Published On Nov 26, 2025
Updated On Nov 26, 2025
Why Most Ethereum & L2 Grant Proposals Fail (and How to Fix)
The Ethereum ecosystem grant landscape has hardened.
Less than 1 in 8 proposals now pass early review across Arbitrum, Optimism Missions, Base Builder Seasons, and Scroll zkEVM programs.
If you’re building in the Ethereum ecosystem, those are your odds.
Reviewers fund teams that show traction, ecosystem fit, and measurable progress, and move forward.
Securing a grant in this phase demands clarity, data, and precision. Yet most teams still miss the signals that matter most.
This guide breaks down why that happens and how to fix the mistakes before your next submission.
Let’s begin one by one.
Not Aligning Your Grant Proposal With The Right Ecosystem Priorities
Ethereum grant programs in 2025 are selective by design.
As the Questbook Arbitrum dashboard (Nov 2025) shows, only 108 of 827 proposals under Arbitrum DDA 3.0 were approved, and the acceptance rate is nearly 13%.
This sharp filter reflects a shift that ecosystems now reward alignment over abstraction.
Proposals that directly solve live bottlenecks within a defined domain, such as Stylus tooling, Orbit observability, or OP Stack consistency, are prioritised.
Those framed as broadly useful for Web3 rarely make it past the first review.
How Web3 Grant Reviewers Evaluate
Each ecosystem operates within its own strategic bottlenecks, which can be technical, governance, or adoption-related.
When assessing a proposal, reviewers begin with one question:
“Does this proposal directly accelerate a bottleneck within one of our active domains?”
That single filter determines initial scoring. Ecosystem teams look for alignment, not just abstraction.
Their priorities are sharp and constantly shifting:
- Arbitrum: Stylus tooling, Orbit observability, bridging trust removal
- Optimism/Superchain: OP Stack consistency, cross-rollup message reliability, and Account Abstraction adoption.
- Base: Developer onboarding velocity and consumer-grade UX
- Scroll: zkEVM proving costs, circuit debugging, calldata compression
These are not just assumptions, they’re documented priorities across Arbitrum’s DDA 3.0, Optimism Mission Requests, and Base Builder Grants.
For instance, Arbitrum’s DAO strategic update sets KPIs around Orbit chain launches and a critical tooling gap in safety and monitoring.
Similarly, Optimism’s Q3 2025 Mission Requests highlight inter-rollup communication reliability as a funded bottleneck.
Why Teams Get This Wrong
Even strong technical teams misstep here. The most common reasons:
- Misinterpreting the domain: The proposal solves a broad Web3 issue rather than a domain-specific constraint.
- Lack of clarity on scope: The proposal doesn’t specify whether it’s targeting a single ecosystem or a multi-chain use case.
- Weak ecosystem integration: No mention of how the work interacts with key frameworks like Stylus, OP Stack, Orbit SDK, or zkEVM.
- No urgency narrative: The proposal doesn’t frame why the bottleneck must be solved now, or what risk persists if it isn’t.
Reviewers recognize these gaps instantly. A technically good proposal still fails if its domain alignment is unclear.
How to Fix It
Identify a Live Bottleneck Within the Domain
Anchor your proposal to a verified ecosystem signal, not just with an assumption. Take reference from official updates, governance posts, or dashboards.
For example, Arbitrum DDA 3.0 reviews have repeatedly flagged the lack of standardized safety and monitoring tools for Orbit appchains.
If your proposal directly targets that, make the connection explicit.
Use Ecosystem-Native Language
Reflect the stack you’re building for. Reviewers notice technical vocabulary like Stylus modules, OP Stack clients, Scroll prover circuits, or Base SDK workflows.
Precision in phrasing signals depth in understanding.
Align Your Milestones with Ecosystem Milestones
Your roadmap should parallel the chain’s own evolution.
- Arbitrum: Stylus acceleration and Orbit standardization
- Optimism: Superchain message reliability
- Base: Account abstraction and onboarding velocity
- Scroll: Proof compression and zkEVM efficiency
This shows reviewers that your progress compounds the ecosystem’s strategic trajectory.
Quantify the Cost of Inaction
Strong proposals don’t just describe what they will do instead, they show what happens if the bottleneck isn’t solved.
Example: Without trust-minimised bridging, Orbit appchains face rising fragmentation and capital inefficiency.
This urgency framing is a differentiator in 2025 reviews.
