All-In Podcast Highlights: David Friedberg's Best Advice for Deeptech Founders Raising Capital

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David Friedberg shows up on the All-In Podcast with hard-won wisdom, and deeptech founders always ask the same question: how do I use that advice to actually raise capital.

In this article, I break down what I’ve learned from David Friedberg’s take on deeptech fundraising and translate it into practical, step-by-step guidance you can use right now.

You’ll find specific tactics on milestones, capital planning, risk framing, data rooms, and term choices that resonate with venture capitalists who understand science-heavy startups.

I’ll also link to relevant investor insights from Capitaly.vc so you can go deeper on process and execution.

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1) What David Friedberg really means by “make science a business”

I hear founders repeat “make science a business” and miss the operational punchline.

It means your experiment has to map to a customer, a price, and a purchase order.

It means you schedule lab work like a factory and backchain experiments to cash flow.

Here’s how I encode it when I pitch:

• Every experiment has a commercial metric. Yield, cost per unit, cycle time, or reliability tied to a price point.
• Every quarter has a customer proof. A pilot, a design win, or a letter of intent with a price and volume range.
• Every raise funds a business milestone. Not just a paper, but a step that moves the product toward recurring revenue.

When investors hear this, they stop thinking "science project" and start thinking "company."

For a structured way to turn R&D into a revenue roadmap, see our blog post: Translating Research Into a Funding-Ready Plan.

2) The All-In Podcast segment every deeptech founder should replay

When David Friedberg talks about capital efficiency and milestone risk reduction, he’s handing you a fundraising script.

I replay those segments before drafting my update emails and pitch narrative.

I focus on three things:

• Risk burndown per dollar. Show how each tranche reduces a key risk cheaply.
• Learning velocity. Highlight how fast your team converts experiments into decisions.
• Commercial touchpoints. Anchor tech claims to customer behaviors, not only lab metrics.

When you speak in risk, velocity, and customer language, partner meetings go from academic to actionable.

For more on shaping investor updates that mirror this cadence, see our blog post: Investor Update Templates That Drive Action.

3) Deeptech milestones vs. software milestones

Software founders talk active users and MRR.

Deeptech founders talk yield, reliability, and throughput that unlock revenue capacity.

I present milestones as a chain of constraints, not a wishlist.

• Constraint A: Achieve 99.9% device yield at wafer scale.
• Constraint B: Hit $X cost per unit at pilot line scale.
• Constraint C: Validate lifetime to Y cycles in field conditions.

Each constraint has the experiment plan, expected date, and a customer proof tied to it.

That’s how you replace vanity milestones with revenue-enabling ones.

For a deeper dive on milestone design, see our blog post: Milestone-Based Fundraising for Deeptech.

4) Translating TRL to investor-ready milestones

Technology Readiness Levels are a great internal shorthand but an incomplete VC language.

I translate TRL to investor milestones like this:

• TRL 3→4: Prototype validates physics with reproducibility.
• TRL 4→5: System integration at bench scale with known failure modes.
• TRL 5→6: Pilot conditions with pre-specified acceptance criteria and customer witness.
• TRL 6→7: Field validation with operating budget line item from a customer.

I then tie each TRL jump to cash needs, time, and risk reduction.

Investors can now underwrite the journey with confidence.

For planning pilots and acceptance criteria, see our blog post: Designing Pilots That Convert to Contracts.

5) Crafting a credible path to productization

Productization is where science meets supply chain.

I show a one-page path that answers four questions:

• What is the bill of materials and who supplies it.
• What process steps are proprietary vs. outsourced.
• What quality system and standards apply.
• What lead times, yields, and costs look like at each scale stage.

Then I tell a story of a real customer who will accept product at Pilot, at Pre-A, and at Scale.

This makes productization feel mechanical, not mystical.

For supply chain readiness templates, see our blog post: Building a Deeptech Supply Chain From Day One.

6) TAM when the market doesn’t exist yet

Friedberg often pushes to ground TAM in current spend that your tech can realistically displace.

I use this three-layer TAM stack:

• Today’s spend: The actual budget line you replace, with sources and current vendors.
• Unlock TAM: New demand enabled by cost/performance shifts, supported by analog markets.
• Frontier TAM: Strategic scenarios that open once you hit scale or regulatory milestones.

I avoid top-down fantasies and map each layer to milestones and pricing.

That’s how you keep TAM credible without underselling the upside.

For modeling bottoms-up TAM, see our blog post: How to Build a Bottoms-Up Market Model.

7) Capital planning that matches physics and factory timelines

Hardware schedules lie when cash buffers are thin.

