I'll search for DeepMind projects led by Demis Hassabis.Now I have enough information to compile a comprehensive list.

DeepMind Projects (Led by Demis Hassabis)

Games & Reinforcement Learning

Science & Biology

Mathematics & Algorithms

Materials & Weather

Healthcare

Generative AI (post-Google Brain merger)

Other Notable Work

• Neural Turing Machines (2014) – Neural networks with external memory
• Differentiable Neural Computers (2016)
• Dopamine reward prediction research (neuroscience contributions)
• Data center cooling optimization (40% energy reduction for Google)

Let me search for specific gaps and pain points for scientists using LLMs in research.Now I have comprehensive data. Let me compile the list for scientists who are newbies in LLM-driven research.

Underserved Needs for Scientists (Newbies in LLM-Driven Research)

Based on documented pain points, failures, and gaps in 2025 research:

1. CITATION & REFERENCE VERIFICATION

2. LITERATURE REVIEW & DISCOVERY

3. DATA ANALYSIS & STATISTICS

4. SCIENTIFIC WRITING & COMMUNICATION

5. REPRODUCIBILITY & DOCUMENTATION

6. HYPOTHESIS & EXPERIMENTAL DESIGN

7. CODE & COMPUTATION FOR NON-PROGRAMMERS

8. DOMAIN-SPECIFIC SCIENTIFIC TOOLS

9. GRANT & PROPOSAL WRITING

10. PEER REVIEW & MANUSCRIPT IMPROVEMENT

11. SCIENTIFIC KNOWLEDGE MANAGEMENT

12. HALLUCINATION & QUALITY SAFEGUARDS

13. COLLABORATION & LAB MANAGEMENT

14. SCIENTIFIC FIGURE & VISUALIZATION

15. TRAINING & SKILL BUILDING

Summary: Biggest Gaps by Impact for Scientists

Key Insight: The "Scientific Safety Net"

Just like vibe coding needs a safety net for non-coders, LLM-driven research needs a verification layer that:

1. Catches fabricated citations before submission
2. Validates factual claims against actual literature
3. Ensures statistical soundness of AI-suggested analyses
4. Documents provenance of AI-assisted work
5. Maintains reproducibility of computational experiments

The opportunity is in building trust infrastructure for AI-assisted science—tools that let researchers confidently use LLMs while maintaining scientific integrity.

Let me search for specific gaps and pain points for non-coders/newbies in the vibe coding space.Now I have enough data. Let me compile the comprehensive list of underserved needs for newbie/non-coders in the vibe coding age.

Underserved Needs for Newbies & Non-Coders in Vibe Coding Age

Based on actual pain points reported in 2025, here's a long list of high-demand, low-supply opportunities:

1. DEBUGGING & ERROR HANDLING

2. SECURITY & VALIDATION

3. DEPLOYMENT & HOSTING

4. CODE QUALITY & MAINTENANCE

5. LEARNING & SKILL BUILDING

6. PROJECT MANAGEMENT & STRUCTURE

7. INTEGRATION & APIs

8. DESIGN & UI

9. COST & RESOURCE MANAGEMENT

10. COLLABORATION & HANDOFF

11. GOVERNANCE & COMPLIANCE

12. SPECIFIC USE CASE TOOLS

Summary: Biggest Gaps by Impact

Bottom line: The entire "vibe coding safety net" category is wide open—tools that catch problems before deployment, explain what went wrong, and help non-coders ship without fear. This is the infrastructure layer missing from the current ecosystem.

Based on my extensive research, here's my analysis of underserved API market segments with high demand but limited/inadequate supply:

API WHITESPACE OPPORTUNITIES: Major Gaps in 2025-2026

1. SOCIAL MEDIA DATA ACCESS (Critical Gap)

Problem: Major platforms have systematically closed API access, creating massive unmet demand.

Reddit:

• 1,000-post ceiling regardless of pagination
• No historical data access, no date-range filtering
• No NSFW content since mid-2023, no webhooks
• Apollo would have cost $20M/year under new pricing
• Rate limits: 60 req/min OAuth, 100/10min unauthenticated

Twitter/X: Enterprise tier now $42,000/month, effectively killed third-party ecosystem

Opportunity: Alternative social data aggregation APIs (Data365 emerging), decentralized platform data access (Mastodon 1.5M MAU, Bluesky growing but still small vs. Threads 130M)

2. GOVERNMENT & PUBLIC RECORDS (Fragmented)

Current State:

• api.data.gov covers only 25 agencies, 450+ APIs but incomplete
• Data.gov catalogs metadata only, not actual datasets
• State/local government data scattered across thousands of county/municipal systems
• No unified API for court records, property records, business registrations, permits, licensing

Specific Gaps:

• Public records require navigating thousands of disconnected county systems
• No standardized format across jurisdictions
• Historical government data often inaccessible
• PACER exists for federal courts but state courts are fragmented

