Each use case has tens of funded companies. Each is churning out features rapidly, getting to parity faster than customers can imagine. Each has early traction and a worthy claim to win.
What will it take to eventually win the game?
1) Will it be about surviving the multiple shakeouts that each vertical/ use case will eventually see? Letting capital-bloated companies implode and letting the “tourists” give up…
2) Will continued product obsession be the key? Essentially refining the product beyond where others give up…
3) Will choosing non-obvious wedges/ ICPs be the way to differentiate & survive? Serve markets that others are choosing to ignore/ finding unviable to serve…
The technology is still so early, and we clearly have a few decades of upside left. Yet, there is a gold rush going on right now, which I am sure will push people to optimize for the short term.
In that case, will founders who are truly playing the long game ultimately win? Or is it more important to “surf the wave” in the present?
The former will look unattractive in current times and hence, will be undervalued and “contrarian”. The latter will appear to be imminent winners, yet could flame out.
Reporting back a few thoughts running through my head in real-time:
1. Essentially, the capabilities & design of every core SaaS use case are being reimagined by AI founders as we speak. In a future steady state, I see many of them living inside larger product suites as “features”, either via the incumbent fast-following and shipping them, or via small acquisitions/acqui-hires.
2. Consumer AI products remind me a lot of the 1st gen iPhone apps. Founders (developers) rapidly shipping entertaining, almost “toy-like” use cases. Like in mobile, will something massive eventually come out of these? So hard to tell…
3. An underlying capability of AI that a majority of products seem to be leveraging is “contextual artifact creation”. Eg. creating videos & decks in real time, replacing specific elements instantly in pre-existing media etc.
4. While the underlying “intelligence” capabilities of the products seem to be next-level, the UI/ UX as of now seems quite incremental relative to the mobile/cloud era. Lots more discovery & risk-taking needs to happen here.
5. Across enterprise & consumer/prosumer, it’s clear that these products can only manifest their power when they have access to extremely differentiated & diverse sources of data. In some contexts, it was unclear how a startup would get access to many such datasets in a fresh & relevant manner.
6. In legacy industries like govt/ public sector, AI-native products, even with game-changing capabilities, will still need to deal with age-old GTM challenges (long sales cycle, who will buy, what are the incentives for users to adopt etc).
7. Finally, it’s still pretty effin’ hard to pull off a glitch-free, low-latency AI demo.
Congrats to all the presenting SPC founders. Can’t wait for how these products shape up going forward!
Driven by an ambitious talent pool, geopolitical tailwinds, operating model innovation & domestic risk capital, Indian vertical AI startups could be the breakout tech story of the decade.
A potential scenario running in my head on how 🇮🇳 startups get a significant share in global AI over the next decade:
AI “infra” winners get built in 🇺🇸 (OpenAI, Alphabet etc.) ➡️
AI “platform” winners too, emerge in 🇺🇸 (Salesforce & HubSpot equivalents; a bunch get built/ led by the Indian diaspora) ➡️
As 1st-gen “Application” winners emerge in 🇺🇸, 🇮🇳 startups fast-follow in specific enterprise verticals & grab market share.
The time lag to fast-follow is significantly lower than, say, what Zoho did to Salesforce, or Freshdesk did to Zendesk.
This time, they play an asymmetric game. Instead of only competing with US startups on their home turf, Indian enterprise AI startups also look to dominate the Global South (SEA, MENA, LatAm, etc.).
Moving beyond binary operating models of India or US-based, Indian enterprise AI startups innovate & develop new, globally fungible, cross-geo operating models, similar to Infosys in the 90s, BPOs/ KPOs in the 2000s, and Chennai SaaS in the 2010s.
Compared to the SaaS wave, Indian enterprise AI startups get 10-100x more market share in each vertical, driven by a more ambitious & courageous founder pool, a talent base with skillsets & knowledge from previous tech waves, democratized knowledge & tools access courtesy of AI, as well as more availability of domestic risk capital at each stage.
Rather than IPO or M&A in the US, verticalized Indian enterprise AI startups either go public domestically, or get domestic Private Equity & conglomerates on the cap table who help them scale way beyond the last gen of software companies.
All these games play out on top of a favorable geo-political alignment between India & rest of the democratic world, driven by a China counter-balance narrative.
Verticalized Indian enterprise AI startups could be the contrarian venture bet of this decade!
During my recent India trip, a question I got asked repeatedly by both founders & investors was, “What are you seeing as the main differences between the AI ecosystem in the Valley vs India?”.
I currently see 2 main differences:
1/ Exposure (& therefore, Ambition)
AI founders in the Valley seem to have significantly more direct exposure to the work happening at the frontier. And not just in terms of the foundational technology, but also what battles the incumbents are taking on, how workflows are being iterated on, what lean, full-stack startup teams are doing to be able to generate significant product velocity & revenue, and how customers are thinking & allocating resources.
Essentially, they have the advantage of directly drinking from the Bay Area fountain of knowledge & information, spread primarily via networks.
A direct consequence of more exposure is that it uplevels the ambition of Valley AI founders and organically pushes them to raise the bar for execution within the company. Thus leading to sharper thinking, more courageous bets, and faster execution that all put together, improves the odds of a large outcome.
2/ Story-telling
I see that while AI founders in both the Valley and India are picking very similar problem statements to work on, the storytelling around the same use cases in the Valley is significantly superior.
