Hard tech is hard. Funding hard tech is even harder. Andrew Haughian has the answer, and it's not what you think.

Andrew is a Partner at Pangaea Ventures, a hard tech focused VC fund with nearly 30 CVCs as limited partners. He's an engineer turned investor who has spent years sitting at the intersection of corporate innovation and early stage hard tech, working closely with companies like Aspect Biosystems, CarbonCure Technologies, Prime Roots, and others navigating the long, complex road from lab to market.

In this episode, Ravi Belani, CEO and Founder of Alchemist Accelerator, digs into the playbook that separates fundable hard tech companies from glorified research projects, and why CVCs are more critical to hard tech than almost any other sector.

They get into:

• Why hard tech founders absolutely need CVCs, and how to build those relationships the right way

• The real timeline for landing a CVC investment (hint: it's not months, it's years)

• How one founder is closing a CVC-led Series A, and exactly what they did differently

• What POs, LOIs, and MOUs actually signal to investors, and which ones are worth the paper they're written on

• The "option value trap": why customer enthusiasm doesn't always mean skin in the game

• How to go from 1 customer to 10 to 100, and what great founders know about that path that others don't

• Why CVCs keep churning talent, and what the best ones are doing to fix it

• The singles and doubles strategy for new CVCs trying to prove their value internally

This one is for hard tech founders, CVC leaders, and anyone trying to understand how corporate innovation actually works from the inside out.

🔔 Subscribe for more Alchemist Influencer Series episodes 🌐 Learn more at alchemistaccelerator.com

Most VCs will tell you to stay away from Corporate Venture Capital. Angelo Del Priore is here to tell you why they're wrong.

Angelo is the Founding Managing Director of HP Tech Ventures, one of the most active CVCs in the game. He's led and followed investments in AI, edge compute, and the future of work, and he's sat on boards alongside companies that have exited to Google, Oracle, and Unity. Before all of that, he consulted for Apple, Cisco, Dell, Intel, Microsoft, and Sony, so he's seen this world from every angle.

In this episode, Ravi Belani, CEO and Founder of Alchemist Accelerator, pushes back hard, and Angelo pushes back harder.

They get into:

Why CVC money can actually be smarter money than a traditional fund

The no-BS truth about when CVCs follow vs. when they lead

How having HP, Intel, or Samsung on your cap table signals something no pitch deck can

Why CVCs rarely take board seats, and why that's a bigger deal than founders realize

The one mistake founders make that wastes everyone's time (free pilots)

How great founders actually use their CVC investors vs. how most founders ghost them

The pre-seed warning: why bringing in a CVC too early can quietly kill you

This is not a CVC sales pitch. It's a real, honest debate between two people who've been on both sides of the table, and it'll change how you think about building your cap table.

🔔 Subscribe for more Alchemist Influencer Series episodes 🌐 Learn more at alchemistaccelerator.com

Corporate venture capital has evolved.

In this episode of the Influencer Series Fireside Chat, Ravi Belani sits down with Jay Eum, Founding Managing Director of GFT Ventures and one of the few investors who has helped build three venture initiatives, including Samsung Ventures and TransLink Capital.

They explore how CVC has moved from slow, strategically entangled “dumb money” to independent, performance-driven CVC 3.0 models. Jay breaks down what makes corporate venture capital successful today, how reputation shapes access to deal flow, and why some CVCs create real strategic leverage while others struggle.

They also dive into hard tech fundraising:

• When founders should approach CVCs

• Why proof of concept matters before engaging strategics

• How to avoid getting burned in capital-intensive industries

• Why reputation is everything in tight venture ecosystems

• How to think about leverage when you only have a few possible partners

Plus, Jay shares his perspective on today’s AI revenue explosion and what founders should focus on during this unprecedented market cycle.

If you’re a hard tech founder, enterprise startup, or emerging fund manager, this conversation is essential.