I make a capital map that includes engineering slip, supplier slip, and qualification retests.

• Plan A: Nominal schedule with buffer for long-lead items.
• Plan B: Supplier slip by 8–12 weeks and alternative vendor cost impact.
• Plan C: Retest cycles after failure analysis with spare parts and lab time reserved.

I raise for Plan A plus half of Plan B to avoid panic bridge rounds.

That makes me look conservative in the right ways.

For building a capital stack that resists slippage, see our blog post: Capital Stack Strategy for Deeptech.

8) Choosing between venture, strategics, and government funding

Not all capital is equal when your company touches atoms.

I use a simple fit matrix:

• Venture capital: Fast decisions, pro-rata support, milestone pressure, dilution high but speed high.
• Corporate venture: Strategic fit, potential channel access, pacing slower, possible preference risk.
• Government/non-dilutive: Great for early R&D and validation, admin load high, timeline long.

I combine sources but avoid becoming grant-dependent or tied to one strategic.

Diversified funding preserves strategic flexibility.

For a step-by-step on non-dilutive capital, see our blog post: Non-Dilutive Funding Playbook for Deeptech.

9) How to pitch risk like a pro

David Friedberg responds to founders who are explicit about risk.

So I enumerate risk by category and show the experiment that kills each risk cheaply.

• Technical risk: Physics works, materials degrade, or integration fails.
• Scale risk: Yield drops with volume, capex requirements, or supply limits.
• Market risk: Switching costs, regulatory friction, or pricing power.
• Execution risk: Hiring, qualification, and line uptime.

Then I show my risk burndown plan by quarter with clear success criteria.

Investors fund clarity and discipline.

For turning risk into a financing narrative, see our blog post: How to Frame Risk in Investor Meetings.

10) Data room essentials for deeptech diligence

Generalist VCs skim pitch decks.

Deeptech VCs dig into data rooms.

My data room has five clean folders:

• Tech Package: Experiment logs, statistical analysis, failure modes, and IP status.
• Pilot Evidence: Signed LOIs, pilot scopes, acceptance criteria, and results.
• Manufacturing: Process flow, yield curves, supplier quotes, and QC plans.
• Regulatory: Standards matrix, test plans, and agency correspondence.
• Finance & Capital Plan: Use of funds, per-milestone burn, and downside contingencies.

I also add short explainer memos, so no doc stands alone without context.

For a ready-to-use checklist, see our blog post: The Ultimate Startup Data Room Checklist.

11) Unit economics before you have units

Investors still ask about unit economics even when you’re pre-revenue.

I present proxy economics with explicit assumptions and sensitivity ranges.

• Cost stack: Materials, labor, overhead, depreciation, and yield-adjusted scrap.
• Price corridor: Competitive parity, value-based premium, and expected volume discounts.
• Margin path: Now, at Pilot, and at Scale with learning-rate curves.

I show what breaks margin and the experiments that fix it.

That shows I can manage a factory P&L, not just a lab.

For building cost curves that stand up in diligence, see our blog post: Modeling Unit Economics for Hardtech.

12) LOIs, pilots, and design wins that actually count

Not all LOIs are created equal.

I separate signal from noise with three rules:

• Money talks: Paid pilots beat unpaid trials.
• Usage metrics: Define success criteria before work starts.
• Conversion plan: Include target price, volume band, and conversion date in the document.

One design win with a clear conversion path beats ten vague LOIs.

It’s not paperwork.

It’s pipeline.

For templates and examples, see our blog post: From Pilot to Purchase Order.

13) The team slide: scientists, operators, and the industrial athlete

Friedberg backs founders who convert science to industrial reality.

I look for what I call the industrial athlete and make it obvious on the team slide.

• Scientist-operator pairs: One drives discovery, one drives throughput and quality.
• Supplier whisperer: Someone who knows how to buy, qualify, and dual-source parts.
• Program manager: Someone who turns chaos into schedules with buffers and gates.

Titles matter less than the ability to hit a yield target by a date.

That’s what investors are underwriting.

For hiring scorecards tailored to deeptech, see our blog post: Building the First 10 Hires in Hardtech.

14) Board construction and governance for long-cycle companies

Deeptech needs a board that can navigate manufacturing setbacks and regulatory nuance.

I aim for a compact, skilled board with clear operating rhythms.

• Composition: Lead VC, independent with domain expertise, and founder.
• Cadence: Monthly operating reviews, quarterly strategy offsites.
• Clarity: Pre-agreed go/no-go criteria for major capex and product changes.