I guess one reason is that operating directly in the target market (vs being a few degrees of freedom away from it) makes it much easier to get higher-quality early validation signals, making the story much more believable.
Also, AI founders in the Valley tend to emerge from the leading-edge companies of the last mobile/ cloud/ SaaS cycles. So they have a much better intuitive understanding of how to position & message the company in the early days to customers, investors & key hires.
Story-telling becomes even more important as how the AI landscape will evolve in specific market segments & verticals remains highly fuzzy.
So, what can India-based AI founders do to bridge these 2 gaps? Here are a few actionable things:
1/ Do extended sprints in the Bay Area regularly to drink from the same fountain.
2/ Surround yourself with Bay Area-based operators, angels & advisors (even remote is ok to begin with) who can regularly feed this knowledge & intel and, more importantly, help uplevel your thinking & ambition.
3/ Follow a conscious 0-to-1 strategy of only building for US design partners, so your product is held to the same bar as those from Valley startups.
4/ Specific suggestion for VCs – mine your network of LPs, Advisors & Portcos to hold regular AI knowledge sharing sessions with leaders of marquee AI-native companies that are building on the frontier in the Bay Area.
From my vantage point as a US-India venture investor, sharing what I observed in 2024 and my expectations from 2025.
As a venture investor in the US-India corridor via Operators Studio, I saw 2024 as the year of taking stock, of heads-down building for founders, and quiet contemplation for investors.
A. 2024 Recap
1/ AI(Enterprise)– after the unveiling of ChatGPT on Nov 30, 2022, and the peaking of the AI mania in 2023, 2024 saw a bit of dust settling down in the ecosystem. In the Bay Area, I heard more intellectually honest conversations amongst founders and investors, with folks going deeper into discussing operating details and how to best leverage this tech step function beyond the “AI is going to change everything” hyperbole.
(a) Focus on the Applications layer
Along similar lines, I saw US-India founders go into deep build mode in AI. Most appeared to focus on the Applications layer, which aligns well with their core strengths. Working closely with portfolio companies like Confido Health as well as interacting with several seed-stage US-India founders, it has been particularly heartening to see them doubling down on spending time with customers, while also ramping up on the latest developments in AI. They are actively leveraging new models and tools to quickly ship new features. A lot of early US-India SaaS vibes!
(b) Indian VC skepticism
In private conversations with many large VCs in 2024, I sensed a fair amount of skepticism on whether the current generation of Indian AI companies will be able to compete with global players. As a result, many of them are choosing to be extremely selective in terms of the number of deals, waiting, watching, and observing how things are playing out in the US, while occasionally backing de-risked repeat founders in one-off large deals.
A few are also experimenting with a multiple-bets approach, writing several small checks (up to $1Mn size) into high-potential teams and seeing how they execute. Tailored seed programs have been created to do this eg. Peak’s Surge, Accel’s Atoms, Chiratae’s Sonic etc.
2/ India-to-the-world deep tech
The domestic deep tech market opportunity clearly became mainstream in 2024, with a spectrum of 1st generation companies now well-established, ranging from public companies like ideaForge in drone manufacturing to growth stage space-tech startups like Agnikul, Pixxel, and GalaxEye.
Given these outcomes, almost all major Indian VCs now have a deep tech thesis, which bodes well for the next generation of founders in the domain.
(a) Rise of the 2nd-gen
In 2024, I saw the 2nd generation of deep tech founders like Sharang Shakti (anti-drone defense systems), Astrophel Aerospace (space tech) and Naxatra Labs (EV motors) emerge on the scene. They are piggybacking on the learnings and playbooks of their 1st-gen predecessors to move faster and think bigger.
(b) Global commercial traction
In parallel, I saw early green shoots of Indian deep tech startups starting to go global commercially in a more meaningful way in 2024. The biggest eye-opener for me in this regard was attending Speciale Invest’s Annual Summit in Nov’2024 and getting updates on their portfolio going global.
For instance, Ultraviolette has officially launched its EV Superbike ‘F77 MACH2’ for the European markets. Uravu Labs is starting to get some major international orders for its recycled water technology. Cynlr recently inaugrated its Robotics Design & Research Center in Switzerland. PS: for those interested in a few hours of deep-dive into the India deep tech ecosystem, the full-day recording of Speciale Summit’24 sessions is available here.
I saw similar signs of rapidly growing global traction in the Operators Studio portfolio too in 2024. Flytbase has now emerged as a clear global category leader in autonomous drone software, with major enterprise drone-dock installations across 16 countries. Cradlewise is one of the fastest-growing smart cribs in the US, and giving incumbents like Snoo a run for their money. Playto Labs has created a sharp niche of STEM learning using robotics kits and live instructors, with more than half of its revenue coming from outside India.
3/ Venture Capital
(a) No Enterprise exits
2024 continued to be a fairly tight year for VC financings in the US-India corridor. It feels like the VC ecosystem is still undergoing some sort of recalibration after the 2020/21 mayhem. While VCs saw some great IPOs at least on the consumer side, exits on the enterprise side were almost non-existent.
As a US-India venture investor, I primarily play in 2 areas – (1) AI/ Enterprise Software and (2) India-to-the-world deep tech. Exits in these areas are typically expected via M&A. With Indian acquirers being sparse, and the US M&A environment at a standstill under the previous administration, Indian enterprise exits saw virtually no action in 2024.