Opportunity: Unified public records API aggregating county/state data with normalized schemas

3. BUILDING PERMITS & ZONING DATA (Emerging)

Current State:

• Shovels.ai and ATTOM emerging but coverage incomplete
• 2,000+ building departments with different systems
• Zoneomics covers zoning but permit data fragmented
• Most cities have open data portals but no standardization

Pain Points:

• Each municipality has different data formats
• No real-time permit tracking across jurisdictions
• Zoning data interpretation varies by locality

Opportunity: Nationwide standardized permit/zoning API with AI normalization (Shovels acquiring ReZone shows market validation)

4. INSURANCE DATA INTEGRATION (Legacy Chaos)

Current State:

• 67% of insurance executives consider API strategy critical
• Only 23% report having mature API programs
• 85% struggle with legacy system integration

Specific Gaps:

• Underwriting data still siloed across systems
• Claims processing requires manual data entry across platforms
• No unified risk assessment data APIs
• IoT/telematics data integration limited

Opportunity: Insurance data normalization layer, embedded insurance APIs, claims automation APIs

5. SUPPLY CHAIN & LOGISTICS (EDI vs API Transition)

Current State:

• Much supply chain data still moves via batch EDI transmissions
• $1T+ in annual supply chain operation errors in US alone
• Fragmented carrier APIs with inconsistent data structures

Pain Points:

• Legacy EDI systems don't support real-time visibility
• Each carrier requires separate integration
• No unified multi-modal tracking (ocean + rail + truck)
• Warehouse-to-carrier data exchange still manual

Opportunity: EDI-to-API translation layer, unified multi-carrier tracking API, real-time inventory sync APIs

6. ENERGY & UTILITY DATA (Regulated Complexity)

Current State:

• ~3,000 electric utility companies in US alone
• Smart meter rollout accelerating but data access fragmented
• UtilityAPI, RateAcuity emerging but coverage gaps remain

Specific Gaps:

• No unified consumer energy data access
• Rate structures vary wildly (RateAcuity helps but expensive)
• Real-time pricing APIs limited to few markets
• EV charging data fragmented across networks

Opportunity: Consumer-authorized utility data API, unified rate calculation API, EV charging network aggregation

7. AGRICULTURE DATA (Underserved SMB Farmers)

Current State:

• USDA Quick Stats API exists but limited
• Multiple satellite/weather APIs (Farmonaut, EOSDA, Agromonitoring)
• Commodity pricing APIs available

Gaps:

• Local market pricing (farmer's markets, regional wholesale) not available
• Equipment/machinery data APIs limited
• Supply chain traceability (farm-to-table) fragmented
• Small farm-specific tools underserved (enterprise focus dominates)

Opportunity: Local agricultural market APIs, farm equipment telematics aggregation, supply chain traceability APIs

8. DOCUMENT DIGITIZATION & HISTORICAL RECORDS (Accuracy Gaps)

Current State:

• Tesseract, Google Vision, Amazon Textract, Mistral OCR exist
• Transkribus for historical handwriting

Remaining Gaps:

• Historical handwriting (Fraktur, cursive, non-Latin scripts) still challenging
• Multi-language document parsing accuracy issues
• Degraded/faded document handling limited
• Structured data extraction from complex layouts

Opportunity: Specialized historical document APIs, domain-specific OCR (legal, medical, genealogical)

9. VEHICLE DATA BEYOND VIN (Consumer Gap)

Current State:

• VIN decoding well-served (NHTSA, CarAPI, VinAudit, etc.)
• Vehicle history APIs exist but expensive

Gaps:

• Real-time connected vehicle data access (Smartcar emerging)
• Repair/maintenance cost prediction APIs
• Insurance claim history APIs
• Fleet management for SMBs underserved

Opportunity: Consumer vehicle health APIs, predictive maintenance APIs, unified fleet data

10. VERTICAL-SPECIFIC WORKFLOW APIS

Identified Underserved Verticals:

• Dental/Medical practices: Specialty EMR integration
• Construction: Project management + permit + compliance integration
• Legal: Court filing automation, case management
• Local government: Citizen services, permit processing
• Nonprofits: Donor management, grant compliance

MARKET CONTEXT

Capital Efficiency Examples:

• Zapier: $310M revenue on $1.4M raised
• Mailchimp: Bootstrapped to $12B acquisition

KEY PATTERNS IN GAPS

1. Walled gardens protecting revenue (social media, MLS)
2. Fragmented legacy systems (government, healthcare, education)
3. Proprietary standards blocking interoperability (LMS, EHR, insurance)
4. High compliance/regulatory barriers (healthcare, real estate, finance)
5. Data normalization needs across all sectors

Want me to dive deeper into any specific segment or create a prioritized opportunity matrix based on your interests in scientific computing and developer tooling?

