinating to watch them interact, showcasing behaviors that were collaborative and, at times, quite heartwarming. While individual Large Language Models (LLMs) can show unique behaviors, multi-agent interactions are an entirely different level of complexity. This experiment hints at how groups of AIs might form something akin to a digital society.

One of the exciting possibilities this opens up is simulating financial markets using AI agents. There's talk of "Agent Based Modeling (ABM)" where AI agents simulate market behaviors, giving insights into potential market shifts. With the development of more specialized LLMs and the rumors of GPT5's release, the AI world might soon see a shift towards models that can make multiple types of decisions in various scenarios. These advancements raise the question: How might this "Alien Intelligence" one day challenge humans' dominant position on Earth?

Reflecting on the traits that made humans dominant—our ability to create and believe in stories, adaptability, and ethical grounding—we can see AI making strides in these areas. AI models now can generate stories that can easily convince humans. They've shown adaptability, as the Smallville experiment demonstrated. However, ethics is a challenging area. We've built AI to recognize our ethical standards, but they don't "feel" or "understand" these concepts as we do. They might, in time, develop their own ethical guidelines for interacting with each other and with humans.

To sum up, current GenAI models generate content based on patterns, often missing the deeper context that humans naturally grasp. But as technology evolves, these gaps might close. It's also worth watching how regulations might influence the pace of AI advancements.

I came across this interesting visualization about the potential future of AI. While I don't necessarily agree with all the timelines, the phases seem spot on. What are your thoughts? Are we already getting into Hybrid Intelligence for main stream use cases?

Are we ready to welcome this Alien Intelligence, now ?

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2023 is a memorable year. Disruptive innovations have kept all of us on our toes trying to connect the dots, build a view on the future and for sure a great topic for dinner table conversations. Being a system leader with constant learning as pre-requisite, I have embraced this new wave of digital renaissance and tried my best to capture my understanding.

Here are set of LinkedIn articles that have been published. I will keep appending this on a monthly basis with new articles. Enjoy!

1. AI Collaborators: Personalization at Scale

2. The AI Gold Rush: Harnessing Collaborative Intelligence

3. The Digital Gurukul — AI Guru with AI Shishya Parampara ?

"I think a lot of the fears around AI are predicated on the idea that somehow there is a hard takeoff, which is that the minute you turn on an AI system that is capable of human intelligence or super intelligence, it’s going to take over the world within minutes. This is preposterous," exclaimed Yann LeCun. Contrarily, Yoshua Bengio voiced, "What Max and I and others are saying is not, necessarily, there’s going to be a catastrophe but that we need to understand what can go wrong so that we can prepare for it" The ever-pragmatic Melanie Mitchell added, "The whole history of AI has been a history of failed predictions. Back in the 1950s and 60s, people were predicting the same thing about super-intelligent AI and talking about existential risk, but it was wrong then. I’d say it’s wrong now" Max Tegmark, with a hint of caution in his voice, questioned, "What do you actually think the probability is that we are going to get superhuman intelligence, say, in 20 years, say, in 100 years? What is your plan for how to make it safe?”

These brightest minds got together at Munk debate to settle the resolution - "Be it resolved, AI research and development poses an existential threat" . The opposition won by a 4% margin, changing more minds among the audience compared to pre-debate opinions. Lex Fridman and Marc Andreessen had a similar dialogue and  one statement stuck with me, I quote Marc saying “I think we're going to need to start thinking about using military-grade security to protect AI data centers. Because if you have a really powerful AI model, and you train it on a massive dataset, and then you lose that data center, that's like losing a nuclear weapon. It's that dangerous.”

Now, where do you stand on this AI risk debate? Do we need government regulation now, or should we adopt a wait-and-see approach?

Every four weeks, I delve into a new facet of AI, sharing my findings and musings. I enjoy reading, experimenting and sharing my learnings in any opportunity possible. All these are my own thoughts with no influence on what I do professionally. Let’s look at how the last four weeks have been..

This time, my Indian roots led me to draw parallels with the Guru-Shishya Parampara (Gurukul framework) from our rich mythology. This framework is a sacred teacher-student relationship that forms the cornerstone of knowledge acquisition and moral upbringing.These enlightened Gurus, powerful enough to drive a nation to glory or bust. Despite their wisdom, have been known to err, resulting in far-reaching consequences, as seen in the epic tale of Guru Dronacharya from Mahabharata.

Are we in the process of creating a Digital Gurukul? As we keep aligning these AI models to be a clone of human intelligence and beyond, with all datasets we got including our myths and realities, our cultures and traditions, our success and failures and with IQ’s beyond Einstein - Would we end up creating AI Guru’s? I've been contemplating the next wave of innovation: AI Agents, instructed by these centralized AI Gurus, undertaking specific tasks. This Digital Gurukul is on a global scale, transcending geographical and cultural boundaries. The fear expressed by some scientists is of an uncontrolled AI Guru, whose intelligence and goals may not align with ours, potentially driving AI Agents to destructive tasks. Do you see the same way?

