How to Pitch David Friedberg: Data Room, Milestones, and Metrics Climate-Tech VCs Expect in 2025

‍

If you want to pitch David Friedberg in 2025, you need a crisp narrative, bulletproof data, and investor-ready proof points.
I wrote this guide to help you build a pitch, data room, and milestone plan that climate-tech VCs actually fund.
I cover the exact metrics, documents, and signals that move a deal from interesting to committed.
You will find practical checklists, examples, and a roadmap you can use this week.

‍

How to Pitch David Friedberg: Data Room, Milestones, and Metrics Climate-Tech VCs Expect in 2025

Who is David Friedberg and what does he expect from climate-tech founders?

David Friedberg is an operator-turned-investor best known for The Production Board and the All-In Podcast.
He cares about deep, durable science, defensible moats, and the ability to scale into gigantic markets.
He expects founders to articulate a clear causal chain from technology to margin to climate impact to market dominance.
He appreciates first-principles thinking and hates hand-wavy claims without measured evidence.
He leans into founders who understand both the lab and the balance sheet.

Here is what I have seen resonate with investors like him.
Be precise.
Show measured progress.
Translate science into economics.
Prove demand from credible buyers.

For more on investor expectations and founder preparation, see our blog post: How to Get Investor-Ready in Climate Tech.

What makes a standout “pitch David Friedberg” narrative in 2025?

I keep the narrative simple and testable.
I use a five-beat story that investors remember after the meeting.

Problem: The bottleneck that stalls decarbonization or resilience today.
Breakthrough: The non-obvious insight and the wedge that others missed.
Proof: Results, references, and quantified shifts in physics, biology, or cost.
Economics: Unit-level margins today and a path to category-leading gross margin.
Scale: The plan to replicate with quality at speed and to finance growth.

I avoid jargon and talk like an operator.
Example: “We drop LCOE by 22% at the array level by cutting inverter clipping losses 60% using a software-defined MPPT that runs on existing hardware.
Pilot with 81 MW under management.
Paid expansions signed.”

For more on storytelling for technical founders, see our blog post: The 12-Slide Deck for Climate Tech.

Investor readiness checklist: are you seed or Series A fit?

I classify stage by proof, not by time since incorporation.

Pre-Seed: Hypothesis proven in lab, early LOIs, clear risk map, path to TRL 5–6.
Seed: First paid pilots, validated unit process, early margins, credible buyers, 12–18 months to repeatable results.
Series A: Repeatable deployments, manufacturing method locked, burn multiple under 1.5–2.0, gross margin trending to target, real pipeline-to-revenue conversion.

If you are between stages, I pick one decisive technical milestone and one decisive commercial milestone and raise for those only.
That discipline reads as investor ready.

For more on fundraising readiness, see our blog post: Investor Readiness Checklist.

The 12-slide deck Friedberg actually needs to see

I keep the deck tight and evidence-led.
Here is the sequence I use.

Title and one-line wedge
Problem with quantified friction
Breakthrough insight and why now
How it works (diagram with one causal loop)
Proof and data (charts, not adjectives)
Product and unit economics
Market design and customer workflow
GTMP (go-to-market and pricing)
Roadmap and milestone risk burn-down
Team and unfair advantages
Financial model and funding ask
Climate impact and category dominance path

Every claim gets a footnote to a data room artifact.
That is the fastest way to gain trust.

Data room checklist for climate-tech

Investors do not fund slides.
They fund evidence.
Here is the climate-tech data room I build before the first partner meeting.

Company: Charter, cap table, board consents, option plan, key contracts.
Tech: Experimental logs, protocols, raw datasets, Git commit history, simulation files, QA/QC procedures, third-party validations.
Manufacturing: Process flow, BOP, BoM with costs, supplier quotes, yield curves, SPC charts, FMEA, MRL assessment.
Commercial: Customer list, LOIs, MOUs, MSAs, pricing sheets, pilot SOWs, references, pipeline by stage with probability model.
Economics: Unit economics workbook, COGS roll-up, capex/opex model, learning-curve assumptions, sensitivity analysis.
Regulatory and Grants: Permitting status, certifications, grant applications, award letters, IRA incentive mapping (e.g., 45Q, 45V, ITC, PTC).
Impact: LCA summary, MRV plan, avoided emissions math, boundary conditions, third-party methodology.
Financials: Historical P&L, cash flow, balance sheet, burn multiple, runway, hiring plan.
Legal: IP assignments, patent filings, trade secret policy, freedom-to-operate memo.

