Tag: Competitive Advantage

Do Indian startups have a right-to-win in robotics?

Do the competitive advantages that Indian startups have enjoyed in software products, services & SaaS, also extend to robotics and other areas of deeptech? I believe they do.

Over the last few months, I have seen a few high-quality, India-based startups going after the industrial robotics segment. Essentially, delivering robotics hardware and/or software solutions for automation on the manufacturing shop floor.

There seems to be an interesting “why now?” for this segment. Manufacturing globally is seeing a shortage of blue-collar workers with specialized skill sets. With the rise of freelance service models like becoming an Uber/ Ola driver or a Doordash/ Swiggy delivery person, fewer people in both developed and developing countries are inclined to put in the hard yards and learn vocational skills like welding.

Source: The biggest threat to the CHIPS Act? The Green Left (UnHerd)
Source: X

So clearly, industrial automation will see massive tailwinds in the next decade. Robotics, in particular, seems to be well-poised to see enormous adoption in this movement. It’s massively benefiting from AI-driven chips & compute investments that the cloud infra players and hyperscalers are doing. This is leading to more computing power being packed in smaller form factors. Vinod Khosla recently said (clip below) – “Robotics will soon have its ChatGPT moment. Robots aren’t programmed anymore, they are learning systems”.

The Western industrial robotics opportunity definitely looks interesting for India-based startups. We have already seen the likes of GreyOrange successfully scaling in the US warehousing robotics market, while Cynlr is targeting the higher-end, more sophisticated robotics segment.

Regular readers of An Operator’s Blog would already be familiar with my obsession with “right-to-win” and having clear differentiation. I have been brainstorming with Indian robotics founders regarding what’s a sustainable right to win for India-based, offshore startups in the global industrial robotics opportunity. Here are some admittedly disconnected ramblings, trying to also draw comparisons with the earlier software services➡products➡SaaS eras:

1/ Availability of Talent

The IT services players of the 90s (Infosys, TCS, Wipro) literally created mini software universities on their office campuses, tapped into a large pool of young Indian engineering graduates, and trained them to become software developers. The later software products and eventually SaaS waves benefited from this ready base of talent created by the services companies.

Is there a similar dynamic at play right now in the hardware engineering talent pool (disciplines like Electrical, Electronics, Mechanical, Industrial, Aerospace)? Am not so sure.

Over the last 10 years, a majority of Indian engineering graduates have, irrespective of their passions or innate strengths, trained themselves to become software developers. The engineering university apparatus too, has molded itself in this direction – software engineering intakes are exponentially larger than electrical, electronics, or mechanical branches.

However, one positive signal of hardware talent is how ISRO, India’s government-owned space agency, has been able to assemble large engineering teams that have gone on to build world-class space tech products on the global stage.

As I see it, there are three potential feeders of latent talent in manufacturing and hardware engineering for Indian startups:

  • Govt-owned research universities and labs (IISc, IIT Chennai, IIT Bombay, TIFR, etc.).
  • Public sector companies in areas like defense (DRDO), energy (BHEL, NTPC, etc.), oil & gas (IOCL, GAIL, etc.), railways, fertilizers, and chemicals.
  • Domestic manufacturers & OEMs in areas like automotive, chemicals, textiles, pharma, and agriculture.

Therefore, given just the sheer numbers of young Indians graduating in non-software verticals from various engineering schools as well as then getting trained by the above feeders, the supply of hardware skill sets might still be enough to serve the needs of industrial robotics startups.

There will surely be gaps in the quality and job readiness of this talent. However, Indian entrepreneurs are known to be ingenious. Similar to the IT services companies of the yesteryears, we might see these new-age hardware startups create their own training programs for electrical, electronics, and hardware-centric software skills.

So overall, I feel reasonably good about the availability of talent to build industrial robotics companies out of India. However, it might still need some push and creative thinking from the ‘hardware Murthy’s and Premji’s’ of the current generation.

2/ Cost of Talent

One of the core pillars of Indian IT services was cost arbitrage. In fact, even the later software products and SaaS companies have continued to benefit from it, reflecting in them pricing their products very aggressively compared to Western competitors and winning deals in the price-sensitive segment (Zoho and Wingify have done this really well over the last 2 decades).