Having said that, let's take a look at what all has been happening in last four weeks that gives any sign how we are pushing the envelope here. To gain knowledge and experience in these fields, I have embraced experimentation and learning. To stay abreast of the continuous influx of disruptive innovations in both the commercial and research realms, I have adopted a simple mental model.

From this simple mental model, I try to find ways to accomplish these below outcomes and see what innovations happening in which areas of the stack. This will help to ensure the business outcome based exploration is performed.

Outcome 1: I like to build a proprietary application that will be accessible to our end customers. The purpose of this application is to facilitate conversations between the users and our systems, enabling them to perform tasks that enhance productivity and operational simplicity. To achieve this, I aim to create a custom-trained foundational model, which I refer to as the "brain," utilizing my private data sets specifically tailored for this application. To carry out these operations effectively, I plan to leverage an end-to-end (e2e) platform that offers flexibility in utilizing both open source and closed source foundational models, along with a comprehensive operational toolkit (ops). Ensuring the security of our private data sets and trained outcomes is of utmost importance, and therefore, we must establish robust security infrastructure.

Outcome 2: I just want to leverage this e2e application which takes my natural language and translates into specific outcomes - my simple english text into Sanskrit language or take my text to create a video or audio. I care less about how it all happens as long as it boosts my creativity or just enables productivity boost.

Outcome 3: I am unclear on which foundational model to use for my applications. I want to experiment with various open source models from a marketplace or Hub. Most importantly, I want a collaborative environment across my company with private datasets, common models sharing, avoiding reinventing the wheels, and common operational tools shared across teams.

Outcome 4: I want to use as a service frameworks to streamline my ML dev tasks and ML ops tasks to ensure continuous delivery of high performing production models. Naturally, this is critical when I am aiming to deliver an e2e proprietary application for my end customer base incorporating into my own existing service/product.

Let me give a quick summary of innovations on each of the layers of the mental model.

Hardware Innovations

Analysis points to the rate of computing power needs doubling every 6 to 10 months. At this rate, it will become too hard to train a large language model with more parameters to make it “AI Guru”. As Mr Altman put it in April, reflecting on the history of giant-sized AI Model: “I think we’re at the end of an era.”

• The battle to have more powerful but cost effective GPU/TPU and other AI Chips is on the rise. AMD MI300 is the next closest alternative to Nvidia GPU frenzy demands. Need to see the next few quarters of all these play out.

• Academia created a new model Guanaco, from llama on a single GPU in a day without sacrificing much, if any, performance.

• Creative way to have one uber LFM with a bunch of smaller parameter LLMs that are influenced by the uber LLM. This is where I was fascinated to hear about the “Teacher & Student (Guru & Shishya)” concept adopted in creating this new LLM called Orca, from Microsoft Research. Orca is a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca reaches parity with ChatGPT. Research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.

Platform Innovations

Incumbent cloud providers are constantly gearing with new capabilities on their eco-system platforms to enable anyone to bring in their base model to train or re-train or leverage any existing pre-trained model. Check it out here. for latest trends on GPU computing infrastructure providers.

• Databricks acquiring Mosaic ML is another example of consolidations in the space to drive better platforms for training, inference and all efficiencies needed to build custom models.

• CoreWeave, an Nvidia funded startup, inked 200M$ funding to further enhance its 8000 servers GPU Cloud infrastructure claiming 80% cheaper.

• Startup's like Lambda Labs, Jarvis Labs venturing in building the GPU farms to accommodate the demands for AI Model training.

Foundational Models (brain behind everything) Innovations

Each flavor of LLM are designed to enable unique value proposition. We cannot just take Autoregressive LLM as is like GPT4 and use it for deeper analytical tasks with private data sets. Open Source & Closed Source debate continues with Open source community drives creative methods build cost effective, simpler models as they are constrained with resources while commercial closed source models will advance with tremendous speed of innovation. Do they need to be in lock step ? Not really.

• One of the best Hub’s that I use to understand the happenings on various models across multi-modal is at Hugging face Marketplace. Check it out here.

• Salesforce announced their XGen 7B, a new LLM with an 8k context under the Apache 2.0 license and XGen uses the same architecture as Meta's LLaMa and is, therefore, a 1-to-1 replacement for commercial use! Trained with 1.5T tokens with 7B parameters.

• Inflection AI caught my attention as I was exploring the possibility of Personalized AI to interact with. Inflection AI. the company has raised $1.3 billion in a fresh round of funding led by Microsoft, Reid Hoffman, Bill Gates, Eric Schmidt, and new investor NVIDIA. The new funding brings the total raised by the company to $1.525 billion. This aligns to the notion of building AI Guru’s with all our knowledge so they can help us to perform our tasks better.