While smaller funds like Operators Studio can still generate healthy exits via secondary sales to growth investors, we as an ecosystem still need full company exits via M&A and IPOs to keep the liquidity pipeline flowing end-to-end over the long term.
(b) Limited seed capital
In the US, while the bar for Series As and Bs has moved significantly higher, seed-stage financings continue to see high levels of activity. In fact, most multi-stage firms like A16Z, Sequoia, and Coatue are also writing idea-stage checks into AI as we speak. Essentially, 2024 saw massive crowding at the seed stage in Silicon Valley, and given the bar for follow-ons has increased a lot, graduation rates have dropped significantly. As per Carta – “30.6% of companies that raised a seed round in Q1 2018 made it to Series A within two years. Only 15.4% of Q1 2022 seed startups did so in the same timeframe”.
India’s venture ecosystem behaved a bit differently in 2024. Established Indian VCs appeared to have become fairly risk-averse in the past year, reflecting both their larger Fund sizes (needing to deploy larger checks with more traction) as well as their efforts to triage the excesses of 2020/21. As I wrote in this post a couple of months back:
From what I am seeing in my deal flow over the last few months (and my focus is (1) enterprise software and (2) deep tech), I feel there is almost a dearth of quality, structured & consistent angel/pre-seed/seed capital in India right now.
From what Founders are telling me, almost all major Indian VC firms seem to be holding out & looking for late-seed/pre-Series A levels of traction even to start a real conversation. The proverbial $1Mn+ ARR, 2-3x y-o-y growth…
Anecdotally, it looks like only previously successful repeat founders are mopping up large seed rounds from these firms at the idea/pre-product stage. Pre-seed/seed seems to be significantly tighter for first-time founders.
Genuine question for myself and many India-based enterprise & deep tech founders out there who are fundraising – who are the angels/ seed firms in India that are comfortable in CONSISTENTLY writing checks at the true early stages in enterprise software and deep tech (idea/pre-product/MVP/design partner/some usage stage)? And by consistent, I mean doing 10-12 deals per year.
Essentially, 2024 turned out to be an extremely tricky year for US-India founders to raise seed capital, with rounds taking significant time to come together, investors wanting to see much higher levels of traction, and valuations fairly compressed especially relative to the amount of progress in the business.
Of course, the other side of this coin was that these same factors made the US-India seed ecosystem an attractive pond to fish in for investors in 2024. In fact, looking at both the quality of the teams I evaluated as well as the entry valuations I saw, I believe 2024 will emerge as one of the best vintages of Indian venture capital a few years down the road.
B. 2025 Expectations
As we enter 2025, here are some expectations I have from Global Indian founders. These aren’t predictions; rather, a wishlist of things I would love to see play out, again in the context of my US-India/ India-to-the-world focus:
1/ Thinking bigger
In 2025, I would love to see a “Path to $1Bn ARR” slide in US-India startup pitch decks. As I wrote in this post a month back:
I would like to encourage Indian founders building software companies for the world to think significantly bigger and more aggressive both in terms of how large their business can become and how fast can they get there (y-o-y growth targets).
Why? Because software TAMs and market growth rates are much larger than what our brains can imagine. Look at the growth rates of these public companies:
1. Shopify (Founded in Canada) is growing 21% at $8.2 Billion ARR. 2. Canva (Founded in Australia) is growing 40%+ at $2.4 Billion ARR. 3. Toast is growing 29% at $1.5 Billion ARR. 4. Monday (Founded in Israel) is growing 34% at $940Mn ARR.
I am now encouraging my portfolio founders to think beyond the proverbial “Path to $100Mn ARR” slide and start strategizing a path to hit $1Bn ARR.
It’s time we reset our internal narratives and think bigger and more aggressive as an ecosystem.
2/ Thinking non-incremental
One of my observations is that we as Indian founders at large still have a tendency to go after low-hanging problem statements. As AI gathers momentum, these will be automated away quickly and easily especially by incumbents, making it increasingly difficult for venture-backed startups to differentiate themselves.
It sounds counter-intuitive to the whole Lean Startup movement of the last decade, but I believe that in 2025, it will be easier to build a differentiated startup by going after harder markets and tackling hard-to-build products that need to exist in a future that isn’t fully here yet.
In 2025, I would like to see Global Indian founders build for the world in a category-creation mindset from Day 0, and not be afraid to play the game on hard mode.
3/ Founders physically moving to their target markets ASAP
If you are trying to build a venture-scale AI/ enterprise software/ vertical SaaS startup targeting the US, every year you spend not physically moving here will be a lost opportunity. Within the constraints of capital, immigration regimes, and family reasons, I would strongly recommend that US-India founders expedite their move to the US in 2025.
4/ Accelerating Deeptech exports
I would love to see Indian deep tech startups build on their global momentum and double down on exports in 2025. In particular, I see the Global South as an extremely attractive buyer of Indian technology in areas like space tech, defense, energy, and agriculture.
While the West is a harder nut to crack from a commercial standpoint, it can be leveraged to access growth capital as well as cutting-edge research talent. Soon enough, commercial traction from emerging markets will provide these companies with enough product maturity and credibility to be able to compete in the US and Europe in a meaningful way.