Gemini API/Platform Ecosystem - Growth Index Ranking

Formula: Growth Index = YoY Growth % × Users (millions)

Tier 1: Massive Scale Products

Tier 2: Developer APIs & Tools

Tier 3: Core Models

Tier 4: Specialized APIs & Features

Tier 5: Agentic & Enterprise Tools

Tier 6: Workspace Integrations

Tier 7: GCP Product Integrations

Tier 8: SDKs & Libraries

Tier 9: Mobile & Edge

Tier 10: Chrome & Browser

Top 15 by Growth Index (Sorted)

Key Observations

1. Consumer products dominate — Gemini App and mobile apps have highest growth indices due to massive user bases
2. Search integration is force multiplier — AI Overviews reaches 2B users through existing Search traffic
3. Workspace bundling works — March 2025 mandatory bundling created instant enterprise scale
4. Developer tools lag consumer — 13M developers is large but dwarfed by 650M consumer users
5. New products (2025 launches) can't be ranked yet: Jules, AgentSpace, ADK, Nano Banana, Gemini 3
6. Gemini 1.5 is declining — deprecated models show negative growth as users migrate

Growth Velocity Leaders (Highest % Growth)

Data sources: Google I/O 2025, Alphabet earnings Q2-Q3 2025, SimilarWeb, Statista, developer surveys Note: Many products launched in 2024-2025; YoY calculations use available data points

==

Here's a comprehensive list of the Gemini API/Platform ecosystem:

Core Models

• Gemini 2.5 Pro
• Gemini 2.5 Flash
• Gemini 2.5 Flash-Lite
• Gemini 2.0 Pro
• Gemini 2.0 Flash
• Gemini 1.5 Pro
• Gemini 1.5 Flash
• Gemini 1.0 Ultra
• Gemini 1.0 Pro
• Gemini 1.0 Nano (on-device)

API Endpoints & Services

• Gemini API (REST)
• Gemini API (gRPC)
• Text Generation API
• Multimodal API (text + image + video + audio)
• Code Execution API
• Function Calling API
• Grounding API (with Google Search)
• Context Caching API
• Batch Prediction API
• Tuning API (fine-tuning)
• Embeddings API
• Token Counting API
• Safety Settings API

SDKs & Libraries

• Google AI SDK for Python
• Google AI SDK for JavaScript/TypeScript
• Google AI SDK for Go
• Google AI SDK for Dart/Flutter
• Google AI SDK for Swift
• Google AI SDK for Android (Kotlin/Java)
• Vertex AI SDK for Python
• Vertex AI SDK for Node.js
• Vertex AI SDK for Java
• Vertex AI SDK for Go
• LangChain integration
• LlamaIndex integration

Developer Tools

• Google AI Studio (web IDE)
• Vertex AI Studio
• Gemini API Explorer
• Prompt Gallery
• Model Tuning Console
• Evaluation Tools
• AI Studio Prompt Templates
• Gemini Code Assist (IDE extension)
• Gemini for Google Cloud CLI

Agentic & RAG Tools

• Vertex AI Agent Builder
• Vertex AI Search
• Vertex AI Conversation
• Grounding with Google Search
• Grounding with Vertex AI Search
• AgentSpace (enterprise)
• Agent Development Kit (ADK)
• Reasoning Engine
• Extensions Framework

Specialized Capabilities

• Live API (real-time streaming)
• Deep Think (extended reasoning)
• Long Context (1M+ tokens)
• Native Tool Use
• Code Interpreter
• Image Generation (Imagen integration)
• Video Understanding
• Audio Understanding
• PDF/Document Understanding
• Structured Output (JSON mode)
• Controlled Generation

Enterprise & Vertex AI Integration

• Vertex AI Gemini API
• Model Garden (Gemini models)
• Vertex AI Pipelines (Gemini)
• Vertex AI Feature Store integration
• Vertex AI Model Monitoring
• Vertex AI Endpoints
• Private Endpoints
• VPC Service Controls
• Customer-Managed Encryption Keys (CMEK)
• Data Residency Controls

Google Workspace Integration

• Gemini for Gmail
• Gemini for Google Docs
• Gemini for Google Sheets
• Gemini for Google Slides
• Gemini for Google Meet
• Gemini for Google Chat
• Gemini for Google Drive
• Gemini for Google Calendar
• Gemini Workspace Add-ons API
• Apps Script Gemini integration

Google Cloud Product Integrations

• Gemini in BigQuery
• Gemini in Looker
• Gemini in Colab Enterprise
• Gemini in Cloud Workstations
• Gemini in Cloud Code
• Gemini in Apigee
• Gemini in Chronicle (Security)
• Gemini in Database Studio
• Gemini in Cloud Logging
• Gemini in Dataform

