DEEP TECH VALUATION

Saturday Brainstorming Thought (294) 27/09/2025

BY:- Er. Avinash Kulkarni, 9822011051

Chartered Engineer, Govt Regd Valuer, IBBI Regd Valuer

Deep Tech Valuation is the complex process of assessing the financial worth of companies built on significant scientific and technological innovation, requiring a nuanced approach beyond traditional methods like Discounted Cash Flow (DCF)

Key factors in Deep Tech Valuation

1) Technological Innovation

The novelty and scientific advancement of the core technology

2) Intellectual Property (IP)

Strength and breadth of patents, trademarks and other protections that create a defensible advantage

3) Market Potential

The size of the addressable market and potential for disruptive market impact

4) Team Expertise

The quality and experience of the management team

5) Technology Readiness Levels (TRLs)

A scale from 1 to 9 measure the maturity of a technology, providing a framework for assessing risk

Challenges in Deep Tech Valuation

1) Extended Timelines

Deep Tech companies often have longer development cycles and significant capital requirements compared to other tech ventures

2) High Risk – High Reward

The foundational science is often groundbreaking, making it inherently risky but potentially disruptive and highly valuable if successful

3) Traditional Method Limitations

Standard valuation methods like DCF are less effective for pre-revenue deep tech companies, as they lack predictable cash flows and established market comparables

Deep Tech Valuation Approaches

1) Bercus Method

Assigns value to key qualitative factors like the idea, prototype, management team, strategic partnerships and early sales

2) Scorecard Method

Benchmarks like startup against industry averages on various criteria such as management, market, product and competitive landscape applying weighted adjustments

3) Technology Readiness Levels (TRLs)

Assesses how far along the technology is on the path from concept to a commercially viable product

4) Hybrid Funding Models

Incorporating features like SAFE notes, convertible notes tied to technical milestones or royalty interests to align with the unique milestones of deep tech

5) AI-Enhanced Platforms

Emerging tools that use graph neural networks to analyze global innovation networks and provide valuation insights

6) Blockchain-Based Collaborative Models

Decentralized platforms for experts to provide input and aggregate data for valuations

Investor Considerations for Deep Tech Valuation

1) Industry Expertise

Investors need deep technical and industry knowledge to understand deep tech ventures

2) Risk Assessment

Evaluating the inherent technological and commercial risks associated with the company’s scientific advancements

3) Long-Term Vision

Understanding the long road to commercialization and potential for significant future returns

4) IP Strategy

A strong IP strategy is crucial for long-term success, investor confidence and defensibility

Deep Tech Valuation Overview

1) The Deep Tech market is projected to reach $714.60 billion by year 2031, growing at a CAGR of 48.20%

2) Total funding in Deep Tech companies reached over $100 billion as of July 2025

3) Sectors include AI, quantum computing, biotechnology, advanced materials, robotics and advanced manufacturing

4) The US leads in funding, followed by China and UK

5) Deep Tech IPOs and acquisitions represent 7.00% of all companies in the sector, higher than the general tech exit rate

Key Trends Affecting Deep Tech Valuations

1) AI Integration and Acceleration

A) AI is blurring the lines between traditional and Deep Tech, accelerating development cycles and reducing time-to-market by at least 40% in areas like materials science and biotech

B) AI-enabled Deep Tech companies are commanding higher valuations, often 15-20x revenue, compared to non-AI Deep Tech (6-8x revenue) and even SaaS companies (8-12x revenue)

C) AI-powered simulations reduce technical risks, making investors more confident

D) AI-enabled Deep Tech companies are achieving exists (acquisitions or IPOs) around 30% faster, often within 5 years, compared to traditional tech (7 years) and non-AI Deep Tech (over 10 years)

E) AI-driven startups demonstrate superior capital efficiency, performing about 1.5 times better than non-AI counterparts

2) Emphasis on Technology Readiness Levels (TRLs)

A) TRLs are increasingly integrated into valuation methodologies, providing a standardized framework to assess tha maturity and risk of Deep Tech innovations especially for pre-revenue companies

B) TRL assessment informs criteria like Strength and Protection of Product/Service in methods like Scorecard Method

3) Sustainability Focus

A) Growing urgency to address climate and meet net-zero commitments is driving investment into Deep Tech solutions for sustainability

B) Government policies like the US Inflation Reduction Act and the European Green Deal provide financial and regulatory support for green technologies, creating market opportunities for Deep Tech

C) Examples include technologies for reducing CO2 emissions in concrete or developing sustainable materials

4) Patient and Strategic Capital

A) Recognising Deep Techs longer development cycles (5-10 years compared to 1-3 years for traditional tech), there’s a growing need for patient funding sources beyond traditional venture capital, including government support and specialized funds

B) Government funding initiatives, such as Rs 10,000 Crore funds in India or Rs 1 Lakh Crore R&D corpus for long-term financing are crucial

C) Corporate Venture Capital (CVC) interest is surging, increasing from 15% in 2019 to 35% in 2023, as corporations seek strategic advantages

5) Stringer IP and Global Presence

A) Deep Tech relies heavily on Intellectual Property (IP), averaging 8-12 core patents per startup, significantly higher than traditional tech (0-2 patents)

B) IP strength, defensibility and potential global scalability are becoming key valuation drivers

C) Indian Deep Tech, for instance, is gaining global traction through strategic partnerships and exports, indicating the international markets validation of these innovations

6) Evolving Exit Strategies

A) IPOs are becoming increasingly common for high-growth, AI driven Deep Tech, especially in North-America and Asia

B) Secondary sales of shares are a growing trend, offering early investors liquidity before a full exit

C) Strategic acquisitions by larger corporations are a significant exit pathway, especially for AI chip and advanced materials companies

7) Government Support and Policy

A) Governments are increasingly recognizing Deep Techs importance for national security and economic competitiveness, implementing policies, grants and infrastructure support

B) Examples include India’s National Deep Tech Startup Policy (NDTSP) and funding for semiconductor manufacturing

8) Shift in Talent and Academic Commercialization

A) More founders have strong technical backgrounds, often PhDs with global research experience leading to more IP-first startups

B) Academic institutions are playing a more active role in commercializing research through partnerships and spin-offs

9) Geographic Specialization

A) North America remains a leader, particularly in AI chips and quantum computing, favouring acquisitions

B) Europe shows a balanced approach with strengths in industrial AI and biotech, utilizing both M&A and IPOs

C) Asia is emerging strongly in robotics and advanced materials, with a preference for IPOs

COMPILED BY:-

Er. Avinash Kulkarni
9822011051

Chartered Engineer, Govt Regd Valuer, IBBI Regd Valuer, Rera Certified Consultant, Black Money Act Regd Valuer