5/ Bounce back of seed VC
We are in the early stages of a massive global AI super-cycle, and there are several categories and pockets where US-India startups are likely to have a strong right-to-win. While remaining diligent in identifying these right markets to go after, keeping a high bar on founder-quality as well, and asking tough questions to them, I would encourage Indian venture investors (including angels, family offices, syndicates, and smaller funds/ Solo GPs) to actively deploy at the seed stage in 2025.
The seed stage is where outlier angel outcomes and fund returners get created and especially at this point in the economic cycle, the risk-reward ratios are extremely strong. By all means, it’s fair to keep the bar high. But the ecosystem needs more courageous risk capital to step up at the earliest stages of building truly innovative companies.
TLDR: for the US-India/ India-to-the-world venture story, while 2024 was the year of taking stock, I expect 2025 to be the year the ecosystem starts coming out of the bottom of the J curve.
As we enter the era of a new generation of critical technologies, from AI and AR/VR to EVs and robotics, the technical and entrepreneurial horsepower of immigrants will be more important than ever for Silicon Valley.
Yesterday, I had the opportunity to attend Entrepreneur First‘s first-ever demo day in San Francisco. Folks in the US and India might not be too familiar with EF – they are one of Europe’s top incubator programs, with a particularly strong presence in London and programs now in Paris, New York, and Bangalore.
Reid Hoffman at the EF Demo Day in SF
EF’s model is interesting – they operate in the -1 to 0 stage, spotting deeply technical founders, mostly in their early-to-mid 20s with many straight out of college, and help them identify and incubate a startup idea that aligns with their core technical skillsets and achievements.
And they are definitely spotting some outlier talent. Within this cohort, I saw everything from a Math Olympiad gold medalist, a Material Science PhD from Cambridge, and a 3rd year PhD dropout in Brain-Computer Interfaces to a Formula 1 aerodynamics engineer, someone who built systems for the US Department of Defense and another who worked on JP Morgan’s first AI systems.
This is what made the demo day super interesting for me. With the advent of AI, Europe is gaining prominence in the global tech scene courtesy of excellent technical universities and research institutions that produce some of the most cutting-edge research talent. A majority of EF cohort companies are in deeptech/ applied sciences and therefore, this demo day in a way, gave a glimpse into the future that leading AI research can potentially bring to life.
My 1 line takeaway from seeing these 32 companies pitch – the future is brighter, and full of “tech magic”, than we can probably imagine right now. Get a load of some of the ideas that are already in early productization:
1/ World’s first AI training processor using photons (directly taking on Nvidia).
2/ Optimizing farming 24×7 with low-cost swarms of Roomba-like robots that live in fields and spray everything from fertilizers to pesticides.
3/ AI platform that does automatic product placement within creator videos (a YouTuber can place everything from a Nike shoe to a Fiji bottle within a video in a matter of minutes).
4/ AI-powered real-time language translation that freelancers in non-English speaking nations can use to work with clients across geos.
5/ Exponentially simplifying going from a 3D render to a detailed pre-manufacturing drawing & design for any production process.
6/ Non-invasive neural links that can help soldiers in a hot zone communicate with each other without talking (telepathy brought to life?).
The raw intellect of these founders, combined with the product progress they appeared to have made in a short period, makes me think that many of these ideas are not that far away from commercialization.
What EF is smartly doing is relocating this entire batch to Silicon Valley, where the founders will live full-time, building product and raising capital. Seeing the ambition level of ideas the cohort is taking on, they definitely need the risk appetite and vision-backing mindset of the Bay Area. Can’t think of any other ecosystem in the world where such technically complex and capital-intensive ideas can be backed by a combination of talent, risk capital, institutional knowledge, and diverse networks.
Which brings me to another thought – how talented immigrants continue to move to the Bay Area to build the future. Imagine such unique outlier talent from places like Europe and India choosing to uproot themselves from their home countries, moving to the Valley, and offering their unique skills & knowledge to companies here. This makes me super-long on the Bay Area and clearly shows that the Silicon Valley immigration flywheel is still as strong as ever.
This macro trend is what also makes me equally excited about India’s emergence as a key supplier of founder talent for the world. And not just to the US, but also to regions like SE Asia, the Middle East, and Australia. I believe the Indian diaspora will make a defining impact on the global knowledge economy over the next 20 years. Combine this with the rise of a new generation of critical technologies (AI, EVs, AR/ VR, robotics, semiconductors, etc.), and this transforms into a generational opportunity that energizes me as a venture investor in the US-India corridor.
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How expectations versus reality has unfolded over the last couple of years—from inflation and recession to Google and crypto—prompting me to share an interesting working theory.
I remember the OG Stanley Druckenmiller telling CNBC he’d be stunned if a recession didn’t happen in 2023. Going against all such predictions and in the face of interest rates being at 23-year highs, the fact that the economy is poised for a soft landing has got me thinking about market expectations and their bearing on how things eventually play out.
I have a working theory on this – based on observation, I believe that when the market has too much of consensus expectations around a negative event, it somehow reduces the probability and/or the intensity of that negative event.
Let’s play out some thought experiments on this…
#1 Inflation
In 2022, the market (including the Fed) had a consensus expectation that inflation would remain sticky and continue to rapidly increase, thus explaining the steepest rate hikes in history.
However, given this widespread expectation, both consumers and businesses likely started to proactively tighten their belts, managing demand, reducing costs, and improving productivity to increase supply. The combined impact of this proactive action perhaps brought down inflation much faster than the Market expected?