Show Proof of Domain Fit
Evidence matters more than promise. Teams need to include testnet deployments, code commits, or early ecosystem collaborations.
Grant reviewers increasingly prioritize proof of execution over project intent.
But domain alignment isn’t enough, because reviewers still need proof that the problem you’re solving is real, recurring, and ecosystem-validated.
Missing the Ecosystem’s Actual Pain Points During Grant Framing
In 2025, ecosystem grants across Ethereum’s L2 landscape reward validated pain, not speculative opportunity.
A proposal may be technically solid, but if it doesn’t address a bottleneck the ecosystem itself has surfaced, it rarely moves past initial review.
What Reviewers Actually Evaluate
Reviewers inside any ecosystem, be it Arbitrum, Optimism Missions, Base Seasons, and Scroll zkEVM cycles, ask one question:
“Has the ecosystem acknowledged this problem, and can you prove it exists?”
Reviewers look for patterns like:
- Governance or delegate discussions where the same issue appears repeatedly.
- On-chain or infrastructure data showing measurable degradation in state bloat, failed proofs, or sequencer lag.
- Official ecosystem documentation or RFPs confirming the bottleneck’s existence.
If those signals are missing, the “problem” is treated as noise.
How This Mistake Shows Up
- Feature-first framing: Teams generally focus on building a universal onboarding SDK or analytics suite. It is useful, but detached from context. Reviewers ask: Why does this need to exist here, now?
- Generic pain statements: Teams start with addressing the pain point, like onboarding is hard or tooling gaps exist. But without chain-specific data, they are just filler.
- No infra validation: Arbitrum’s public documentation on state growth in Orbit chains outlines storage and latency trade-offs. While ignoring it, the team shows reviewers that you’re not building for on-chain reality.
- Assumptions packaged as facts: “Everyone knows onboarding is broken.” Reviewers want metrics, not claims.
How to Fix It
Source the Problem from the Ecosystem
Start by anchoring your proposal in signals the ecosystem itself has surfaced, governance threads, foundation updates, developer RFCs, or on-chain telemetry.
Every ecosystem, whether built on the OP Stack, Orbit SDK, Polygon CDK, or a custom zkEVM, has public indicators of where friction exists.
Examples of credible signals:
- Arbitrum: Check for official guidance cites state-growth pressure on Orbit rollups, affecting node stability and cost.
- Optimism / Superchain: See engineering threads highlight version drift and cross-rollup dependency misalignment as ongoing interop challenges.
- Base: Look for developer SDK updates, call out session key lifecycle and gas sponsorship pain points in onboarding flows.
Teams can use each ecosystem’s public documentation, engineering updates, or governance metrics to extract a real, surfaced pain point.
Quantify It with Data
Once identified, support the problem with measurable, on-chain, or infra evidence. Reviewers trust quantification over narrative.
Examples of how to make it tangible:
- Track latency, gas, or memory metrics on devnets to demonstrate technical debt or bottlenecks.
- Compare cross-chain message success rates or version compatibility metrics between releases.
- Measure onboarding funnel drop-offs in AA flows or SDK integrations.
- Correlate the prover cost or batching delay data with published fee breakdowns.
Numbers build trust. A small chart from your test logs often tells the story better than a paragraph of claims.
Show the ecosystem-level risk of ignoring the problem
Ecosystem grants are not written to reward innovation in isolation, but they’re designed to de-risk the network’s trajectory.
Frame your proposal in terms of what fails if this bottleneck persists:
- Performance risk: Chain instability, slower block times, higher sequencer cost.
- User risk: Fragmented liquidity, failed messages, broken onboarding flows.
- Growth risk: Developer drop-off, integration stagnation, loss of ecosystem trust.
Show reviewers what the downside looks like. Ecosystems react faster to risk than to features.
Prove the problem surfaced multiple times
One comment or forum post isn’t enough proof. Reviewers look for patterns, not opinions.
To make your problem statement credible, back it up from different, reliable places:
- Official sources: Ecosystem docs, blog posts, or roadmaps that mention the issue.
- Community discussions: Governance threads, dev calls, or GitHub issues where the problem keeps coming up.
- Your own data: Logs, metrics, or tests that show the issue happening in real setups.
When these signals align, reviewers see it as a real, recurring ecosystem pain, not just your personal assumption.
Even after defining the problem clearly, another critical filter remains: demonstrating enough traction to prove your team can actually execute.
That’s the next mistake.