Does this cost-arbitrage advantage apply to hardware engineering startups too? In general, the broad-based cost of talent in India has steadily gone up over the last decade. In fact, on the pure manufacturing front, India is no longer a cheap place to produce stuff and one of the reasons why the likes of Bangladesh have taken share away from India in areas like textiles.

Same with software, where high-quality developer talent in Bangalore is now only marginally cheaper than in the West (adjusted for quality, output, and time zone challenges, it might even be more expensive in certain cases). This is one of the reasons why Eastern Europe and LatAm have taken a share away from India in IT offshoring too.

Using these trends as signals, it’s reasonable to assume that though Indian skilled hardware talent will definitely be cheaper than say the US, the magnitude of arbitrage is not as large as say, in the 90s and early 2000s, especially when adjusted for quality and output.

However, a positive signal on this front is again, the likes of ISRO and IISc that have built world-class products at a fraction of the cost compared to the developed world. As an example, ISRO had a budget of just ~$75Mn for its successful lunar mission Chandrayaan-3, while NASA is on track to spend roughly ~$93Bn on its Artemis moon program through 2025.

There is also a large domestic base of SMB-type OEMs and manufacturers that can provide relatively low-cost early iteration and prototyping capabilities. As the Indian govt. increasingly focuses on developing domestic manufacturing and indigenization of technology, this base will only grow from here.

So overall, I feel reasonably good about Indian startups being able to build industrial robotics products at a relatively lower cost compared to Western counterparts. However, the key will be matching the quality and performance of comparable global products while keeping the operating costs (and therefore, pricing) lower than these competitors.

3/ Superior Customer Support

One of the key reasons Indian software companies are able to compete and win against global competitors is the ability to provide superior customer support while maintaining cheaper price points.

Leveraging large workforces at lower costs, the likes of Freshworks have been able to provide white-glove service to even mid-market customers in the US. Combine this with the willingness to do services and provide customization wrappers on top of their products, and you have a unique offering that Western enterprise customers just love.

I have observed that providing superior customer service & support has now become an integral part of the Indian founders’ DNA and has been socialized a lot within the startup ecosystem playbook by communities like SaaSBoomi.

Therefore, I believe this customer support DNA can translate into a right-to-win for Indian startups even on the robotics and hardware side, where most global customers tend to be large and often legacy manufacturing companies that really value this ability to provide a white-glove service as well as offer customization for their needs.

4/ Differentiated IP

Now this is where the situation gets a little tricky. Traditionally, Indian higher education hasn’t focused on fostering original research and development at scale. That’s one of the reasons why even on the software/ SaaS side, most Indian startup success stories have tended to be “fast-followers”, creating a product with feature parity against US competitors and then beating them via aggressive pricing and superior customer service.

Barring a few islands of excellence like IISc, IIT Chennai (Research Park), and IIT Bombay (SINE), students largely aren’t taught to think originally during high school and university.

However, there is a nuance here. At the risk of generalizing, it’s fair to say that while local Indian talent at large may not be the best original thinkers & IP creators, they are definitely strong engineers, operators, and executors.

Combine this with the raw entrepreneurial hustle that the Indian way of life teaches you, as well as first-principles business acumen that’s part of the grassroots culture in states like Gujarat, Rajasthan, Punjab, and Tamil Nadu, and you potentially get a unique combination of engineering and GTM skills amongst many Indian founders. I call this the “engineering dhandho” persona [inspired by the successful public markets investor Mohnish Pabrai; “dhandho” is the Gujarati word for “business”].

Now, not every robotics & hardware problem statement requires IP creation. Many of them can be solved by engineering creativity and ingenuity, or what I call ‘contextual-innovation’. This approach can actually be used to create new categories in relatively untapped verticals like agriculture, automotive, space, defense, energy, B2B commerce, and even consumer hardware.

We already have a few scaled examples of this contextual-innovation from India-based startups:

  • GreyOrange has become an emerging category leader in the US warehousing robotics market, crossing $100Mn in ARR.
  • ideaForge manufactures drones for defense, and went from idea to IPO over the last 8 years. While China-based DJI corners ~70% of the global drone manufacturing market, ideaForge has still been able to create & dominate the indigenous defense drones category in India.
  • Agnikul (launch vehicles), Pixxel (constellation of satellites), and Skyroot (launch vehicles) are making giant strides in the commercialization of space from India.
  • Ather (electric scooters) and Ultraviolette (electric superbikes) are capitalizing on the global movement towards EVs and rapidly emerging as the Hero’s and Bajaj’s of today’s India.
  • Atomberg (ceiling fans), boAt (headphones), and Ultrahuman (fitness trackers) have created differentiated consumer hardware with specific features for their respective personas, and have also built successful brands despite the presence of large incumbents.