#2 Recession
The market expected that given the steepness of rate hikes and how that has played out in the past, a recession was imminent. This widespread expectation likely prompted both individuals and businesses to proactively become more fiscally prudent, improve their productivity, and essentially, start doing more with less. These efforts perhaps contributed to avoiding a recession and creating a soft landing instead?
#3 Google
Post the launch of ChatGPT in Nov’22, the market held a consensus expectation that Google’s search business would be rendered irrelevant in the new chat-based AI paradigm. The stock hit a 52-week low in early’23, with the likes of Brad Gerstner (Altimeter) proclaiming that Google’s monopoly was over.
However, the rise of OpenAI and negative market expectations likely woke up Google from its slumber, forcing the management to focus, play its hands in AI (Bard, investment in Anthropic), and do some tough belt-tightening (unprecedented series of layoffs and org. flattening from a company that has been considered as a safe haven for employment over the last 15 years).
#4 Crypto
With the SBF scam, and the SEC cracking down on Coinbase and Binance, many thought that the US is completely closed for business in Crypto. In fact, I remember this being explicitly mentioned in one of the All-In Podcast episodes. However, less than a year after these events, the US has approved Bitcoin ETFs, bringing crypto to the mainstream as a legit asset class (read my post “Bitcoin ETFs and The Challenges of Digital Gold“). As it turns out, the SEC crackdowns were not to shut down Crypto but to clean it up so that it could come into the mainstream.
Connecting the dots…
It seems like widespread negative expectations have a tendency to catalyze a chain of mitigating actions by various stakeholders. In this era of social media, this happens even faster than expected. Perhaps, the human behavior of “loss aversion” creates a sense of urgency, precipitating tough decisions and better execution.
Modeling future scenarios…
Let’s use this mental model to run a few more thought experiments on things the market has negative expectations on right now.
#1 AI taking away jobs
The market has a widespread narrative that AI will end up taking away most knowledge jobs as we know them. However, workers are already aware of this shift and are working to counter it eg. upskilling themselves, starting side hustles to create financial buffer etc.
In parallel, universities are already realizing that their coursework might be outdated soon. They are frantically working to upgrade content, get more AI practitioners involved, and introduce coursework that requires “building” as a way to learn.
Enterprises too, are keenly aware of how a post-AI world will require a different set of skills and are already starting to re-train and upskill employees. Further, in less than a year of ChatGPT’s launch, govts. across the world are proactively thinking through the socio-economic ramifications of AI, including how to stay nationally competitive as well as re-distribute wealth in an increasingly unequal world.
Contrary to current market expectations, all this could create a positive surprise on productivity and job creation in an AI-driven world.
#2 China
The market has overwhelmingly negative expectations of China, including how Xi is taking the country back to its communist roots, the population is de-growing and it’s getting geo-politically discarded by the West.
However, this multitude of adversities could actually rally the CCP to undertake path-breaking reforms, the general population to become more productive, and the country generally coming together more effectively to come out of this mess. Again, more likelihood of a positive surprise from here on.
#3 San Francisco
The market consensus is that San Francisco is America’s Gotham City, a decaying region that the next generation of talent is unlikely to choose to live in.
However, these dire circumstances are already putting massive pressure on local political leaders, forcing corporate leaders like Marc Benioff to speak up against how the city is being run, large budget deficits bringing administrative incompetence to the fore, and the city’s residents finally deciding to speak up and drive political change. I won’t be surprised that all this drives a positive change in SF faster than anyone expects.
However, this rock-bottom could perhaps force developers and landlords to innovate and re-think what the concept of an “office” should be going forward. And, even force cities to upgrade regulations on how urban buildings can be converted to mixed-use. Irrespective of technology, the human need to connect and collaborate with others, as well as be outdoors to refresh and re-energize, remains the same.
Closing thoughts…
Of course, these are just probabilistic thought experiments. As a disciple of Howard Marks, I am always wary of forecasting. However, this working theory that consensus negative expectations often end up seeing a surprise on the positive, is a useful mental model. It helps in not getting overly carried away with the crowd’s narrative, thinking through the likelihood of various scenarios playing out, and positioning yourself optimally.
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From anti-immigration and de-globalization to tech org restructuring and vertical SaaS headwinds, Tech is staring at a drastically different world going forward.
Was chatting with a VC friend earlier this week where we were discussing the US-India corridor and what the future looks like for cross-border SaaS from India.
During the convo, I ended up saying this – “I can just feel that the ground seems to be shifting in a big way for tech and most people aren’t fully recognizing it”. Btw, I repeated this line to my better half the next day in some other context too.
It just feels like a lot is changing at the same time, both macro and micro, and we as tech workers caught up in the daily grind of keeping the ship afloat in our businesses and personal lives, aren’t fully realizing how big some of these shifts are and how they will massively impact our futures.
Consider this laundry list of things unfolding as I write this (sorted from macro to micro, but in no particular order of importance):
1/ Military conflicts
As the world barely came out of Covid, it’s now faced with multiple global conflicts – Russia-Ukraine, Israel-Hamas, Red Sea, and now Iran has frikkin’ fired missiles at Pakistan (who would have thought?).
In the medium to long term, we are also staring at other potential standoffs like China-Taiwan, China-Japan, India-China, and more fronts in the Middle East.
While they might seem distant, these geopolitical tensions can have an indirect economic impact, especially on inflation and cross-border activity.