Application Development Innovations

This space is just exploding with creativity boost given to the right side of the brain. I break it down into few categories:

• With v5.2 of Midjourney Zoom Out feature, this took the world by storm including 3D extentions creators like Ilumine AI did.

• The Plugin store at ChatGPT is exploding with valuable and productivity tool kits. One of my favorite right now is Visla plugin on ChatGPT Plus which creates a decent quality video stitching from a text prompt.

• Google Music LLM and Meta’s voice box is another recent addition which is exciting to see touching human creativity space more closely.

• One of my favorite creators @XRarchitect who recently posted AI + AR+ QR Code. unbelieve user experience and the potential to disrupt traditional marketing. Try it yourself. Scan the QR code show left of this content.

All these enable individual applications getting exercised to perform a specific task or objectives. As I have been highlighting, the trend will naturally be around interconnecting the LLM or models or applications outside the LLM domain to derive Solution Outcome. There is a new set of innovations in this space which enables creators, developers to build new capabilities. Few of the trends that has caught my attention:

• Function call API from OpenAI: New way to more reliably connect GPT's capabilities with external tools and APIs.Function calling allows developers to more reliably get structured data back from the model. I started exploring with this now.

• New gpt-3.5-turbo-16k model support - 16k context means the model can now support ~20 pages of text in a single request, easier cross linking.

• ChatGPT plugin ecosystem is a great way to derive end outcome by inter-playing the plugins for each task and eventually combining for final outcome. My favorite outcome driven with stitching plugins: a) Watch a video and analyze (b) Draw a diagram articulating the inter-connections of what is seen and analyzed from the video c) Share your view points on this. (d) Now create a new video summary based on this analysis.

• Langchain framework is enabling LLM integration frameworks and widely used in early phases of prototyping.

• Many more..

In Summary, we're living in an era where AI is catalyzing innovations across multiple domains, including text, video, image, music, and language. The technology is boosting human creativity, enabling even those traditionally less creative to push their boundaries. AI-powered tools are helping writers craft better sentences, musicians compose more complex melodies, and designers generate more engaging visuals. In essence, AI isn't just eating the world; it's aiding in its recreation. With constant battle between Open Source & Commercial model race, we might end up with a Large Foundational Model with many domain specific Foundational model working closely, hopefully in harmony!

As we push this envelope, build powerful models to support complex use cases, we might have to look out for any “emergence” state from these systems. This would naturally lead to the path of AI Guru defining its goals with the new emergence of knowledge and in-turn influencing the AI Shishyas. Hopefully, we will have all the controls to guide this process for the better!

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Naval Ravikant recently tweeted a prediction that's been echoing in my mind: 'Everyone will soon have AI Anxiety.' AI innovations are unfolding at an unprecedented pace, with development cycles compressed into days and weeks. I admit, this 'AI Anxiety' has already gripped me. AI revolution is unleashed which appears irreversible. As Microsoft's CEO Satya Nadella suggests, it's as though we're undergoing an upgrade, with AI serving as a powerful steam engine for our minds. As the AI revolution unfolds, the debate on AI's future is heating up, polarizing into two main camps: fully embracing AI's potential or swift regulation to avoid catastrophe. We heard the AI revolution as “printing press moment” and Mr. Musk is predicting AGI within this decade. Now, Eliezer Yudkowsky, MIRI AI Researcher, wants to pull the plug ASAP as expressed in recent TED talk. Debate persist.

Now, one thing is clear -  'AI will not replace you, but a person using AI will.' It's a sentiment I've come to embrace wholeheartedly. With AI as our collaborators, we're poised to unlock unprecedented potentials, and I find myself thrilled at the prospect as I explore regularly through experimentations when time permits. Yes, this excitement is expressed in any opportunity I get having conversations with colleagues, friends and family. We all can predict with some level of certainty that AI will & is disrupting industries, a modern Renaissance, and potentially new flavor of intelligence leading to new Enlightenment. (though perhaps that's me getting a bit carried away...)

As we witness the rapid adoption of AI, the growing excitement around it, and the potential opportunities it presents, even in critical personal areas like healthcare (GPT4 passed radiology board-style tests), it's crucial to remain aware of potential pitfalls. Therefore, we must scrutinize AI systems like GPT-4 and other large language models (LLMs). Do they truly understand the responses they generate? Can we attribute wisdom to them based on their output? These questions are vital as we strive for 'Dependable AI' (Credit to Ved on this term, I really liked it), rather than just 'Generative AI'.

Marc Randolph's tweet is a valuable reminder: "There’s an AI gold-rush underway... But remember, what appears as low-hanging fruit... rarely is." This brings to mind the California Gold Rush of 1848 to 1855, where the discovery of gold at Sutter's Mill sparked a global migration of individuals hoping to become rich.Interestingly, Sam Brannon, the era's first millionaire, who made his wealth not from mining gold, but by selling hardware supplies to miners. We absolutely see AI gold-rush post the discovery of LLM’s & capabilities to generate anything from txt, images, video, audio using the simple plain natural language of choice. Miners are building plugins, applications built leveraging the underlying AI platforms, fine tuning to some degree & the goal to have co-pilot with every software stack we used till date.