A few contextual-innovation greenshoots from my own portfolio:

  • Flytbase has created the global autonomous drone software category, unlocking massive enterprise use cases. HQ in Pune, 100% of its revenue is global.
  • Playto has developed a proprietary robotics kit that supports 1,000+ builds for kids from Grades 2-8 to learn STEM skills. HQ in Bangalore, a majority of revenue is global.
  • Sharang Shakti is building an anti-drone & airborne threat mitigation system for defense. It will start with India but I expect countries in the Global South to be major markets in the future.
  • Astrophel is building ground-up sub-orbital launch capabilities for commercial space payloads. Astrophel and other Indian space tech companies will cater to global customers, especially those from price-sensitive developing countries.
  • Yulu has designed and built EV micro 2-wheelers from the ground up, keeping specific Indian urban micro-mobility needs and contexts such as hyperlocal deliveries, logistics, and last-mile daily passenger commuting.

Looking at all these examples, I believe Indian startups can utilize the contextual-innovation approach to make a dent in several industrial robotics use cases, especially those that require smartly stitching together hardware + software solutions for legacy enterprise customers.

Closing thoughts…

Summarizing my observations across 4 key axes – Availability of Talent, Cost of Talent, Superior Customer Support, and Differentiated IP, I feel reasonably good about the right-to-win of Indian startups in industrial robotics, and would even extend this conviction to many other verticals of hardware.

I believe many of the advantages that Indian startups have enjoyed in the previous IT services, software products, and SaaS waves, will also extend in some shape or form to the oncoming global deeptech and hardware wave.

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AI Musings #4 – How To Differentiate As An AI Applications Startup?

Discussing two areas startups can focus on to create competitive differentiation in the AI applications layer – (1) data and (2) product flows.

Over the last few weeks, I have met a few exciting startups building in the applications layer of AI. As the landscape stands today, the foundational LLMs layer is likely to be dominated by a mix of open-source, Big Tech, and perhaps 1-2 hyper scalers (eg. Anthropic). The cloud infra (compute, safety, security etc.) to deliver these model capabilities will definitely be served by the Big Tech cloud players.

This leaves 2 categories for startups to exploit against these large competitors – (1) Applications and (2) Dev Tools. On the latter, I don’t understand it deeply enough to have a view on it (yet). However, the Application layer is something I get, and therefore, have some working POVs on it.

Almost all AI application layer startups I am seeing right now are essentially using ChatGPT (+ Bard and Llama in a few cases) to build features that solve sharp use cases in specific verticals. Based on observation, some low-hanging verticals that founders are going after include Insurance, Marketing, Sales, and HRTech with AI-generated content being a horizontal ingredient in most of these products (eg. automated email generation, stitching together a marketing video, crafting a training course outline etc.).

In all these cases, I am still struggling to understand how these startups can create competitive differentiation or moats purely by building features on top of hyper-scaler APIs. To take a step back:

For a new technology inflection to create viable startup opportunities, there need to be sizable areas where new companies are significantly better positioned than incumbents to leverage this new technology and solve unaddressed customer problems.

This is a really important point. For a startup to be viable, it’s not enough to just be an early adopter of cool technology and build new products before anyone else. The startup has to be able to create significant differentiation against entrenched competition too. Eg. Apple beat IBM in the PC inflection, Amazon beat offline retailers in the Internet inflection, Instagram and WhatsApp beat Facebook in the mobile inflection, and Figma beat AdobeXD in the cloud inflection.

This is the aspect where I am pushing all AI application founders I meet to start thinking through and strategizing from Day 0. A couple of ways to potentially drive competitive differentiation have emerged from these working sessions:

1/ Access to data

While ChatGPT is great for bootstrapping specific use cases, eventual product differentiation will emerge from startups fine-tuning their own LLMs (with open-source models as a starting point) using proprietary data sets for industry-specific use cases.