Enterprise AI startups don’t fail because the tech is weak. They fail because they sell the wrong way.

In this episode of the Alchemist Influencer Series, Ravi Belani sits down with Gnani Palanikumar, former Head of Product at Apigee (acquired by Google for $725M) and chair of Alchemist’s GenAI track.

Gnani shares a tactical, operator-level playbook for turning AI pilots into real enterprise adoption.

They discuss:

• Why traditional enterprise org structures break AI deployments
  

• The mismatch between 2-year AI cycles and 12-month buying cycles
  

• Why founders should sell at the department level, not enterprise-wide
  

• How to find pre-approved budgets and avoid multi-stakeholder gridlock
  

• The “4-week proof” tactic for voice AI in financial services
  

• Why domain expertise and fractional operators matter
  

• How to identify whether a company is AI-native or stuck in SaaS thinking
  

• Why AI projects must deliver value within a quarter
  

Gnani’s 5-word takeaway says it all:

Lead with trust. Deliver proof.

If you’re building enterprise AI, this episode is pure tactical insight.

Agentic AI is everywhere right now, but most teams are still struggling to price it, deploy it, and make it work in production.

In this Fireside Chat, Ravi Belani sits down with Vibhor Rastogi, who leads AI investing at Citi Ventures, to unpack what’s actually happening inside enterprise AI adoption and what founders often get wrong.

They dive into why pricing agentic software is still an unsolved problem, what’s genuinely overhyped in the “agent” wave, and how startups can build defensible AI businesses in a world of reskinned workflows and runaway costs.

Topics covered include:

• Why seat-based, consumption-based, and outcome-based pricing all break in different ways
  

• How CIOs think about predictability, guardrails, and runaway AI costs
  

• What investors really want to see from agentic AI startups
  

• Why many “agents” today are just rebranded APIs and workflows
  

• The importance of memory, learning, and real autonomy in agentic systems
  

• How founders can solve the data cold-start problem through design partnerships
  

• Why an “Agentic Ops” layer may be the missing piece for enterprise adoption
  

If you’re building, investing in, or deploying agentic AI, this episode will help you separate signal from noise.

Enterprise AI isn’t failing because of the models.It’s failing because of data.

In this episode of the Alchemist Accelerator Influencer Series, Ravi Belani sits down with Sidney Rabsatt, Chief Product Officer at MindsDB and former AI and infrastructure leader at Google Cloud, AnyScale, and F5 Networks.

Sidney breaks down why connecting AI to enterprise data is far harder than most teams expect and where the real unlocks are hiding. From messy, fragmented data systems to the false promise of endless ETL projects, this conversation explores what it actually takes to get real value from AI inside organizations.

You’ll learn:

• Why AI struggles when enterprise data is fragmented
  

• The hidden cost of traditional ETL and data clean-up
  

• How AI can work directly on messy data without years of prep
  

• Where startups can find real opportunities at the AI + data layer
  

• Why focusing on the use case matters more than the technology
  

If you’re building, deploying, or investing in enterprise AI, this episode will change how you think about data, workflows, and where real value is created.

Manjeet Singh lives where AI hype meets real enterprise reality. He is the Senior Director of Product Management at Salesforce AI and Agentforce, and previously led product teams at Iron Mountain and ServiceNow. He spends his days shipping AI products that millions of people actually use.

In this episode, Manjeet sits down with Ravi Belani to talk about what is really working in enterprise AI adoption, and where a lot of teams are still stuck in POC land.

They explore:

• The real state of enterprise AI and agentic adoption in 2025

• Why so many AI projects stall at proof of concept

• Where agents are already driving real KPIs, from software development to customer support

• How to tell if an AI opportunity is likely to deliver real ROI for an enterprise

• Why constrained, well defined workflows win first

• Vertical vs horizontal AI, and how to build more than “just an LLM wrapper”

• Why regulated and high cost of failure domains can be great markets for startups

• The missing “toolchain” for agents, from testing and evals to observability and tracing

• How to think about moats, domain expertise, and speed in the AI platform era

• Why Manjeet is excited about the intersection of AI, healthcare, and preventative fitness

If you are building, buying, or investing in enterprise AI, this conversation will give you a grounded view of what works, what does not, and where the next opportunities are likely to appear.