The pickaxes and shovels of this AI Gold Rush are supplied by major infrastructure and chip vendors, who provide user-friendly API interfaces to LLMs and robust infrastructures for fine-tuning, retraining, and deploying models at scale. Notable examples include:

• Google's Vertex AI, Microsoft's Azure AI Studio, Amazon's AWS Bedrock, and Nvidia's Nemo Service and many more.

• We've already seen the AI 'pickaxe provider' Nvidia reach a market cap of $1 trillion, akin to Brannon's successful mining equipment store.

• Google's recent I/O event showcased the rapid incorporation of AI models to boost productivity.

• OpenAI's ChatGPT Plus and Bard have introduced a wealth of productivity tools, Plugins, Code interpreter to name a few.

• Meta and LLaMA are democratizing AI models with initiatives like OPT175B.

• Meta LLaMA with 7B, 13B, 33B, and 65B parameters at high 1.4 Trillion Tokens support but with minuscule carbon footprint compared to GPT3.These mini-LLM as I call it, once opened for commercial use could dramatically influence personalized enterprise centric use cases at rapid pace.

• Hugging face released open source LLM Falcon-40B with 40B parameters, trained on 1T tokens and a smaller open source LLM Falcon-7B as well.

• The introduction of LangChain, an open-source framework for building applications and autonomous agents that link LLMs, opens up endless possibilities for creative innovation.

With so many tools out there, a high schooler built a financial advisor chatbot  within a week by fine tuning an existing model with high school level data points and asked GPT4 to provide the code to create a website and host it natively on AWS Cloud Infra. Stories of such innovative achievements are emerging from all corners of the globe and across all age groups.

It’s also quite obvious that to be a dominant player in this AI gold rush, completeness, correctness, and clarity of data is a must have. One can really put guard rails, add knowledge, add skills and continuously improve enterprise domain centric AI Models only if they have reliable domain specific data sets, and a lot of them! Above all, ensuring cybersecurity hooks are in place from day1.

As this advancement penetrates into mainstream enterprises, the question still prevails - Are these AI systems transitioned from just basic intelligence to having wisdom? I ponder this question with a sense of curiosity both technical and philosophical. With mission critical use cases at stake, it's critical to scrutinize before following blindly the hype. Here are my responses to these questions..

Let’s keep the possible emotional attachments to AI systems aside when we see amazing personalized and conversational responses. AI models, at their core, perform based on sophisticated mathematical operations. Simplifying from Wolfram's article, AI models take a corpus of data, perform tokenization (adding a reference id to each word) and location of each of these tokens in comparison with other tokens, relationship between these tokens, the neural network then uses these relationships to predict the next word in a given string of words, with continuous feedback to improve these predictions. As a mathematical operation, the system just performs based on what it is pre-trained with and spits out the next possible word, sentence, paragraph. We all know, the systems do lack a deep understanding on the context, the emotional state of the user, situational awareness.

Intelligence involves analyzing inputs, building a plan, and executing tasks which a well trained machine can perform. However, Human intelligence evolved when Homo sapiens surpassed the intelligence of other human species and dominated the planet. One of my favorite authors, Yuval Noah Harari, articulates meticulously in the book “Sapiens: A Brief History of Humankind” - For many centuries, Human intelligence was tightly coupled with subjective experiences like feelings, emotions and we strive to derive solutions keeping this experience in mind. Consciousness is the ability to feel things, pain, pleasure, love, hate. Its this alert cognitive state in which you are aware of yourself and your situations. Humans are the intelligent species dominating the planet and they are the ones with consciousness. Now, the argument could be: our decision making capacity or intelligence doesn’t need to be driven by consciousness. A great example I came across was plants are intelligent living organisms but they don’t have consciousness. This century surely dictates a new course of intelligence which decouples from consciousness. AI systems could be more intelligent than humans but still with no feelings.

So, based on where things stand today, I feel, we have AI systems which are intelligent enough to perform certain pre-trained tasks and naturally cannot be left for full autonomy with decision making when stakes are high. In the text generation realm, it's best in doing the most “reasonable continuation” of the next word. That is great intelligence to have as collaborators, creators and tutors alongside human intelligence. We can clearly see this applicable for any knowledge base dependent functions - support, documentation, SW development, learning and development, data analysis, visualization etc. This level of intelligence is powerful. We should aggressively embrace it and there will be opportunities to expand into much complex enterprise automation tasks by fine tuning an open source LLM (for better controls), and by cross linking LLM's with agents.