Applying For Ethereum And L2 Grants Without Proof of Execution
In 2025, the difference between a funded and a rejected grant submission in the Ethereum ecosystem often isn’t the idea, but it’s the proof that you’ve begun building.
Reviewers are no longer willing to imagine your progress; they expect to see it.
What Reviewers Actually Evaluate
When assessing proposals for ecosystems such as Arbitrum Foundation (DDA 3.0), Optimism Foundation (Superchain Missions), Base Labs (Builder Seasons) and Scroll Labs (zkEVM Grants), reviewers ask:
“Do we have evidence this team can deliver?”
They look for:
- A working demo or testnet deployment that reviewers can inspect or interact with.
- A public repository (GitHub or similar) showing continuous commits, feature branches, bug fixes and documentation.
- A technical memo or experiment log that records what you tested, measured, and learned.
- Early ecosystem feedback or partner engagement, e.g., developer on-ramps, builder integrations, pilot users.
- Partial roadmap execution, at least 20% - 30% of planned milestones completed before the grant ask.
- Integration with stack-native modules by showing familiarity with L2 architecture, e.g., OP Stack, Orbit SDK, zkEVM prover pipeline.
If your submission lacks all of this, reviewers default to treating it as conceptual, not operational.
How This Mistake Shows Up
- Absence of surface: No demo or testnet means reviewers cannot evaluate what you will build.
- Inactive repository: A brand-new or sparsely updated GitHub signals a low execution rhythm.
- Elegant design, no execution: Proposals focusing on diagrams and architecture but ignoring real constraints such as gas cost, proof latency or chain-specific mechanics get flagged as academic.
- Roadmap starting at zero: If everything begins after funding, reviewers view the risk as high. Without a visible pre-funding motion, trust is low.
- Limited traction definition: Founders often confuse “I have a good idea” with traction. To reviewers, traction only means proof, not promise.
How to Fix It
Launch a Minimum Viable Build Before Applying
Your MVP doesn’t need to be market-ready, it needs to be verifiable. Launch something small, public, and functioning.
For example:
- A devnet deployment that interacts with your smart-contract module.
- A CLI tool or UI that hits your contract endpoints.
- A test flow that uses stack-native hooks, e.g., account-abstraction in Base, message bridging in OP Stack.
In 2025, teams with visible builds had significantly higher success rates in grant screening.
Maintain a Transparent, Active Repository
Your code repository is your execution track record. Reviewers look for:
- Frequent commits and branches.
- Documentation of experiments and results.
- Clear trace of development path (including failures).
A repository with 6+ months of consistent activity is a strong signal of reliability.
Publish a Short Technical Memo Grounded in Real Experiments
A concise one-pager that describes:
- What you tested.
- The metrics or logs captured.
- What changed as a result.
For example, In an Orbit-based devnet, we observed that state-size expansion led to higher RAM and read latency, so we applied compression logic and reduced latency materially (e.g., a drop in p95 latency).
This gives reviewers concrete data, not just speculation.
Show Early Ecosystem Engagement
Referring to builder calls, delegate feedback, or integration trials demonstrates you’re not working in isolation.
Phrase it like:
“We engaged two builder teams on Orbit devnet, ran our monitoring hook, and refined based on their latency logs.”
Mentioning that interaction signals you’re aligned with the ecosystem’s stack priorities.
Execute Before Funding
Complete meaningful milestones before submitting. Even basic completions count:
- Smart-contract skeletons deployed.
- Proof-pipeline adapters integrated.
- Monitoring dashboard with limited functionality.
This evidence points shift in the proposal from “planned” to “already in motion”. Reviewers consider this lower risk and higher priority for funding.
For reviewers, traction isn’t optional. It is the strongest currency in modern grant evaluation.
And even with traction, teams lose credibility fast if they misjudge the next filter: unrealistic milestones, timelines, and budgets.
That’s Mistake 4.
Setting Unrealistic Milestones, Timelines, Or Budgets In Your Proposal
The fastest way to lose reviewer confidence is to overpromise and under-specify.
Ambitious plans may sound inspiring to founders, but to reviewers, compressed timelines, inflated KPIs, or unrealistic budgets signal something else: inexperience in real L2 execution.
Modern Ethereum-aligned ecosystems operate under production-grade constraints like gas dynamics, audit queues, version drift, and prover latency.
If your plan doesn’t reflect that reality, it won’t survive technical due diligence.