This keeps governance from becoming reactive.

It turns the board into an operating asset.

For practical governance frameworks, see our blog post: Setting Up an Effective Startup Board.

15) Milestones-based financing and tranched rounds

I’ve found that tranched capital works when both sides agree on objective gates.

I propose tranches tied to tests with binary outcomes, not hand-wavy progress.

• Gate 1: Achieve X% yield at Y throughput with third-party validation.
• Gate 2: Close Z paid pilot with acceptance criteria signed.
• Gate 3: Regulatory pre-submission accepted or certification passed.

If you de-risk cleanly, tranches release without drama.

If you miss, you know why and what changes.

For negotiation tactics on tranches, see our blog post: How to Negotiate Milestone Tranches.

16) SAFE vs. priced round for deeptech

Founders ask me which structure David Friedberg would prefer for deeptech.

Here’s how I decide:

• Pre-seed/Seed: SAFE with valuation cap and MFN when speed matters and price discovery is weak.
• Post-pilot/A-round: Priced round when you can defend a valuation with objective milestones and want clean governance.
• Bridge: Convertible with discount plus cap when timing to the next proof is short.

Deeptech often benefits from a priced round once you have pilot proof, because you’ll need a board that can help steer capex and scale.

For deal structure nuances, see our blog post: SAFE, Convertible, or Priced Round?.

17) Narrative arcs that resonate with All-In–style investors

Investors who listen to the All-In Podcast want a narrative that is ambitious but operationally grounded.

I keep my arc simple:

• World shift: The physics breakthrough that changes a unit cost or performance limit.
• Proof path: Three steps from lab to factory with cash and time boxes.
• Demand signal: The customers already leaning in and the use cases they will pay for.
• Capital use: Exactly how dollars turn into de-risked assets and revenue capacity.

Then I end with the inevitability statement: “If we hit these proofs, this market tilts to us.”

That’s the moment partners lean forward.

For narrative coaching worksheets, see our blog post: Crafting a Fundable Narrative.

18) What to ask from a lead investor and how to spot a tourist

I ask prospective leads three questions to see if they’re real partners or tourists.

• Who on your team has qualified a factory or passed this certification before.
• What reserves and pro-rata policy will support us through three experiments, not one.
• Which customers or strategics will you intro within 30 days of closing.

A tourist wobbles on answers or over-indexes on optics.

A real lead cites names, dollars, and dates.

For more on picking a lead and building a syndicate, see our blog post: How to Choose a Lead Investor.

19) Common deeptech fundraising mistakes I see

I keep a running list of avoidable errors.

• Science-first decks with no commercial line-of-sight.
• Hand-wavy TAM with no budget owner identified.
• Unpriced LOIs that don’t mention conversion.
• Data rooms missing failure analysis and lessons learned.
• Optimistic timelines with no supplier buffers.

Fix these and your odds go up immediately.

Most investors pass for avoidable reasons.

For a pre-pitch checklist, see our blog post: The Pre-Meeting Fundraising Checklist.

20) The quiet superpower: non-dilutive funding that doesn’t slow you down

Non-dilutive capital is a gift when you use it to de-risk specific milestones without bloating the org.

I target programs that map to my next proof and avoid grants that drag me into scope creep.

• SBIR/STTR: Great for prototype validation and early customer discovery.
• ARPA-E/DOE: Strong fit for energy and industrial breakthroughs with scale potential.
• DARPA: Useful when dual-use and extreme performance drive your advantage.

I staff lean, plan for reporting, and keep venture timelines intact.

That keeps my cap table clean and my momentum high.

For a curated list of programs and timelines, see our blog post: Best Non-Dilutive Sources for Deeptech in 2025.

Investor meeting script you can steal

Here’s the short script I use that aligns with David Friedberg’s investor preferences.

• Opener: “We’ve converted a physics breakthrough into a product with a clear path to $X gross margin at scale.”
• Proof: “We’ve hit Y yield at Z throughput and met Customer A’s acceptance criteria in a paid pilot.”
• Risk burndown: “The remaining technical risk is lifetime under high duty cycle, which we will resolve with two 6-week test loops and spare capacity reserved.”
• Capital ask: “We’re raising $M to fund three milestones that unlock $N in booked revenue and a priced A round in Qx.”
• Close: “If we hit these proofs, we become the cost leader, and the market tilts to our platform.”

This keeps the conversation about de-risked value creation, not speculative upside.

How I align experiment design to commercial goals

I write experiments like mini-business cases.

Each experiment asks and answers a commercial question.