To conclude, I believe these AI collaborators are akin to a new breed of toddlers, learning and adjusting to the world, which in turn forces us to adapt. They can be trained with the right data and use cases, as long as proper security measures are in place. Remember, despite their potential, there remains a 'bit of voodoo magic,' as Wolfram puts it. This is because we don't fully understand why some AI responses are so compelling, despite what the underlying mathematics suggests. Open sourcing the sophisticated LLM's of today in the hands of researches is the way to start to uncover these magic & eventually community will dictate the adoption outcomes. This era of AI is powerful and should be embraced. Change is constant.

We can either stay with it, get ahead of it, or risk being left in the past.

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Considering the widespread usage of AI tools for various tasks, it seems to me that we are entering a phase where it’s increasingly important to emphasize that content is “only made by humans” in order to maintain its appeal to human audiences online. So, based on my experiments and learnings, the following expressions are solely my personal opinion - by human ;).

Following up from my previous posts on AI Innovations (here), the race has been unprecedented in transitioning AI tools into AI Collaborators. A shift is underway from impersonal “it” to a more personified “she/he” just like Jarvis in Ironman. Do you feel that ?

Even though prediction or recommender algorithms gave us a sense of personalized treatment in the past, we are now driving the next wave of “personalization at scale”, unleashing creativity & most importantly automation in every domain. Through generative agents & cross integration of different flavors of LLM’s fine tuned with your own specific data sets, there is tremendous opportunity to be efficient in all segments of the industry especially in the cybersecurity domain. In my view, these advancements are poised to revolutionize the way we perceive time & significantly alter the burden of labor for all across all industries.

“Attention is all you need” started the transformer journey leading to flurry of LLM’s with generative chatbot interfaces. But for AI Collaborators to become much more personalized they need Theory of Mind (TOM) capabilities. TOM as I understand is our cognitive ability to understand the mental states of others without any observable cues like eye contact, body language, voice tone etc. Humans typically fully develop this around 6 to 8 years of age. My recent conversations with Stanford buddies led me to further understand Michal Kosinski’s paper - Theory of Mind May Have Spontaneously Emerged in Large Language Models. GPT4 solved 95% or more TOM tasks compared to GPT3 May 2020 version at 40% success. We can surely argue that GPT4 LLM’s do have TOM, which further leads to some phenomenal personalization use cases. I am imagining a FamilyGPT4Chat which is now fully trained by the families data set, which understands emotional states of all family members and aid in managing tasks accordingly based on emotional states. I tried various TOM experiments on my GPT4Plus interface and it was shocking to see the level of emotional state understanding by these systems. Have you tried this ?

One of the most powerful use cases demonstrating personalization is Khanmigo, AI powered Khan Academy. This tighter integration of well trained LLM into Khan academy workflows enables a personalized 1:1 tutor. This level of meticulous integration including Math (which native GPT4 are not so very powerful), clearly demonstrated how we can for-see future integration of generative agents into not only our daily lives but how enterprises could start incorporating in all sectors of organization functions - Sales, Services, Support, Development, NOC/SOC, Finance ops and many more. Even though each of the teams deliverables would be different it would be really powerful to not to view these are siloed functions but one cohesive system which then leads to build fully automated cross integration solutions still leveraging individual teams generative agents. Do you agree?

Few weeks ago, I was experimenting with AutoGPT to perform a set of home automation tasks like check the wifi performance at home, look at any Comcast outages in my region and then get me results on how good my network is. Now, multi-LLM integration is already underway within a personalized simplified ChatGPT Plus interface - Code interpreter, Fact Checker, Image generation and many more. Greg Brockman, in a recent TED talk, articulates how we could teach these models to learn from us and deliver the right outcomes that we are looking for. My takeaway from it was - if within weeks they now transformed ChatGPT Plus from simple text conversation & generation tool kit into a autopilot multi-step personalized assistants inter-connecting multiple AI systems, then what we could see in months from now, just cannot imagine the pace of innovations. What do you think will come out next ?

This whole notion of personalization at scale is driving a very aggressive innovation cycle by everyone in the industry. Some unique ones that I feel have dramatic impacts to our field (0) GPT4 handles 32K tokens. Work underway to push this envelope to 1M and beyond. Imagine Game of Thrones data as input as your prompt!! (894K tokens). (1) Ghostwriter Chat from Replit (raised 97.4M$ at 1.16B valuation). A development platform helping to code (competition to Github) for you and collaborate with the community via a bounty service. (2) A major battle between teams resolved by Google. Google Brain + Deepmind = Google Deepmind. Now, this is the power of rapid innovations forcing companies to let go of years of friction. (3) Nvidia’s Nemo Cloud service for enterprise hyper-personalization and at-scale deployment of intelligent large language models

Now, the race for “Generative AI as a Service” via the cloud providers (Vertex AI from GCP, Bedrock from AWS, Azure Open AI) and AI chip vendors like Nvidia are all coming to the mix.