To put it simply, foundational models will keep doing a great job of adding horizontal knowledge. Startups will need to do the work of incorporating deep vertical knowledge into the models.

Here, access to the ‘right’ customer data will be critical. But then, entrenched incumbents would already have access to much more data than a 0-to-1 startup. So, how does a startup create a data advantage?

One way could be to identify unsolved pain points for customers that large pre-AI competitors aren’t going after, either because they are contextually unviable (Innovator’s Dilemma), were unsolvable pre-AI, or due to organizational inertia.

In these cases, AI-native startups can leverage their speed to get to the ‘right’ customer data sets before anyone else, and start creating an edge via custom fine-tuning and benefiting from faster learning cycles.

It’s interesting that the underlying driver of this differentiation is still good-old startup execution, rather than just building AI-first features. The company would still require classic software execution (founder-led sales, figuring out ICP, setting up GTM motions, etc.) to succeed.

2/ New product flows

Another area where startups could do better than the entrenched competition is putting in the work to develop AI-first product flows. We saw this happen in previous tech inflections where new capabilities and form factors gave rise to new ways of doing specific jobs. Eg. Apple cracked the smartphone user experience while Nokia struggled. Or Figma figured out how designers should work and collaborate with other functions in a fully hosted, in-browser experience, while Adobe continued to be stuck in its old UX.

Given the vast range of new capabilities that AI is unlocking (eg. chat-based UX, AI ‘agents’ to deliver specific tasks that underly use cases), it’s reasonable to expect a plethora of new workflows to emerge across customer segments. Many of them will require absolutely fresh product thinking to crack, something that pre-AI product teams at established companies might struggle with.

Similar to the ‘access to data’ point earlier, the underlying driver of this product flows differentiation again will be good-old, startup-style product management – Paul Graham’s “do things that don’t scale”, starting with a wedge of focusing on a very-specific customer persona and pain point, frantically iterating on it, and in the words of Brian Chesky, “Focusing on 100 people that love you, rather than getting a million people to kind of like you”.

Putting things together…

If one looks at both the above areas of potential startup differentiation, the way AI might end up creating viable startup opportunities is not the LLM technology itself, which will become baseline, widely available (like cloud today), and likely open source (similar to programming languages like Java and Python).

Rather, the drivers of value creation by startups will be in:

(1) What’s needed to effectively leverage these LLMs to solve verticalized, deep industry-specific problems – eg. pre-AI, a 10x backend engineer was needed to leverage the cloud. Post AI, specific datasets will be needed to leverage LLMs.

(2) 2nd and 3rd order impact of AI on product experiences and workflows – Figma and Notion took years of fresh thinking and iterations to reimagine collaboration UX in the cloud. AI-first use cases will require similar untethered, ground-up product thinking to deliver these capabilities effectively to customers.

What does this mean for venture investing?

It means even in the post-AI world, investors should continue to look for founding teams that demonstrate many of the classical startup traits, a few of them being – (1) ability to unearth a unique customer insight, (2) product thinking to be able to solve for it, (3) GTM skillset to be able to create a differentiated business out of it, and (4) grit to last through the journey.

Essentially, might be a good idea to avoid AI overthink and keep doing more of the basics of venture capital.

Note: check out the previous post #3 in this AI Musings series – LLMs for Beginners.

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True competitive advantage is a “Mesh”

Today, I was remembering my strategy professor from ISB Prof. Prashant Kale. All my batch-mates will agree that he was probably one of the best, if not the best, professors that year for Class of 2010. In particular, one of his classes where he taught the legendary Southwest Airlines strategy case is imprinted in my consciousness — I even remember exact drawings he created in the class.

The key takeaway from that class was that the competitive advantage of Southwest Airlines wasn’t a single linear element; rather, it was a “Mesh of inter-connected, inter-dependent, self-reinforcing activities” that was almost impossible to replicate by competition. Eg., turning around a plane in 15 mins (fastest in the industry), which required the gate staff to operate at a certain cadence, which in-turn, required the check-in staff to do certain activities at a certain speed etc. Essentially, executing any precedent element well made the next dependent element even stronger; conversely, if one of the elements was poorly executed on, the entire mesh advantage ceased to exist. This Mesh model has been a transformative strategy concept for me, and has been a key foundational element of my thinking.

