Lessons from Mumbai Tech Week 2025

Welcome back to Consortium.

I recently attended Mumbai Tech Week 2025. For the uninitiated, MTW is a collective of tech founders, startup enthusiasts, investors, business professionals, and students coming together to listen and learn from the industry’s best and brightest on the future of technology and its impact on society, the economy, and the world.

This year’s event was centred around the impact of artificial intelligence (more on this from Consortium soon!) in various industries ranging from gaming and entertainment to telecom. Outside of this, key debates that are currently at the centre of the AI revolution were discussed by both technologists who are leading the change as well investors who are fuelling it. To drop a few brands here, I managed to listen to Pratyush Kumar from Sarvam AI, Hemant Mohapatra from Lightspeed Venture Partners, Harshjit Sethi from Peak XV, and Sanjay Nath from Blume Ventures.

Here are some of the notes I picked up from the event on where things are headed.

Happy reading!

On India’s AI Investment Landscape

A significant investment gap exists between India and other countries. For example:

• The U.S has already invested approximately $300B in artificial intelligence

• Apple alone has recently announced a staggering $500B investment in AI technology

• Lightspeed has led Anthropic’s latest round

• China has established an $8.2B government investment fund dedicated to AI

• Alibaba has earmarked $53 billion specifically for AI initiatives

India is at a meek $1.42B in total AI investments. Why is this the case?

On Research Foundation & Talent Development

A significant gap in investment is driven by the large difference in India’s AI ecosystem talent compared to the U.S:

• The United States breeds a culture of innovation with its universities sprouting research centres where talent emerges from

• Major U.S. companies maintain several research groups that drive innovation

• India lacks a similar research culture across educational institutions and corporations

• Leading tech companies in India have historically prioritised the return of profits to shareholders rather than reinvesting capital into fundamental research

As a side bar to the above point, from the Ken’s article on India’s future in the context of China and AI:

The thing that prevents India from building a Deepseek is how research is viewed, and consequently practised here. Socially and professionally, research projects are neither prioritised, funded, nor lauded. Deepseek’s success is considerable, but it’s probably one of the dozens and dozens of AI research projects that are going on in China. India has chosen to go into use-cases because researchers constantly face the question—“but what’s the point of doing this? What’s the application?”—all the time. So it’s much easier, and tempting professionally, to choose to build something that has some outcome and application than to spend time building something that may not go anywhere.

• This research gap fundamentally impacts India’s ability to build bleeding-edge foundational AI models

Despite these challenges, India has a lot of opportunity as visits from major AI leaders to the country and connections between Indian talent and global AI development communities are increasing. The emphasis has to be on stronger linkages between Indian universities and industry to foster AI innovation.

On Foundational Models vs. Applications

A central debate we keep hearing is whether India should focus on developing its own foundational models or concentrate on just the application layer. Here’s what the current thinking is like:

• Majority of Indian companies would like to focus on the application side of things — very much in line with what we see with regard to the attitude towards the economics of doing solid research in India

• Although some foundational model work might be done in India, it would form a minority of the AI activity in the country

• For data sensitive applications surrounding work with the government, defense, banking, healthcare, the need for sovereign Indian foundational models is real

• The market is large enough to accommodate various models and applications can be built across the stack

However, regardless of whether India builds its own models or not, applications will forever be model-agnostic. This means that whichever foundational model that met the use case the best would be used irrespective of whether the model was foreign or Indian. An exception to this would be if applications surrounding national security and other data sensitive sectors were being built. This use case would require model sovereignty.

On Data Privacy, Provenance, and Regulation

There is a complex balance between rapid AI development and data privacy concerns:

• Data provenance¹ is essentially the kryptonite to current AI development given how data for AI models are currently scrappy, cheap, messy and untraceable for the most part. Provenance would kill current models if large companies were required to practice this as a standard

• Indian regulations require certain data to be stored in data centres that are present on Indian soil

• There are two approaches to solving the data privacy challenge:

  • Technological solutions (edge computing, on-device processes that doesn’t require sending data to the cloud)

  • Regulatory frameworks that dictate what data foundation models can train on

• However, rather than calling for specific regulations, there is a need for single, stable frameworks that aren’t dynamic across states or leadership transitions

On The 10-Year Investment Thesis

Identifying a single AI investment thesis to commit to for the next decade is difficult but the following are some ideas that investors are bullish on:

• Services Enhanced by AI

  • India has a rich history of being a service based industry (over 50% of the GDP comes from here) and thus transforming these services gives India’s right to win. This would require:

    • Taking existing knowledge about how service delivery works and reimagining these services with AI capabilities

    • Creating new service delivery models that are faster, cheaper, and more effective

    • Combining human expertise with AI to create full-stack solutions for customers

• AI as a New Platform Shift

  • When major platform shifts (such as the shift to the internet or the shift to mobile) take place, the largest outcomes and biggest companies emerge

  • New opportunities are thus created which leads to the creation of new companies

  • Mobile enabled the rise of Uber, Instagram and Airbnb. AI will definitely lead to entirely new business models

  • Global consumer ventures that originate from India have not been common however AI has the capability to facilitate a new wave

• Expanding Market Size

  • Initial SaaS companies sold workflow optimisation, now AI can help these companies sell work itself

  • This combines software spend and labour spend into a larger TAM

  • Companies that adapt to this shift could become significantly larger than previous SaaS leaders

