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‘A Junior VC’ Secures ₹100 Crore Fund for Pre-Seed Investments in India - Related to will, building, fuel, gemini, vcs

‘A Junior VC’ Secures ₹100 Crore Fund for Pre-Seed Investments in India

‘A Junior VC’ Secures ₹100 Crore Fund for Pre-Seed Investments in India

Investor and former executive at Venture Highway, Aviral Bhatnagar, has successfully closed AJVC’s (A Junior VC) inaugural ₹100 crore fund. AJVC aims to invest in 12-15 startups annually, focusing on emerging technologies such as AI, SaaS, and. Consumer technology.

This fund is specifically designed to target the pre-seed stage in India, a sector Bhatnagar believes remains largely untapped in the country’s startup ecosystem. “I did not expect this kind of response when I started the fund. Beyond hitting our target raise, we have received over 5,500 applications and made nine investments. The response is both overwhelming and humbling,” expressed Bhatnagar.

Started in 2018 by Bhatnagar, AJVC has already invested in nine startups across various sectors. These investments include five AI startups, two D2C brands, one B2B firm, and one consumer tech firm. The fund’s sector-agnostic approach allows it to explore diverse opportunities, aligning with India’s growing tech landscape.

The Indian startup ecosystem is witnessing a surge in micro VCs. Which are filling the early-stage funding gap. The pre-seed funding space is particularly crucial, as it provides essential capital for startups to develop and scale their ideas.

While late-stage AI startups attract funding from large VC firms and global investors. Early-stage capital remains scarce, creating a gap that microVCs are filling. The Indus Valley Annual study 2025 highlights a shift where seed rounds exceeding $3 million now make up half of early-stage funding, while sub-$1 million rounds have sharply declined.

Building on these developments, this trend has made it increasingly difficult for first-time AI founders to raise capital. Paving the way for micro VCs to bridge the funding shortfall. AJVC’s efforts align with a trend where micro VCs are increasingly crucial.

Unlike larger VCs that favour proven founders and market traction, micro VCs take on high-risk. High-reward bets, particularly in deep tech and AI. Their key advantage lies in domain expertise.

Rather than offering broad seed funding, they specialise in AI, SaaS, or deep tech, providing not just capital but also technical mentorship, AI model optimisation. And go-to-market strategies tailored for AI startups.

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Start building with Gemini 2.0 Flash and Flash-Lite

Start building with Gemini 2.0 Flash and Flash-Lite

Since the launch of the Gemini Flash model family, developers are discovering new use cases for this highly efficient family of models. Gemini Flash offers stronger performance over Flash and Pro, plus simplified pricing that makes our 1 million token context window more affordable.

Today, Gemini Flash-Lite is now generally available in the Gemini API for production use in Google AI Studio and. For enterprise clients on Vertex AI. Flash-Lite offers improved performance over Flash across reasoning, multimodal, math and factuality benchmarks. For projects that require long context windows, Flash-Lite is an even more cost-effective solution, with simplified pricing for prompts more than 128K tokens.

Developers are already leveraging the speed, efficiency. And cost-effectiveness of the Flash family to build incredible applications. Here are a few examples:

Building effective conversational AI, particularly voice assistants, requires both speed and accuracy. A fast Time-to-First-Token (TTFT) is essential for creating a natural, responsive feel, alongside the ability to handle complex instructions and. Interact with other systems via function calling.

Daily is leveraging Gemini Flash-Lite to help developers create cutting-edge voice AI experiences. Using their open-source, vendor agnostic Pipecat framework for voice and multimodal conversational agents, Daily has created a system instruction code demo to reliably detect voicemail systems and. Tailor messages accordingly.

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Investor and former executive at Venture Highway, Aviral Bhatnagar, has successfully closed AJVC’s (A Junior VC) inaugural ₹100 crore fund. AJVC aims ...

Will Micro VCs Fuel the Next $1 Billion Indian AI Startup?

Will Micro VCs Fuel the Next $1 Billion Indian AI Startup?

India’s AI landscape is witnessing rapid growth, however, access to early-stage capital remains challenging. While large venture capital (VC) firms and global investors often back late-stage AI startups, early-stage funding is increasingly scarce.

This is precisely where micro VCs step in and fill the funding gap.

The Indus Valley Annual Report 2025 (Blume Report) highlights a clear trend: larger seed rounds or ‘mango seeds’ (>$3 million) now account for half of total early-stage funding, while sub-$1 million rounds have significantly declined.

Moving to another aspect, this shift has made it harder for first-time AI founders to secure capital, opening up opportunities for micro VCs to step in.