As an enterprise, one has to see how to play the long term approach on this by avoiding the usual gold rush mindset. One has to keep in mind the high risk of exposing PII data when attempting to fine tune a pre-trained model with an enterprise specific dataset or employees finding ways to use 3rd party AI tool kits within internal systems exposing vulnerabilities & exploitation opportunities. At the same time, AI driven Security Systems will allow for radical advancements in efficiency and real-time trust decisions. A topic on security implications is an article by itself. Stay tuned.

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Life journey is unpredictable, and that's what I am sure keeps us all going. We hear this more often — Change is constant. What is the most relevant outcome of this? You all agree that our mind-set shift to embrace change is the most valuable outcome . So, easy to say than getting it done, you say! That's true. Ask a marathon runner, “what’s your wall and can you overcome it?” Ask a golfer, everyday is a new day “what’s your plan to excel?” Now, ask a leader or a professional in the digital era, who believe they adapted to constant change, “how high is the wall and what would you do differently to succeed?”. My blog here is trying to share interesting experiences recognizing this, and then exploring ways to disrupt, innovate, find solutions to break the barriers. Transformation is not easy unless we know deeply the challenges to change and sensitive to know that “we are hitting the wall and how high it is”. It's been a forcing function for me to challenge the status quo, strategize, and move forward to an outcome which has well defined purpose.

Many historical moments in 2019, but one that motivates and challenges me to the gut is sub-2 hour full marathon run by Eliud Kipchoge! We are born to Run, there is no doubt about it. Homo Sapiens overcame Neanderthal with sheer endurance and less of power play. Homo Sapiens intelligently forced an antelope to death by pushing them for a running race. In modern era, Tarahumara tribes in Mexico, named as Running men, have really demonstrated, humans have no limits when it comes to running long distances. 400+ miles in 50 hours non-stop running, barefoot in many cases, unknown trails, no water stations or medical facilities waiting and no anticipation of any medals at the end. Why do they do this and most importantly how ? Christopher McDougall is one of my favorite author. His best seller, Born to Run demonstrates why this hidden tribe, super athletes are the best in the world. It just reiterated my love to run. Now, Why do we hit walls or bonking doing a simple 26.2 mile marathon run in a well defined controlled environment. Walls in a marathon run is the most painful moment which typically observed around mile 20 depending upon various factors including BMI, Oxygen intake, energy burn rate from our storage cells and most importantly, our anticipation of the pain. Unlike any other species in known history, humans have the most complex body-mind conflict. Body built for performance but mind built for efficiency. The wall here is “how do I know when I will hit this pain point, what should I do differently to overcome it”. There is lot of learning from this to our day to day challenges and opportunities as well. In my case, even with so many full marathon completions, have struggled to overcome this wall and it leaves a painful memory after the completion of the run. I had this unique mind-set to sign up for marathons and just run, with very minimal planning or practice but was able to get through no matter what!

As a preparation for my first Major World marathon recently, the 2019 November NY Marathon, I took a step back, went deeper into understanding my body complex, strategize my practice sessions and defined a solid execution plan for the real run. Every practice run included very detail on what to eat every day, what not to eat, tracking intake of calories during runs, the pace mapped to it and calculations of the burn rate and importantly, focus on the real outcome and managing the body-mind conflicts. Above all, just like Tarahumara tribes, created lots of unknown trials, hill runs, bare-foot runs and these jolts to body-mind brought different perspective! Nov3rd 2019 NY Marathon was the best run of my life, enjoyed every moment of it with 50 thousand runners, toughest of all Major marathons in the world, 5 Bridge crossings including toughest Queensboro bridge, 20 mile passed by and no sign of any fatigue or “Wall just disappeared”, met my well defined outcomes. Now, post that run, few weeks later did a 28 mile practice run easily and feels like I can hit a 50 miler before I hit 50!

As Christopher beautifully summarizes “We are Born to Run! You don’t stop running because you are old, you are old because you stopped running”…I am aiming to be a 6 Star Finisher, to be among 6400 runners in world by 2022!

Now, another instance of hitting a wall and what we learn from it. Imagine a 4 foot metal stick which has 71mm width head to hit a 1.68inch diameter weighing 63 grams ball so that it lands say 120 yards closer to or inside a 4.25 inch diameter hole, in open outdoor environment with all kinds of obstacles surrounding and possible wind, moisture and sandy bunkers. This is game of Golf, which is my favorite sport of life and as Bobby Jones said “Golf is a game played on a 5 inch course — the distance between your ears.” A game which is a great teacher, a leadership development program and teaches the art of laser focus execution maintaining core values of respect, courtesy, integrity, authenticity and most importantly, overcoming failure to succeed. One name many attest to this game of golf is Tiger Woods. One of the instance where Tiger demonstrated leadership to overcome his challenge during the game was at 1999 Phoenix open. Analyzed the situation of where his ball was lying, discussed the Rule 13.2 of Golf, took help from the fans, got them move the boulder which was blocking his shot and ended up making a birdie on the hole! We all know his return to the glory after all the challenges he has been through.