What Reviewers Actually Evaluate
Every serious grant committee, whether under Arbitrum’s DDA 3.0, Optimism’s Superchain Missions, Base Builder Seasons, or zkEVM grant rounds, begins with one filter:
“Can this team deliver this plan under real-world network conditions?”
They examine:
- Milestone specificity: Do deliverables map to concrete modules, test results, or integrations?
- Timeline realism: Have audit, integration, and version buffers been included?
- Budget credibility: Are costs justified against market rates for smart-contract engineering, audits, infra, and ops?
- Dependency mapping: Are chain-level or tooling dependencies, for e.g., OP Stack updates, Orbit SDK versions or prover APIs acknowledged and timed correctly?
- Impact alignment: Are KPIs measurable within the chain’s public metrics with throughput, reliability, or adoption curves?
How This Mistake Shows Up
Vague Milestones
Phrases like “Phase 2: Infrastructure build-out” or “Enhance scalability” mean nothing to reviewers. They expect testable outputs:
- Integrate cross-rollup batcher API
- Deploy Orbit monitoring module and publish benchmark logs
- Run zk-proof compression tests with Scroll v0.10
Incomplete Timelines
Timelines that skip audits, version upgrades, or testnets show poor planning discipline.
A credible timeline accounts for design → implementation → unit & integration tests → testnet deployment → partner feedback → audit → mainnet readiness, with buffers for version drift or infra delays.
Implausible Budgets
Budgets that are too low or too high both break trust. In 2025, reviewer references are roughly:
- Smart-contract + protocol dev: $100–$150+ per hour per senior engineer
- Infrastructure + monitoring ops: $50–$349/month
- Audits: $5k–$15k depending on complexity
If your numbers fall outside these ranges without justification, reviewers assume you haven’t priced execution realistically.
Inflated Impact Claims
KPIs that promise “10x throughput” or “universal onboarding” without measurable targets read as marketing, not metrics. Impact must tie directly to observable chain primitives:
- Orbit: state-growth reduction, uptime %
- OP Stack: cross-rollup message success rate
- Base: session-key activation and user retention
- Scroll: proof generation latency or fee reduction under load
These failure signals are frequently called out in grant reviews and forum commentary when reviewers explain rejections.
How to Fix It
Write milestones as verifiable outputs
Avoid “phases” and focus on what can be measured or deployed. Instead of “Build core infra,” say:
“Deploy batcher API v1 on devnet, execute 50 cross-rollup test transactions, publish results to repository.”
Reviewers fund concrete outputs, not abstract progress.
Design Timelines That Survive Real Engineering Friction
Show the real sequence: design → build → test → integrate → audit → deploy.
Include 15-25 % time buffer for audits, version misalignment, or L2 infra updates. This shows operational maturity.
Align the budget to the real 2025 cost bands and justify each line
Divide budgets into modules:
- Core dev
- Infra and testnets
- Audits and verification
- Ecosystem integration
- Post-grant maintenance
Use public data from Questbook domain allocations, past LTIPP reports, or Foundation transparency dashboards as reference points.
Define KPIs That Map to Ecosystem Metrics
Tie each KPI to something reviewers can independently verify:
- Increase cross-rollup message success from 92 % → 98 %.
- Reduce Orbit state-growth rate by 10 % via compression.
- Cut zk-proof generation latency from 1.3 s → 0.9 s.
This level of specificity builds credibility.
Show dependency awareness and mitigation steps
Every stack evolves. Reviewers expect awareness of moving parts.
List external dependencies (e.g., OP Stack upgrades, Sequencer API changes, prover releases) and mitigation steps (e.g., fallback RPCs, version locks, modular adapters).
A simple risks & mitigations table demonstrates foresight.
Benchmark Against Funded Precedents
Use previously funded proposals as calibration references.
If your structure and numbers mirror past successful Questbook or LTIPP projects, reviewers interpret your plan as familiar, executable, and trustworthy.
Before reviewers ever read your words, they measure your plan’s realism. But once they do, the next filter is tone, and that’s where many teams fail.
Using AI-Generated Content That Lacks Real Builder Insight
AI is useful. It helps structure sections, tighten grammar, and keep formatting clean. The problem is not the tool.
The problem is content that reads like it was generated by someone who did not build the thing.
Reviewers across Arbitrum, Optimism, Base, Scroll, zkSync, Linea, and Starknet see the same patterns every cycle.
They do not penalize AI; they penalize the absence of chain-specific insight and missing technical fingerprints.