“India’s innovation ecosystem depends heavily on micro VCs, particularly as the window for creating AI-first product-tech companies gets smaller,” noted Ranjeet Shetye, venture partner at YourNest Venture Capital, in an .

“MicroVCs can support more early-stage startups by writing smaller checks, which spreads the risk and. Increases the possibility of power-law outcomes, where a few breakthrough successes generate enormous returns,” he stated.

Micro VCs, which are smaller, highly specialised funds that invest in pre-seed and seed-stage AI startups, differ from traditional venture firms in their approach. , over 100 micro VCs in India typically invest between $100,000 and $500,000 at the seed or pre-seed stage.

Unlike larger VCs that prioritise proven founders and market traction, micro VCs focus on high-risk. High-reward opportunities, especially in deep tech and AI.

Another key advantage of micro VCs is their domain expertise. Unlike generic seed funds, micro VCs often specialise in AI, SaaS, or deep tech, providing targeted support beyond capital. This includes technical mentorship, AI model optimisation, and go-to-market strategies tailored for AI startups.

Their ability to invest at valuations of $1 million to $8 million allows them to take early bets on disruptive AI technologies even before mainstream investors take notice.

MicroVCs Driving India’s Next AI Unicorn.

AI-first startups require substantial capital to train and. Refine LLMs. However, capital efficiency remains key to early growth. Shetye emphasises that micro VCs play a critical role in shaping India’s next AI giant.

“Founders can achieve product-market fit, iterate quickly, and drive sales-led growth by supporting AI-first product development and customer success models early on,” Shetye explains.

“Reaching milestones like $1 million ARR faster is enabled by this lean. Capital-efficient strategy, which also speeds up follow-on funding rounds.”.

This model has already shown promise. Startups backed by seed funds typically take longer to raise Series A rounds compared to those funded by multistage VCs.

However, with strategic backing from micro VCs. AI startups can optimise early-stage growth while keeping burn rates low. This increases their chances of securing larger investments down the line, fueling their journey to unicorn status.

Interestingly. India’s push for AI sovereignty has led to increased government support for AI startups. The ₹20 billion ($240 million) AI Sovereignty Fund, presented in the latest Budget, aims to spur homegrown LLM development and AI research.

Additionally, Indian startups now have access to 18,000 GPUs at 40% below market rates. Providing a significant cost advantage in AI training and deployment.

Additionally, this policy shift aligns well with micro VC-backed startups that prioritise lean AI model development. While US and Chinese AI firms raise billions for model training, Indian AI startups are adopting frugal innovation strategies, making breakthroughs with smaller budgets.

Despite their benefits. Micro VCs face challenges in scaling their influence. One of their biggest hurdles is the struggle to raise capital themselves, making it difficult to maintain consistent funding pipelines. Additionally, they operate in a high-risk environment where a large percentage of portfolio companies may not survive past Series A.

Another challenge is the scarcity of specialised AI talent. While India has a strong engineering base, it lacks the deep AI research ecosystem seen in the US and China. Many AI founders prefer to move abroad for superior funding and infrastructure.

To counter this, micro VCs are now collaborating with academic institutions such as IITs and IISc to nurture AI talent at home.

As India aims to build its own DeepSeek-like AI models, with the recent being Soket AI Labs’ Project EKA that is inviting researchers and developers to build sovereign AI models. Micro VCs could play a pivotal role in funding the next generation of AI leaders.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Flash Junior Secures landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • General AI capabilities
  • AI-driven scientific breakthroughs

Expert Perspectives

Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:

"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."

— AI Researcher

"Organizations that develop effective AI governance frameworks will gain competitive advantage."

— Industry Analyst

"The AI talent gap remains a critical barrier to implementation for most enterprises."

— Chief AI Officer

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:

  • Improved generative models
  • specialized AI applications
  • enhanced AI ethics frameworks

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • AI-human collaboration systems
  • multimodal AI platforms
  • democratized AI development

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • General AI capabilities
  • AI-driven scientific breakthroughs
  • new computing paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of ai tech evolution:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Responsible AI driving innovation while minimizing societal disruption

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Incremental adoption with mixed societal impacts and ongoing ethical challenges

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and ethical barriers creating significant implementation challenges

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

large language model intermediate

algorithm

platform intermediate

interface Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

API beginner

platform APIs serve as the connective tissue in modern software architectures, enabling different applications and services to communicate and share data according to defined protocols and data formats.
API concept visualizationHow APIs enable communication between different software systems
Example: Cloud service providers like AWS, Google Cloud, and Azure offer extensive APIs that allow organizations to programmatically provision and manage infrastructure and services.