Marcelo Calbucci has been building tech for over 30 years. He’s a serial entrepreneur, multi patent holder, former engineering leader at Microsoft and Amazon, and someone known for having bold, contrarian takes on where AI is actually heading.

In this episode, Marcelo sits down with Ravi Belani to challenge some of the biggest assumptions people have about AI right now.

They dig into:

• Why layoffs and job freezes might have nothing to do with AI

• Why AI isn’t replacing software engineers anytime soon

• Where AI is being massively overused

• The areas where AI is actually underused and full of opportunity

• Why founders need to stop outsourcing strategy to LLMs

• How to use AI to disconfirm your ideas instead of chasing confirmation bias

• Why the next big wave of opportunities may come from “multiplayer AI”

• What roles AI is likely to replace, and which roles will remain essential

• Why adaptability will matter more than anything else in the next decade

Marcelo also shares which startup opportunities he’d pursue today, how he thinks teams will collaborate with AI agents in the future, and the one piece of advice he’d give founders in 6 words: We’re entering the world of adaptability.

If you want fresh, honest, and thought provoking perspectives on AI and the future of work, this conversation is a must watch.

Amr Awadallah has seen multiple waves of hype in tech and helped build real companies through it. He co founded Cloudera, took it public, watched it get acquired for 5.3 billion dollars, and is now building Vectara, a platform that helps enterprises ship AI apps that are accurate, safe and actually usable in production.

In this episode, Amr joins Alchemist Accelerator founder and CEO Ravi Belani to unpack a striking finding from an MIT report. An estimated 95 percent of enterprise AI projects are failing.

They dig into why that is happening and what to do differently:

• The real reasons enterprise AI fails in production
  

• Why “go do AI” from the board is not a strategy
  

• How fake exploration budgets create fake success stories
  

• The 3 whys every founder and buyer should test
  

• Why do anything

• Why now

• Why this vendor
  

• How to tell a real business need from AI theater
  

• Why passion for the problem, not the tech, drives grit
  

• How cofounders, early hires and investors make or break your company
  

• What Vectara is building as an “operating system” for AI agents
  

• Why context, security, accuracy and cost control matter more than demos
  

Amr also shares raw, founder level advice. From firing fast in the first 50 hires, to choosing investors who have actually been in the trenches, to his simple test for whether you really love the problem you are working on.

If you care about building AI products that survive past the pilot stage, this conversation is a must listen.

Dr. Shelby Heinecke, Senior AI Researcher at Salesforce, joins Ravi Belani to explain why the future of AI will not belong only to giant models with hundreds of billions of parameters.

Shelby makes the case for small language models: compact systems with only a few billion parameters that can run faster, cost less, protect privacy, and still perform at a very high level when they are trained well on focused tasks.

In this episode, they dig into:

• Why small models are a different tool, not a weaker version of large models
  

• How fine tuned small models can beat much larger models on specific agentic tasks
  

• Where small models shine most: privacy, speed, cost to serve and on device use cases
  

• How Salesforce built “Tiny Giant,” a 1B parameter model that outperforms much larger models on selected tasks
  

• What really matters in training: data quality, workflows and trajectory style datasets
  

• How synthetic data, noise and guardrails help make models more robust in the real world
  

• Why founders should look closely at on device AI and domain specific small models
  

Shelby also shares practical advice for founders who want to build in the small model space, and closes with a simple takeaway: do not underestimate small models.

If you care about AI agents, privacy, edge computing or future startup opportunities, this conversation will give you a lot to think about.