I keep a ReadMe file that maps every slide claim to a folder path.
I also provide a 30-minute “data room tour” loom video for speed.

For more on investor data rooms, see our blog post: Data Room Checklist for Climate-Tech Founders.

TRL, MRL, and CRL: translating readiness levels to investor proof

Climate-tech founders often say TRL 6 or MRL 4 without context.
I translate those into investor-relevant evidence.

TRL means the physics or biology works in the environment of interest with realistic interfaces.
MRL means the process is stable, documented, and capable within target spec and yield.
CRL (Customer Readiness) means a buyer can adopt with minimal change to workflow and budget.

I show a table of risks retired per milestone.
For example: TRL 6 by running 1,000-hour continuous operations at pilot scale with SPC on critical variables and zero safety incidents.

Deep tech metrics that matter: from lab to factory

Investors want the smallest set of metrics that predict bankable scale.

Yield and variability: Cpk on critical dimensions and % out-of-spec.
Throughput: Cycle time, uptime, and availability by bottleneck step.
Learning rate: % cost reduction per doubling, broken down by materials, labor, overhead.
Quality cost: Scrap, rework, warranty as % of COGS.
Safety and compliance: LTIR, near misses, audits passed.

I avoid vanity numbers like “number of lab runs” unless tied to capability improvements.

Climate tech KPIs for software-enabled solutions

Software-led climate companies need different KPIs than hardware.
Here is the set I use.

ARR and net revenue retention for enterprise deployments.
Activation rate and time-to-value for workflows that displace Excel or manual processes.
MRV accuracy measured against ground truth or certified baselines.
Gross margin after data costs and compute.
Sales efficiency (magic number) and payback periods on CAC.

I also quantify climate impact per dollar of ARR to show leverage.
Example: 0.8 tCO2e avoided per $1 ARR by optimizing process heat scheduling.

For more on climate KPIs, see our blog post: Climate KPIs That Actually Close Rounds.

Climate impact math: MRV, LCA, and avoided emissions

Impact without measurement is marketing.
I ground claims in standards and context.

LCA with clear system boundaries, functional unit, and scenario assumptions.
MRV with evidence chains, calibration plans, and auditability.
Avoided emissions calculated against a realistic counterfactual with sensitivity bands.

I add a one-slide “Impact Summary” with numbers investors can repeat: “-2.1 tCO2e per unit over 10 years at 90% confidence.”

Milestones roadmap: what to prove in the next 18 months

I split milestones into technical, commercial, and organizational, each tied to a risk retired and a valuation step-up.

Technical: Hit 5,000-hour durability at pilot under ambient conditions with ±2% performance drift.
Commercial: Convert three pilots into multi-year MSAs with standard pricing and NRR over 120%.
Organizational: Hire a head of quality and stand up SPC across lines.

I label each milestone with date, cost, lead, and acceptance criteria, not just aspirations.

Go-to-market models that resonate with climate VCs

Investors fund go-to-market that matches buyer behavior and cash cycles.

Wedge with fast payback: 6–12 month payback pilots that expand into multi-site rollouts.
Financing-enabled: SPVs or partner project finance to remove capex from the buyer decision.
Channel leverage: OEM bundles or EPC partnerships to reduce sales cycle risk.

I avoid custom projects that stall product learning unless they unlock a repeatable template.

For more on GTM in climate, see our blog post: Climate GTM Playbooks That Work.

The economics: capex, opex, COGS, and unit economics

I put unit economics up front and expose the assumptions.

COGS with a bom-by-bom roll-up and vendor quotes.
Capex per unit capacity and how it trends with learning.
Gross margin today and at 10x volume with evidence.
Service and warranty reserves based on real failure modes.

I include a tornado chart so investors see which levers matter most and how risk is managed.

Project finance readiness and offtake strategy

Many climate companies cross into infra-like cash flows.
I show bankability early to avoid later surprises.