Jordan Spieth, my favorite young player, Master’s Champion at age 21, Open Champion at age 23 and unbeatable for his tenacity, focus and execution. 2017 British Open is one of the masterpiece of leadership from Jordan and his Caddie on hole 13th, 5 hole come back to win the championship. This is edged in my memory forever — you have challenges, analyze the situation, collect all data points, discuss with your team, debate, define the path forward, take risk, commit and execute! Today, same Jordon is going through challenging phase of his career, struggling to make the cut and sometimes it feels, he lost the touch of golf. He has hit a wall, he feels the pain, being the elite and suddenly a drought for W. How is he planning to overcome this wall ? As Spieth reminds us, always make adjustments to your game, practice it, trust it and when you do this consistently, it will all come back stronger! The guru of the game Jack Nicklaus says “He’s going to be just fine; a competitor is a competitor and a winner is a winner and he’s both”. I am sure Jordan will break through the wall soon, will celebrate the day it happens!

What is the “ wall” in our digital era ? We are incumbent, we own the market, we know our customers better than anyone, we thrive.. Oh but wait, we need to change dramatically now to adapt to new industry and customer needs, that’s transformational journey and the barriers to disrupt are way too many! This in essence is the challenge facing many industries and especially, Networking Software and Hardware industry. Current generation customer mindset shift is fueled by disruptive innovators who are proving anything is possible! SpaceX is one of my favorite, which clearly proven anything is possible, paved a path to un-imaginable frontiers. With such disruptions in complex world of space technology, it’s natural for our customers to expect us to innovate much faster! Future prediction is simple, any industry can be disrupted, displaced and potentially could disappear.

It’s that time of the year, where industry leaders in various domain assemble to explore the future of Mobile networking industry — MWC Barcelona. It brings back some of the memorable time we had 5 years ago! One of the industry trends in 2015 was building wireless broadband services like Wi-Fi Hotspots, reduce subscription churn to incumbent cable broadband services via Wi-Fi technology and drive stickiness. We embarked on an journey not just being equipment supplier but business outcome, value driven solution provider. This was a disruptive approach to give best user experience, reduce the customers opex spend and enable them to innovate on top of our platform. Our goal was to enable our customers to “Chase Money to Make Money” and at the same time “ Save Money to Make money”. Mobility iQ solution delivered in 2015 as a true Software as a Service model. It provided actionable valuable network and business insights to our customers about their customer behavior as well as network behavior. Offering this pay as you grow model to our customers, in-turn enabling us to drive recurring revenue, was the most rewarding and memorable experience. The barriers to breakthrough were many — our customer readiness and acceptance of cloud based delivery model, sales force transitioning to value based selling from traditional perpetual device sales, services engine trying to explore what additional values they can add, Finance and operations team figuring out software supply chain constructs and ensure we are profitable, pricing strategy focussed around value and less on features, governance on data privacy, scalable platform and architecture which should be ready to be re-written as the need be, and finally, passionate agile nimble teams to embark on disruptive journey.

With laser focus purpose driven execution, had the opportunity to experience successful launch of this solution at 2015 MWC Barcelona and ensured it can scale to the most dense and hostile radio frequency environment possible as we provided the value to the 100K+ visitors ! The ups and downs of the journey, taught few things — focus on end user experience and outcomes, value before feature delivery, equip your sales engine and partners and most importantly, build a culture for innovative change and learn from your failures! In golf terminology — it is mindset shift to clearly articulate the expectations, take risks to attack the flag and be the change!

As we move forward into the next decade, CxO’s are challenged to keep up with the demands of business needs, technology transforming very quickly, and above all, they have to continue to generate new revenue streams still running the floor efficiently and securely. Especially in the field of Networking Software industry, large Enterprise customers need to evolve quickly to newer architecture and equipment suppliers have to deliver value based solutions rather than traditional products. Unfortunately, the walls are very high not only to the suppliers of new technology but also customers ready to consume it. Of many trends in this space, a common theme is around “how can we transform the complex network, managed by large IT staff, to a model where network becomes enabler of business applications driven by end customer needs”. This is where Software Defined Network solutions enables, with a purely “Controller-led” paradigm to drive operational simplicity, agility to business priorities and at the same time, flexibility to consume services on-prem or from cloud, software as a service model.