This tension has powerful political and economic actors at the center and therefore, can have a second-order yet decisive political impact, especially with the 2024 Presidential elections around the corner.
In times of weak macros, high inflation, and a rising perception of hardship, I expect immigration to be a major election issue this year, particularly in the US.
Candidly, I have been a big beneficiary of massive tailwinds of globalization starting in the early 2000’s. Many of the companies I worked for in India served US customers. The venture firm I worked for had US LPs. I moved to the Bay Area and became a global expansion operator. My startup had a distributed team across 4 countries.
At present, it definitely feels like these globalization tailwinds have weakened considerably. I am reading about Indian founders struggling to get US visas, the EU clamping down on migration, and China falling out of favor in terms of global trade and people movement.
If these tailwinds continue to weaken, this is a massive change in a key assumption that underlies the career plans of many global tech workers, especially those from emerging markets. To get a sense of this, check out this awesome thread on X that shares how Indian Masters students in the US will struggle to find jobs this year.
5/ The decline of China
China has come out in the open as an overtly aggressive competitor to the West. At the same time, Xi is executing a drastic socio-economic reset domestically that has decimated an earlier-vibrant tech sector. Noted economist Ruchir Sharma recently cited how in its peak years, China used to attract ~$100Bn of FDI in a single quarter, and now, its FDI has de-grown in Q3’2023.
I remember being in awe of China’s infra, talent and execution focus while working at Alibaba. That just seems like a dream now. I never imagined that I would read headlines about 21% unemployment and disillusioned youth in an energetic economy like China.
What are the repercussions of this? As Western companies pull out investments from China, this is an opportunity for other emerging markets like India and SEA to capture parts of this supply chain being diversified.
From operating in a near-zero interest rate environment for more than a decade since GFC, the Fed has now executed the steepest interest rate ramp ever.
When the cost of capital is low, an economic party begins. Public stocks appreciate given the denominator effect. People borrow more so housing demand goes up and homeowners feel richer. Companies lever up and aggressively invest in physical infra and talent.
At the same time, investors start searching for higher yields given low risk-free rates, thus boosting illiquid-high-return asset classes like venture capital and private equity.
While this post-GFC ZIRP party was in full swing, Covid took it to a new crescendo courtesy of additional QE and stimulus packages. As everyone in the party reached peak highs, a neighbor (inflation) called the cops (Fed), and the party abruptly ended (interest rates rose from 0.25-0.50% in Mar’22 to 4.75-5.00% in Feb’23).
While the highs of the ZIRP party have been gradually coming off through 2022 and 2023, who knows what the long-term impact of this prolonged loose monetary policy will be? Millennials like me have largely worked and grown up in ZIRP, creating our goals, expectations, and lifestyles according to what we saw. Are we ready to re-configure our lives in this new era of higher interest rates?
7/ Tech org restructuring
The recent Big Tech layoffs in the Bay Area are much more significant than many people imagine. For the last 15 years, this compact region has been used to massive jobs getting created by default, salaries rising on auto-pilot, and major equity upsides being captured by RSUs and options. Forget layoffs, anyone working in the Valley since 2010 has only seen an era of multiple job offers and compensation ramps.
This scenario seems to be changing at a highly disruptive rate. Elon catalyzed it by doing deep RIFs in X, including eliminating entire functions altogether. Across mid and large tech companies, am now seeing orgs getting drastically flatter, classic white-collar functions like product management, ops, program management etc. either getting extremely lean or even going away altogether.
I fear that unless a tech worker can either build (code) and/ or sell, they will struggle to see adequate demand for generic tech ops skillsets. At the minimum, this will reflect in drastically restructured compensation packages.
8/ Rise of AI
I am lucky that as a venture investor, I get to see cutting-edge products before the world has even heard of them. From what I am seeing in terms of AI-powered products, both infra and application layer, I fear that many jobs as we know them will get automated away rapidly.
Individual developers and software dev shops have already started using AI for testing and debugging code. This was a job typically done by entry-level IT services talent in offshore centers like India.
Making creatives for digital ads and other low-complexity design tasks are being automated away rapidly.
Google has been drastically cutting down on its ad sales team, expecting a lot of that work to get automated by AI.
Ever since I entered tech in 2011, I have seen engineers be the kings both in startups and big companies. While outstanding engineers will always be gold, the last decade saw even mediocre engineers with basic skill sets reap massive financial rewards mainly due to the supply-demand imbalance.
As we enter the age of AI agents, I am not sure if this will be the case going forward. PS: for more insights on how the AI landscape is playing out, check out my AI Musings series – #1 How The Odds Are Stacking Up?, #2 OpenAI DevDay and #3 LLMs for Beginners.
9/ Bitcoin becomes legitimate
The biggest news of 2024 already is the SEC green-lighting Bitcoin ETFs (see my post ‘Bitcoin ETFs and The Challenges of Digital Gold‘). From being an edgy piece of technology for innovators in 2013, to being discovered by early adopters like myself in 2017, hitting all-time-highs in 2021, then seeing large-scale frauds like FTX in 2022, the SEC suing Coinbase in 2023, and now, getting recognized by the same SEC as a mainstream asset class – whew, who would have thought?
Again, I don’t think most people realize the significance of this move. Over a decade, pure, grounds-up, community-driven adoption of Bitcoin by common people has created a new asset class, helped it travel from Silicon Valley to Wall Street, and forced the regulator to recognize it.