Offtakers with credit profiles, LOIs, or term sheets.
Contracts with take-or-pay, floor pricing, or hedges.
EPC and O&M partner readiness and wrap options.
Interconnection and permits status with gates and lead times.

I map how equity funds tech maturation and how non-recourse capital scales deployments.

Regulatory leverage: IRA, grants, and non-dilutive capital

Non-dilutive capital is a competitive advantage in 2025.
I quantify it.

IRA credits like 45V, 45Q, ITC/PTC with eligibility criteria and transferability plans.
DOE OCED and SBIR timelines and alignment to milestones.
State incentives like LCFS or SAF credits with price scenarios.

I present a capital stack that blends equity, grants, and tax credit transfers to stretch runway without slowing execution.

Risk map: technical, scale-up, market, policy, and execution

Great investors are professional risk managers.
I show my full risk map and the plan to burn down each risk.

Technical: Failure modes and test plans.
Scale-up: Supply chain constraints and dual sourcing.
Market: Buyer budget risk and alternatives.
Policy: Sunset clauses and compliance paths.
Execution: Hiring bottlenecks and culture-of-quality checkpoints.

I color-code risks by severity and include early warning indicators that trigger mitigation actions.

Diligence-proof data: experiments, pilots, and customer references

In diligence, stories collapse and evidence remains.
I stack the deck with third-party proof.

Blind tests with independent labs.
Joint development agreements that include performance-based milestones.
Customer references with named contacts ready to speak.
Before/after data measured by the buyer, not by me.

I also include a red-team memo that lists what could be wrong and how we checked.
That honesty builds trust fast.

Team proof: why your team is built to de-risk the thesis

I show why the team reduces the specific risks in the plan.
This is not a resume dump.

Who shipped what that is similar in physics, scale, or buyer type.
Who owns which risk on the milestone map, with time allocation.
Advisors who do real work like quality systems, regulatory, or project finance.

I include a hiring plan that lines up with milestone gates and avoids premature specialization.

Fundraising mechanics 2025: SAFE vs priced, valuation, syndicates

Terms signal maturity and confidence.
Here is how I decide in 2025.

Early Seed: Post-money SAFE with MFN and pro-rata rights if speed matters and proof is near.
Late Seed/Series A: Priced round with a lead who underwrites the risk map and reserves for follow-on.
Syndicates: I anchor with one decision-maker, then layer strategic angels who bring customers or manufacturing capability.

I keep a clear use-of-proceeds tied to valuation step-ups and set expectations on board cadence and reporting.

For more on terms and process, see our blog post: Raising Climate Rounds in 2025.

AI in climate-tech: where it helps and what to avoid

AI helps when it turns messy real-world signals into decisions that move energy, materials, or capital flows.
It hurts when it adds latency, opacity, or data cost without margin.

Good: Predictive maintenance on critical assets with verified downtime reduction.
Good: Forecasting and dispatch that lowers energy cost under real tariffs.
Risky: MRV models without calibration or audit pathways.
Risky: Black-box optimizations in regulated environments without explainability.

I disclose data rights, model governance, and per-inference costs so investors can trust the margin.

How to tailor your ask for David Friedberg and All-In style investors

I make the ask simple and auditable.

Amount: The minimum to hit three milestones that unlock the next round.
Use: 70% on milestone-critical items, 20% on GTM, 10% on flexibility buffer.
Proof cadence: Monthly metric reviews and quarter-end deep dives.

I end with a one-sentence commitment: “In 18 months, we will have a repeatable product with 50% gross margin and three named customers under multi-year MSAs.”

Pricing models that unlock adoption and protect margin

Pricing is strategy in numbers.
I choose a model that aligns value and cash timing.

Performance pricing when impact is measurable and defensible.
Per-site tiers for multi-location rollouts with volume incentives.
Hardware + SaaS to stack margin and create lock-in without vendor fatigue.

I always show a path to 60%+ blended gross margin as scale kicks in.

Manufacturing maturity without overbuilding

Premature factories kill runway.
I prove capability in steps.

Pilot line to lock process windows and yields.
Bridge capacity through contract manufacturers with clear quality gates.
Dedicated line only after demand and process capability converge.