• Question: What lifetime do we need for a three-year service contract at $Y/month.
• Design: Accelerated stress test to equivalence.
• Decision: If we hit it, Pilot B converts to a 200-unit order at $Z price.

This turns lab work into a revenue machine and makes fundraising far easier.

How I negotiate LOIs that actually convert

I approach LOIs like term sheets for future revenue.

I insist on five elements:

• Commercial owner: Name, title, and budget.
• Success criteria: Quantified performance and timing.
• Target price and volume: With ranges and discount schedule.
• Conversion trigger: The condition under which PO is issued.
• Exclusivity scope: Limited in time or scope so I’m not boxed in.

Strong LOIs reduce investor doubt and shorten diligence.

Capitaly.vc’s fast path to a fundable deeptech story

When I help founders tighten their fundraising process, I use a fast but rigorous sequence.

• Week 1: Narrative, milestones, and capital map.
• Week 2: Data room with pilot evidence and unit economics proxies.
• Week 3: Target list, intro strategy, and meeting schedule.
• Week 4: Objection handling and term calibration.

This compresses time-to-term-sheet without cutting corners.

For more on running a tight process, see our blog post: Running a 30-Day Fundraise.

Building credibility with investor updates

Friedberg values operators who learn fast and communicate clearly.

I send monthly updates with the same structure every time.

• What we planned.
• What happened.
• What we learned.
• What we’re changing.

I include one chart that matters and one ask that’s specific.

Momentum is the message.

For update templates that get replies, see our blog post: Monthly Investor Update Template.

Customer development in deeptech without burning cycles

I focus on the budget owner and the operational champion.

Both must say yes or the deal dies in procurement.

• Budget owner: VP or GM who cares about cost, uptime, or regulatory exposure.
• Operational champion: Engineer or scientist who runs the test and signs off on metrics.

I pre-wire both roles before pilots start to avoid surprises at conversion.

That saves months.

For scripts and emails that open doors, see our blog post: How to Land Your First 5 Deeptech Pilots.

Defensibility that speaks to venture capital

Patents help, but process moats and scale moats often matter more.

I articulate defensibility in layers.

• IP layer: Patents, trade secrets, and know-how.
• Process layer: Proprietary manufacturing steps and QC algorithms.
• Scale layer: Contracts, capex, and yield learning curves.

Venture investors fund moats they can see compounding, not just legal filings.

For a defensibility audit checklist, see our blog post: Building a Moat in Deeptech.

FAQs: David Friedberg, All-In Podcast, and deeptech fundraising

Q1: What are the top three things David Friedberg looks for in deeptech pitches.

Clear risk burndown plan, credible path to productization, and real customer signal.

Q2: How much should I raise at seed for a deeptech startup.

Raise enough to hit two objective proofs and one commercial conversion, typically 12–18 months of runway plus buffers.

Q3: Are SAFEs okay for deeptech.

Yes at pre-seed/seed for speed, but consider a priced round once pilots validate economics and you need structured governance.

Q4: How do I keep strategics from slowing me down.

Seek commercial agreements with limited exclusivity, set timelines, and optionality on future collaboration.

Q5: What belongs in a deeptech data room that software companies often skip.

Failure analysis, yield curves, supplier quotes, and regulatory planning documents.

Q6: How do I talk TAM if my market is new.

Tie to existing spend you displace, show unlock TAM with analogs, and map both to milestones and pricing.

Q7: What’s a good sign an investor is a tourist in deeptech.

They fixate on vanity metrics, avoid talking about yield and qualification, and won’t commit specific intros or reserves.

Q8: How do I price pilots.

Charge enough to cover costs and signal value, with a pre-agreed conversion price and volume if success criteria are met.

Q9: How do I prepare for technical diligence.

Pre-write memos that explain results, gaps, and next steps, and organize raw data with context and version history.

Q10: Should I pursue non-dilutive funding early.

Yes if it maps to your next de-risking step and doesn’t pull you into scope that delays commercial proof.

Conclusion: Put Friedberg’s advice to work this week

David Friedberg’s All-In Podcast guidance boils down to building a business around your science, de-risking with purpose, and speaking the language of customers and factories.

If you frame milestones, capital, and risk this way, you’ll raise faster, negotiate better, and build a more resilient company.

Use the scripts, checklists, and linked resources to turn investor interest into term sheets.

For more investor insights and practical playbooks, keep exploring Capitaly.vc.

All-In Podcast Highlights: David Friedberg’s Best Advice for Deeptech Founders Raising Capital becomes your operating manual when every experiment converts to commercial traction.

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