What does Bitcoin going mainstream say about our current monetary systems? Will it change the balance of power between the wealth hoarders (Boomers) and the wealth aspirers (Millennials and Gen Z)? With cash fading away globally in various respects, is this the dawn of pure Internet money? Are there going to be any other ripple effects of the expected mainstream adoption of Bitcoin going forward?
I feel these are open questions with massive implications for who will hold wealth and power over the coming decades.
10/ Startup and VC shakedown
The last 2 years have been the most turbulent for the startup ecosystem since GFC. Venture financing in the US has been on a major downward slide, from ~$348Bn in 2021, to ~$242Bn in 2022 and then, another estimated 30% drop to ~$171Bn in 2023. Startup shutdowns have hit all-time highs, and given the drastic reset in public market comps, valuations in both early and growth-stage financing have drastically come down.
As recently as Q1 2022, just 5.2% of new fundings on Carta were down rounds. In Q3 2023, that figure was 18.5%, continuing a nine-month stretch in which nearly one out of every five rounds raised by startups resulted in a decreased valuation.
This shakedown is reflected in the VC ecosystem too. A major Boston-based VC firm OpenView with $2.4Bn in AUM abruptly shut down in Dec’23. More recently, hard-tech VC firm Countdown Capital wound down operations, stating the following reason – “funding industrial startups is not inefficient enough to justify our existence, and larger, multi-stage venture firms are best positioned to generate strong returns on the most valuable industrial startups”.
I believe that the 2023-25 vintage of startups will be built with very different philosophies, fundamentals, and capitalization strategies. In parallel, the 2020-21 vintage startups will need drastic re-wiring that in most cases, might just not be possible, leading to large-scale write-offs (read my post: Cheetah in the Rainforest: 2021 Vintage of Venture).
Another related view that I recently posted on X – “access to capital was widely considered a competitive edge but it now looks like a view that should be carried with contextual caveats eg. applicable only in low cost of capital macros and in specific types of startups like those with network effects”.
11/ Vertical SaaS headwinds
Within the venture landscape, I wanted to do a quick double-click on vertical SaaS.
With weak macros and the rise of AI, most point SaaS solutions have seen intense customer headwinds over the last 3 years. Startups selling to other startups have been hit particularly hard (many YC companies fall in this category), given the customers themselves are doing brutal cost-cutting.
Enterprise customers too, have been under pressures of layoffs and reducing general opex, hence creating push-back on the per-seat pricing model. See this prescient thread from David Sacks in late’2022 when SaaS was bottoming out.
Based on anecdotal conversations, am also seeing many customers now focusing on reducing software fragmentation and trying to consolidate tech stacks to bring down costs and complexity. In a sense, this seems to be a move away from buying a portfolio of unbundled SaaS solutions, and towards buying bundled software that addresses multiple use cases from the same vendor. In fact, I feel there is an understated opportunity here for startups with strong PMF to really push up their ACVs by solving multiple use cases for customers.
The biggest question mark is on the future of Covid-boosted products. Hopin, one of the poster children of the era, sold for peanuts to RingCentral. Point SaaS products in productivity, sales enablement, and workflows accelerated in 2020 but with the current customer behavior, it remains to be seen if they are vitamins or painkillers, and whether their differentiation and value to customers is strong enough to justify their independent existence.
Closing thoughts…
It’s probably the January-effect but this week got me organically thinking and connecting the dots on all that is unfolding in the world right now. The venture investor in me is part-excited for all the new opportunities this change is going to bring with it, and part-concerned for how both myself as well as existing portcos need to navigate this massive change.
Having adaptability and a growth mindset is going to be key. I have a strong resolve to be on the right side of this change, and also working to transfer this conviction and learning to the founders I work with.
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By coming out & saying “I want people to know we made them dance” in this clip, Satya Nadella has officially announced the beginning of an AI war with Google, who in turn, has also accepted the challenge by launching its own version of ChatGPT called Bard (unfortunately, the launch was botched, wiping out ~$100Bn from its market cap in a day).
Btw, how awesome was this clip? Just the look in Satya’s eyes & the intent behind the statement fired me up, & I don’t even work at Microsoft.
The first battleground of this war is Search. And it’s expected to see classic “David vs Goliath” type asymmetric warfare (Google has 90%+ market share in Search, as opposed to single digit % for Bing). Goliath has everything to lose while David has relatively fewer resources (existing Search distribution in this case).
So, how would each of them be thinking about war strategy? There are clues in asymmetric military wars that have unfolded historically eg. the US in Vietnam.
David’s view (Microsoft):
David can’t beat Goliath in conventional warfare due to the sheer gap in resources. So, it doesn’t make sense for him to engage Goliath by following standard rules in the open. David’s best bet is to engage with Goliath unconventionally, perhaps playing by a new set of rules ‘cos that’s when existing resources will mean less.
Real-world examples of this include (many of these ideas are covered in Sun Tzu’s Art of War, & can be seen in historical military confrontations):
Attack Goliath when he least expects it.
Target areas where Goliath has more to lose than David (eg. a classic nuclear threat).
Avoid a battleground that Goliath is familiar with. Take the battle to unfamiliar territories.
Prefer guerrilla warfare over all-out confrontation.
Use new modes of warfare wherein there is more parity with Goliath eg. economic warfare, communications warfare, strategic diplomacy etc.