On The Future of SaaS in an AI World

How will software companies fare in an AI-dominated future?:

• Three Tiers of Vulnerability

  • Process/storage products can be easily replaced by AI capabilities

  • Middle-tier SaaS companies must find a deeper differentiation to survive

  • Infrastructure companies like Snowflake might be more insulated from disruption

• Adaptation vs. Disruption

  • Multiple SaaS companies are rapidly building AI capabilities into their products rather than resisting the technological transition

  • “Wrapper” businesses or those that merely add a layer of AI to their existing products, may not thrive for long as opposed to those that are fundamentally driven on AI technology

  • Thus, the opportunity lies in fundamentally re-architecting products around AI capabilities

• Self-Service and Visualisation

  • Tasks previously outsourced to SaaS vendors might be brought in-house using AI tools leading to more integrated business models

  • For every $1 spent on SaaS, approximately 1.8x is spent on implementing the SaaS solution

  • Multiple SaaS tools exist in the market and due to this, several are adopted but only 60-70% of them are utilised despite the significant investment in them

  • Artificial intelligence could reduce both implementation costs and friction in software development

On India’s Brain Drain Challenge

India has historically faced a challenge of talent migration to Silicon Valley and other tech hubs, here’s some of the reasons why:

• Economic Realities

  • The U.S. economy is 8-10x larger than India’s which naturally leads to attracting founders seeking the largest possible markets for their products and services

  • Foreign markets lead to higher compensation which acts as a natural pull factor for top AI talent

  • As the largest possible markets are in countries like the U.S, many founders prefer to be closer to their customers and to the use cases their products can serve

• Models for Talent Retention

  • “TCS/Infosys model” where HQ is in India but significant portion of the business comes from abroad

  • “Freshworks model” where the founder and deal team are based abroad but large development centres are in India

  • “Globally distributed model” where companies take a hybrid route and are not strictly “Indian” or “American” in nature

• Creating a Stronger Ecosystem

  • Corporate partners that offer better compensation to compete with international opportunities

  • Government initiatives that support AI research and development

  • Robust partnerships between universities, corporations, and startups

  • Recognition that talent flow should be bidirectional for learning and growth

  • Creating an environment where founders can be globally competitive while based in the home market

On The Spectrum of AI Investments

• Left Side: Infrastructure Layer

  • Computational centres, power infrastructure, chip companies, and foundational model developers

  • Requires significant capital investment but offers a clear win-lose situation

  • Getting increasingly commoditised despite the higher barriers to entry

  • Some venture funds (eg. Lightspeed) have investments in power companies across the U.S and Europe, chip companies globally, and foundational model developers

• Middle: Application Layer

  • Middleware companies, horizontal applications, and vertical industry-specific solutions

  • Represents the most significant portion of venture activity in the AI space, Lightspeed alone has made 75 AI investments in the past year

  • Crowded space and thus very high uncertainty on who will achieve breakout success

  • Requires a careful evaluation of team quality and technological differentiation

• Right Side: Services Layer

  • Dominated by large consulting firms eg. PwC has generated ~$1.5B from AI consulting alone

  • High revenue potential but not venture investable

  • Difficult for startups to compete with large global consulting firms such as Accenture

On What Investors Look For in AI Founders

Venture capitalists when investing in new companies are always looking at the strength of the team, here is what they look for when evaluating new AI companies:

• Agency and Initiative

  • Founders who demonstrate “agency” are sought after — those that take initiative with modern tools — Cursor, Replit, Loveable, Claude Code

  • In line with this, founders who rely less on theorising and more on using the tools above

  • Teams that rapidly evolve and adapt to the dynamic AI landscape

• Younger Teams

  • Founders that are Gen Z have the advantage of being “AI-first natives”

  • The advantage here is that these founders are first time learners and do not need to unlearn previous approaches or transition from prior industry practices

  • This allows for fresh thinking about the various applications of AI technologies

• Customer Understanding

  • Deep understanding of how AI will change customer workflows

  • Crystal clear insight into why customers would adopt their solution rather than others that are present in the market

  • Ability to articulate how their product transforms customer experiences

• Distinctive Insights

  • Novel perspectives on the market, customers, and the problem statement

  • Teams that exploit insights that others will not touch or have not recognised just yet

  • Compelling forecasts on where the market is potentially heading and why and how their product takes the market there

• Team Quality and Adaptability

  • Ability to rapidly adapt as markets and technology evolves

  • Capacity to attract other talented individuals to join their mission

On Investment Strategies and Challenges

Different strategies are required for different segments of the AI stack:

• High Uncertainty in the Application Layer

  • Numerous companies are pursuing similar opportunities

  • Thus it is difficult to predict which companies will achieve breakout success

  • If there are players that claim to be winners in this space, they are “fooling themselves”

• Selective Approach — similar to the 5Ts framework

  

  • Wait to see which companies demonstrate real traction before investing large amounts of capital

  • Making early bets based on founder quality and technological differentiation

  • Focus primarily on identifying the best founders and the best technologies

• Volume Strategy - numerous investments across the AI landscape as picking winners is more than challenging in the current environment

Conclusions

There is a cautious optimism in the air about India’s future in AI given the significant opportunities and their accompanying challenges that lie ahead. The best AI companies are those that achieve rapid revenue growth, faster than previous technology waves.

While India may not lead in foundation models or capital investment, a strategic focus on AI applications, services, and specific sovereign use cases could position the country as a dominant player in the global AI landscape.