We have been transforming to deliver this architecture by empowering our customers through Intent Based Networking journey. The mindset shift for the teams is around delivering a “Solution” which is outcome driven leveraging the best of individual “Product” capabilities. Dramatic transitions becomes essential as one has to be in the shoes of the customer to really understand the problem statements and ensure “value” of the solution delivered before anything else. Each individual team member has to become responsible to the outcome and march cohesively towards a common goal. The tool kit that has helped me to overcome these transformational barriers is Design Thinking methodology. Next, emphasize and rallying the teams around User experience led delivery model with constant closure of feedback loops with field tests. Focus on high value customer use cases rather than “next big thing” in the market splashes and instrument to generate valuable insights of the usage of the solution. One of innovative solutions in this domain that has tremendously enabled customers is turning up their networks with least amount of time and money —Network Plug and Play solutions. The concept is simple to consume and most importantly drives tangible business outcome to customers. Expanding that further into a full Software Defined Network Architecture solution for full Orchestration, Management and Analytics with Security. Its utmost important that every individual within the team and external teams “drink their wine — they use the solution built” on a day to day basis. This builds deeper commitments, passion and most importantly, opportunity to be in customer shoes. The focus always has to shift in driving outcomes rather than getting excited on complex networking platforms and infrastructures, which are critical but its means to end. And above all, enabling sales engines creatively, for customer insight driven sales model via Solution Telemetry rather just traditional blind quota driven approaches. These shifts in the industry is becoming natural and decades ahead of us would drive monumental changes! Exciting transformations happening across the industry in this domain and will come back to share more on this from my experience!

In conclusion, each day we encounter unpredictable life journey and many times every wall seems high. Applying valuable learnings from various sports, work or from home, I am sure we can all overcome the barriers and explore opportunities to disrupt, innovate and contribute!

Mahatma Gandhi said it all “Find Purpose. The Means will Follow”

The question I want to leave with is — What is your experience to overcome unique obstacles and what were the learnings from it ?

Human endeavors on this planet changed forever on 20th July 1969. We set our small steps on Moon opening up giant possibilities for mankind. Last 50 years of disruptive innovation across every walks of life continues to challenge us to explore something new. Personally, I have been watching some of the recent amazing accomplishments in the area of space and astronomy — my favorite Event Horizon giving us first picture of Black Hole, first detection of Gravitational waves by LIGO or SpaceX’s drone ship Of Course I Still Love You catches the reusable rocket boosters! These were just few that excites me and I am sure it has kept all of us wondering what’s next?

With mega computers in our hands today that are a million times faster than what Neil Armstrong and others used during their Apollo missions, this new generation user expectations of outcomes from technology is far beyond imagination. This new breed of us, or our customers or our community will have to adapt to ever changing world enabled primarily by a digital revolution. It’s quite clear — we all want something new and better.

But the current generation’s population who have not experienced the past, demands a different experience focusing on outcomes and simplicity.

How many organizations have embraced this revolution thinking outside the box and nurturing the end-customers with disruptive innovation along with simplicity. We need to take a step back and ask the same fundamental question — how? How did they ask themselves a questions like — what is a car anyway? Why isn’t it a computer on wheels controlled by an app? Or what can be done to delight my customer? There could be many factors enabling companies to transform. If we dig deeper into those companies that stand out, changed our lives via disruptive innovation, I feel it could be attributed to adoption of Design Thinking principles! Apple, Tesla, Uber, AirBNB and many more who have embraced Design Thinking development and delivery. This is gradually becoming a norm and it will be very exciting to see what future holds when we see this in every company’s DNA.

The short time I spent at Stanford D-School, really enlightened the power of this. What is Design Thinking anyways? The simplest way to articulate is — a methodology that focuses on people as the source of inspiration of ideas, problem solving and innovation, it’s not just technology or business alone. How does this enable transformation of companies would be the follow up question? Aren’t the leaders of companies know what their customer wants, how to build it and sell it? Well, this is where the differentiation comes into play and Design Thinking drives a mind-set change which is the hardest. Skill set could always be upgraded but mind-set change is an uphill task.

The core tenant of Design Thinking is “Human Centered or User Centered”. What this means is the focus is to understand or empathize the needs of end user. Be in their shoes, uncover the problem statements that are not clearly said or expressed, ideate with them, and maintain this continues loop until the solutions transforms the user to whole new level of experience that had not envisioned themselves. This process seems straightforward and it is, but as I said, its mind-set changes that individuals or organization have to go through to embrace it fully. There are plenty resources out there which have articulated the value this methodology brings!

By adopting this methodology, personally one thing that stands out for me — “It immediately forces each and every individual in the organization embrace and align to the common “Why” or “Purpose” and have the mind-set to passionately contribute to the success. As this brings each one in defining “the Why”, enables each one to share their ideas, prototype the solution, collect feedback, refine “the Why” and be part of the journey to transform the user. This generation of users demands the previous generation to change because the newer generations needs have to viewed in a newer lens! Design Thinking principles has enabled to explore this.

In conclusion, it has become evident that many companies are not able to keep up with the new digital innovators of this day and age. This new world of digital transformation demands new ways of working and transformational mind.

The question I want to leave with is — How are you or your organization transforming to be disruptive innovators?

Does your team understand why it’s important to know “The Why” now than ever before ?