Engage in indirect conflict by leveraging third parties that have some edge over Goliath.
If one closely observes how Microsoft is approaching the AI war in Search, it’s using many of the above elements.
First, under Satya’s leadership, Microsoft made itself stronger as a software conglomerate (Teams winning over Slack, LinkedIn’s massive moat, Azure taking a significant lead over GCP etc.). This has brought it more parity with Google at a group level.
Second, while Microsoft has increasingly become an agile & aggressive war machine, Google’s unthreatened monopoly in Search has eroded both the rate of innovation & sense of urgency from its operating culture. In a way, Microsoft is attacking Google when it is at its weakest culturally, while itself being at its strongest in a decade.
Third, the rise of AI is fast changing the rules of the game and as OpenAI’s ChatGPT has shown, Search is likely to look very different in the future. This change is being organically driven by a technology inflection, making Google’s existing dominant position in Search potentially less meaningful going forward.
Fourth, Search is a battleground where Google has much more to lose than Microsoft – the classic Innovator’s Dilemma. Microsoft can afford to take bolder bets, while Google has to fend it off while also protecting its existing business.
Fifth & final, Microsoft is leveraging a third party (OpenAI) as a main actor in this war. Unencumbered, unpredictable, agile & brave – third parties like OpenAI are hard to figure out & gameplan against by large incumbents, similar to how large military machines often struggle against guerrilla warfare.
So, how can Goliath counter these tactics?
Goliath’s view (Google):
While David’s main aim is to use his “brain” & make the battle as unconventional as possible, it makes sense for Goliath to use his “brawn” & exploit David’s vulnerabilities, in particular the disparity of resources.
Some ways he can do this include:
Attempt to drag the war back to familiar territory.
Open multiple fronts against David so he is forced to spread his resources thin.
Drag the war out for as long as possible, to drain David’s resources.
Cut off any access points that David can use to replenish.
David’s key strength is his morale so think of ways to destroy it.
Focus on de-throning the general & the army will automatically collapse.
So, while Microsoft’s challenge appears stiff, Google can use many strategies to counter it.
First, Google shouldn’t be deterred by the first punch. It can strategically prepare itself for a long drawn-out war & leverage its Search distribution might to outlast the competitor.
Second, it can open up multiple fronts against Microsoft to distract it. Potential areas include Cloud, enterprise workflow (GSuite) etc.
Third, given AI is so early, there isn’t likely to be any first-mover advantage. As we speak, many high-quality teams are already working on OpenAI competitors, providing Google with a valuable opportunity to partner with them & make up for lost ground.
Fourth, one of Microsoft’s major strengths is its leader. Google should be open to making moves in the market that distracts Satya or puts him under pressure.
Fifth & final, Google should use this rare competitive pressure to revitalize its execution culture. Perhaps one of the founders returning to the helm is a possibility? If taken in the right spirit, this is a valuable opportunity for the company to reset itself for the next 2 decades.
These are just game-theory conjectures at this point. Given the resources at the disposal of both companies, this AI war in Search is likely to unfold over several years. We will see many of the above tactics get played out in each scene, which will be tremendous learning for lifelong students of strategy like myself.
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Workomo gives you actionable context on the people who truly matter!
I love the days when I organically encounter a solid use-case for Workomo (“Relationships Intelligence for Power Professionals”). Today, I was having a Whatsapp discussion with a good friend, who is also an ex-founder (I had invested in her last startup). As part of another early stage startup now, she is incubating a new micro-lending product in India, and wanted to check with me whether I could intro her to someone working in the space.
Now, being an active startup ecosystem stakeholder & connector, I really want to help her. Given my Alibaba/ Ant Financial/ Paytm background, finding someone with “lending” experience/ expertise should be fairly easy for me. Except it’s not. Barring 1–2 people who are top-of-mind for me right now, it’s extremely hard for me to know who among the people I already know/ have shared history or context with, will be relevant for a potential warm intro. I tried to do a LinkedIn search with keywords like “lending” and “fintech”, but got crappy results wherein I don’t even know any of the people in the first page search results. PS: I don’t even know why I am being shown “company results for fintech”, which btw, are also beyond crappy.
Actual search results page from my LinkedIn profileActual search results page from my LinkedIn profile
Compare this with how Workomo helped me solve for this pain point. Currently, the product is in early private beta stage, wherein for me personally, I am tracking about ~160 of my top-priority professional relationships. These are ex-colleagues, customers, batch-mates, investors etc. — essentially, people with whom I already have a shared history, context, modicum of trust and double opt-in.
I went into Workomo, clicked on the “Relationships” tab and ran a search with the keywords “fintech” and “lending” (at an MVP stage, these are few of the many manual tags I have been using to curate my relationships). I got 9 and 2 search results respectively, comprising founders, VCs and operators, all of whom I know well-enough to ping and check.
Actual Workomo screenshotsActual Workomo screenshots
This is what Workomo is doing at such an early MVP stage. We are in process of building an AI-powered “context engine” that will ingest hundreds of signals and “auto-tag” your top-priority relationships. Imagine your own, personalized, contextual “LinkedIn Search”, working in the way it should, helping you search & curate a high-quality dataset comprising only of relationships that truly matter to you!
Intrigued? Sign-up to request a private beta invite today. We will be delighted to partner with you as an early adopter, in building Workomo out.