Illustrating the proverbial “Mesh of Competitive Advantage”

I recently got reminded of this concept again after a decade, this time in an entirely different context of public market investing. I was reading the Q1 2019 Quarterly Investor Letter by O’Shaughnessy Asset Management (OSAM), a top quant asset management firm founded by the legendary public markets investor Jim O’Shaughnessy. This letter is particularly fascinating, as it talks about how to cultivate real edge as an investment firm.

OSAM defines the following framework for real investing edge (quoting the letter):

  1. “Real investing edge should (instead) be cultivated at the organizational level.”
  2. “Properly built, an edge should be very difficult or impossible for others to replicate.”
  3. “Ideally, the edge naturally increases over time — something venture capital investor Keith Rabois calls an “accumulating advantage.”

Specifically, OSAM does 2 things that drive the edge — 1) consciously building a Research Graveyard, which essentially means doing lot of research, data analysis and number crunching projects that don’t necessarily lead to immediately improved investing outcomes but increase the overall ideation & knowledge of the firm in a compounded way over the long term; and 2) building tools (data-sets, software and combos thereof) and then deliberately opening them up publicly for other researchers to use (like the way Amazon opened up its cloud infra to developers, creating AWS), whose usage, in turn, has generated some of the best research insights for OSAM.

As a finance and investing person, while both these elements are individually interesting to me, the real deal was this sentence (again quoting the letter):

“These things — software, data, research partners, our graveyard, even the podcast and our twitter activity — all link into and depend on each other, which makes each more valuable and harder to copy.”

“Think back to the ownership data project. Without other connected tools, that would have just been a dead idea — time wasted. But now the ownership data set has become a critical piece of another software tool we use for clients called Portfolio X-Ray. It is now also a new data set available to research partners, who may find something interesting that we did not.”

This is the “Mesh of Competitive Advantage” all over again. A set of activities that, while look replicable in isolation, are almost impossible to replicate by competition as an inter-dependent, inter-connected, self-reinforcing system. This, my friends, is where true competitive advantage comes from!

This is the same reason why, despite having an incredibly transparent investing strategy, framework, terms, processes & activities, other venture firms are unable to replicate the Y Combinator model. This is the same reason why I saw Alibaba winning in China eCommerce (a rhythmic mesh of commerce, payments, logistics, cloud and advertising that is perhaps, impossible to replicate even with infinite capital). It’s the same reason why, as Prof. Kale told us in 2009, Southwest won in the US airline market.

As I think more about this concept in the context of tech startups & Silicon Valley, I believe the following execution elements are important drivers of on-ground success:

  1. Mesh creation has to be deliberate — it’s really hard to defend individual, linear advantage elements in the long run (eg. just having more capital than competition).
  2. Constituent elements of the Mesh need to flow from an authentic place residing inside founders/ leadership teams — what we call as DNA, else it’s hard to sustain.
  3. The Mesh strength compounds over time — demands consistent execution over a long-enough period of time.
  4. This is why true diversity in the team is important — underlying this Mesh of Competitive Advantage, is really, a Mesh of diverse people, each contributing a uniqueness that, combined as a whole, is a super-power. Like the YC Founders or Paypal Mafia.

Would love to hear how you have created competitive advantage for yourself/ your companies.

Side-note: the “Mesh of Competitive Advantage” can also be used to differentiate yourself as an individual professional. Instead of being linear in your career, try to create your own cross-functional, cross-sector, cross-cultural & cross-market Mesh of skills & experiences that, while individually might not look compelling enough, combine together to give you a truly differentiated world-view and approach to life. In today’s age of automation & tech-driven leverage, having this type of Mesh is worth its weight in gold as it can’t be replicated by software; rather, software & tech tools can be used to leverage it up & further magnify its impact.

Related note for parents raising kids in Silicon Valley: given we live in an echo-chamber, with template approaches to pretty much everything (from hiking at the same spots, wearing similar Patagonia vests, to starting up and listening to the same podcasts), it’s important we consciously expose our kids to the non-Silicon Valley world. We would do well to nurture their authentic qualities and original habits, whether they fit with the Valley way of doing things or not. In fact, I would argue that the more contrarian or differentiated these intrinsic personal qualities are, the more we as parents, should encourage them. This will set them up as adults to create their own, authentic “Mesh of Competitive Advantage” that stands the test of time and disruption.