I track OEE, yield, and cost learning monthly and share the dashboard in updates.

Supply chain strategy for resilience and speed

I de-risk single points of failure early.
I dual-source critical components and pre-qualify alternates.

Long-lead items with buffer inventory or vendor-managed stock.
Trade and compliance checks for tariffs, export controls, and origin rules.
ESG of suppliers to align with customer procurement screens.

I treat supply chain as a product feature, not an afterthought.

Customer proof that shortens diligence

Real customers speak louder than any pitch.
I prepare references that can answer three questions fast.

Did the product hit spec under real conditions.
Did it pay back within expectations.
Would they expand and at what pace.

I also include lost deals and the lessons I applied to win the next one.
That maturity stands out.

Market sizing that avoids the TAM trap

Investors care about serviceable markets you can actually enter now.
I size SAM and SOM with buyer counts, price, and adoption curves.

Bottom-up using named segments and budget owners.
Competitor share and displacement wedges.
Expansion logic into next adjacent segments over time.

I never anchor the story with a trillion-dollar TAM slide without the bottom-up logic.

Operating cadence and investor reporting that builds trust

I win trust with consistent, boring execution.
Investors want to see the same dashboard every month.

North-star metric tied to value.
Leading indicators for production and pipeline.
Cash and runway with scenario bands.

I share misses and course-corrections the same week, not the next board meeting.

Common mistakes founders make when pitching deep tech

I have made these mistakes and fixed them the hard way.

Overselling timelines and under-resourcing quality.
Hiding uncertainty instead of bounding it.
Chasing custom projects that never repeat.
Building factories before process capability is real.

The cure is simple.
Choose fewer milestones you can crush and publish the receipts.

Positioning your company within the climate stack

Investors map you against the climate value chain.
I explicitly show where I sit and who I integrate with.

Upstream inputs and constraints.
Midstream processing and logistics.
Downstream customers and compliance.

I then show how the position gives pricing power and data network effects over time.

Building your All-In moment: how to close the round

Closing is about momentum and clarity.
I set a tight process and define a commit window.

Stage the diligence with pre-shared data room and weekly Q&A.
Reference calls pre-scheduled with customers and partners.
Term sheet target date with a clear decision framework.

I ask directly for the lead and next steps.
Polite, specific, and easy to say yes to.

FAQs

How many slides should I use in the first meeting?

I use 12 focused slides and leave the rest for appendix and data room.
Clarity beats volume.

Do I need an independent LCA before Seed?

If impact is core to your value, yes.
A scoping LCA or third-party methodology review is enough at early seed.

What burn multiple should I target at Series A?

I target 1.5 or better.
Hardware-heavy businesses may be a bit higher if gross margin and bookings support it.

Can I raise on LOIs?

Yes, if the LOIs are from credible buyers, time-bound, and include price and volume ranges.
Paid pilots convert better than LOIs.

What if my tech timelines are uncertain?

Bound uncertainty with ranges and acceptance tests.
Show how each test reduces variance and what you will do if it fails.

How detailed should my unit economics be?

Detailed enough that an investor can rebuild them in Excel from your BoM and process assumptions.
Provide sources and sensitivities.

How do I show project finance readiness?

Line up offtake interest, EPC partners, and a term sheet template.
Show DSCR under conservative price scenarios.

What KPIs matter most for climate SaaS?

NRR, gross margin after data and compute, MRV accuracy, and sales payback.
Impact per revenue dollar is a plus.

Should I use a SAFE or priced round in 2025?

Use a SAFE for speed at early seed.
Use a priced round when you have repeatability and want a committed lead with reserves.

What internal processes impress climate VCs?

Quality systems, SPC, safety culture, and disciplined monthly operating reviews.
These show you can scale responsibly.

Conclusion

Raising in climate tech is about disciplined proof, not perfect certainty.
Investors like David Friedberg fund founders who translate science into margin and margin into scaled climate impact.
Your deck, data room, and milestones should make that translation obvious and auditable.
If you want to stand out and successfully pitch David Friedberg, anchor your story in evidence, economics, and execution.

Subscribe to Capitaly.vc Substack (https://capitaly.substack.com/) to raise capital at the speed of AI.