What Reviewers Actually Evaluate
Reviewers don’t judge writing quality. They judge whether the writing reflects:
- Authenticity: Does it sound like someone who actually built, tested, and debugged the system?
- Ecosystem fluency: Does the content reflect real familiarity with OP Stack, Orbit, Nitro, Scroll prover behaviour, or Base UX constraints?
- Specificity: Are the details grounded in actual experiments and constraints, or vague?
- Technical fingerprints: Are there real engineering scars, nuances, caveats?
- Ownership: Does the proposal sound like the authors are the same people shipping the product?
If these are missing, it does not matter how smooth the prose is. It reads like marketing. Reviewers move on.
How This Mistake Shows Up
- Template language: AI uses phrases like “unlocking new possibilities”, “redefining developer experience”, and “driving ecosystem growth”. These phrases fit any chain and signal low context.
- Chain-agnostic framing: Text that could be copy-pasted to Solana or Cosmos without edits. Each L2 has distinct bottlenecks. If you are not anchoring to them, trust drops.
- Vocabulary misuse: Mixing OP Stack with Orbit terms, generic “zk” claims for Scroll without prover details, outdated references to Arbitrum priorities, vague Base UX claims without AA specifics.
- Polished but shallow paragraphs: Elegant prose without a single log line, metric, or failure mode. Real builders mention what broke.
- Recycled proposals: Same paragraphs across ecosystems with only the chain name swapped. Cross-ecosystem reviewers spot this instantly.
How to Fix It
Use AI for structure, not for insight
Let AI help format sections or clean grammar. But every claim, nuance, and technical detail must come from the team’s real work.
Add chain-specific engineering details only a real builder knows
For example:
- OP Stack: Cross-rollup message retries spiked when we upgraded to op-node v1.8.2; we added a sequencing delay of 250 ms to stabilize the inbox/outbox flow.
- Arbitrum Orbit/Nitro: State reads stalled at ~12 ms p95 when state size passed 85 GB on our Orbit devnet. We added a key prefix compaction and saw p95 drop to 8.7 ms.
- Scroll zkEVM: Batch 64 failed until we reduced the circuit’s lookup table size by 11% and increased prover queue depth from 8 to 16.
- Base AA: Session key init failed for 7.6% of flows due to a paymaster gas sponsorship guard. We added a preflight check and cut failures to 2.1%.
Show artifacts reviewers can verify
- Testnet addresses and contract ABIs
- Short log snippets with timestamps
- Commit hashes and tags
- Minimal repro steps
- Screenshots or 30-second clips of the flow
One verifiable artifact is worth more than ten smooth paragraphs.
Write with technical precision, not marketing polish
Prefer concrete nouns and verbs: “batcher API”, “inbox proof”, “sequencer retries”, “prover queue”, “session init”.
- Cut adjectives and claims you cannot measure.
- Include at least one failure and what you changed.
Add short specifics from your own experiments
Even one line like: “During early testing, our Scroll prover integration stalled on batch 64 until we adjusted the circuit parameters.”
This level of detail is impossible for AI to fake convincingly.
AI is not disqualifying. Lack of substance is. The proposals that win read like a lab notebook from the actual team: parameter choices, versions, errors, fixes, and measurable outcomes tied to the target chain.
A proposal that could only have been written by your team is the one reviewers support.
Conclusion: Grants Don’t Reward Potential, They Reward Prepared Builders
You now understand the five mistakes that quietly eliminate strong teams long before reviewers reach the final vote.
These aren’t theoretical pitfalls; they’re patterns that repeat across Arbitrum, Optimism Missions, Base Seasons, Scroll zkEVM cycles, and every ecosystem that has matured past early-stage funding.
The shift is simple: Ecosystem grants in 2025 don’t fund ideas. They fund evidence.
Reviewers want to see:
- A domain mapped to a real ecosystem bottleneck
- A problem validated through public signals and on-chain behavior
- Traction that proves you can execute without hand-holding
- Realistic milestones grounded in engineering reality, and
- A proposal written by the people actually building the product, not generated by AI templates.
When these elements come together, a grant becomes an accelerant instead of a dependency. When they’re missing, even the strongest concept collapses in screening.
And that’s the transition point teams need to internalize: A high-quality proposal isn’t about writing more. It’s about showing more context, more proof, more ownership, more clarity.
At Lampros Tech, we help builders turn execution into evidence and evidence into funding.
Our grant support program framework aligns your proposal with what reviewers actually prioritize: clarity, traction, and credibility.
Barsha Mandal
Growth Lead