Mobile & Edge

• Gemini Nano (on-device Android)
• ML Kit Gemini integration
• Firebase Vertex AI SDK
• AICore (Android system service)
• Gemini for Android Studio

Chrome & Web

• Gemini in Chrome (built-in AI)
• Prompt API (Chrome)
• Summarization API (Chrome)
• Translation API (Chrome)
• Writing Assistance API (Chrome)

Evaluation & Safety

• Gemini Safety Filters
• Responsible AI Toolkit
• Model Evaluation Service
• Bias Detection APIs
• Content Moderation API
• Harm Category Settings
• Citation Metadata

Authentication & Access

• API Key Authentication
• OAuth 2.0
• Service Account Authentication
• Workload Identity Federation
• IAM Roles for Gemini
• Quota Management
• Usage Analytics

Pricing Tiers

• Free Tier (Google AI Studio)
• Pay-as-you-go (per token)
• Provisioned Throughput
• Context Caching pricing
• Batch API pricing (50% discount)
• Committed Use Discounts

Documentation & Resources

• Gemini API Documentation
• Gemini Cookbook (GitHub)
• Prompt Engineering Guide
• Best Practices Guide
• Migration Guides (PaLM → Gemini)
• API Reference
• Quickstart Tutorials
• Sample Applications

Community & Support

• Google AI Developer Forum
• Stack Overflow (google-gemini tag)
• Discord Community
• GitHub Issues
• Google Cloud Support tiers
• AI Studio Feedback

Google Developer Ecosystem - Growth Index Ranking

Formula: Growth Index = YoY Growth % × Users (millions)

Top 10 by Growth Index (Sorted)

Key Observations

1. Gemini dominates due to explosive growth from tiny base - distorts the ranking
2. Excluding Gemini, Android, GCP, and Firebase lead with stable massive user bases
3. AI-related products (Gemini, Vertex AI) show highest growth rates
4. Angular declining - losing market share to React
5. Flutter growing faster than most frameworks at 47% YoY
6. TensorFlow losing to PyTorch in research but holding enterprise

Data sources: Statista, Stack Overflow Developer Survey 2024/2025, SimilarWeb, Google Cloud earnings, various industry reports Note: Some figures are estimates based on available data; actual numbers may vary

==

Here's a comprehensive list of Google's developer ecosystem:

Core Cloud & Infrastructure

• Google Cloud Platform (GCP)
• Compute Engine, Cloud Run, Cloud Functions
• Google Kubernetes Engine (GKE)
• Cloud Storage, BigQuery, Cloud SQL, Firestore, Spanner
• Pub/Sub, Cloud Tasks, Cloud Scheduler
• Vertex AI, Cloud TPU, Cloud GPU

Firebase

• Realtime Database, Cloud Firestore
• Firebase Auth, Firebase Hosting
• Cloud Functions for Firebase
• Firebase Analytics, Crashlytics, Performance Monitoring
• Remote Config, A/B Testing
• Cloud Messaging (FCM), Dynamic Links

Android

• Android Studio, Android SDK
• Jetpack libraries (Compose, Navigation, Room, WorkManager, etc.)
• Kotlin (primary language), KSP
• Play Console, Play Services
• Android NDK

Web & Frontend

• Angular
• Lit (web components)
• Material Design, Material Web Components
• Workbox (PWA/service workers)
• AMP (Accelerated Mobile Pages)
• Chrome DevTools, Lighthouse

AI/ML

• TensorFlow, TensorFlow.js, TensorFlow Lite
• JAX
• Keras
• MediaPipe
• Gemini API, PaLM API
• ML Kit

Languages & Runtimes

• Go (Golang)
• Dart
• V8 JavaScript engine

Cross-Platform

• Flutter

APIs & Services

• Google Maps Platform (Maps, Places, Routes, Geocoding)
• YouTube Data API, YouTube Analytics
• Google Ads API
• Gmail API, Calendar API, Drive API
• Google Sheets API, Docs API
• Google Workspace Add-ons
• reCAPTCHA
• Google Pay API
• Google Identity Services (OAuth, Sign-In)

Search & Discovery

• Custom Search JSON API
• Programmable Search Engine
• Google Search Console API

Developer Tools

• Google Cloud CLI (gcloud)
• Cloud Build, Artifact Registry
• Cloud Code (IDE extensions)
• Error Reporting, Cloud Logging, Cloud Trace
• Apigee (API management)

Chrome/Browser

• Chrome Extensions API
• Chrome Web Store
• WebGPU, WebAssembly (collaboration)
• Puppeteer

IoT & Edge

• Google Home/Nest APIs
• Actions on Google (Google Assistant)
• Android Things (deprecated but notable)

Analytics & Ads

• Google Analytics 4 API
• Tag Manager
• Google Ads scripts

Protocols & Standards (Google-led)

• gRPC
• Protocol Buffers (protobuf)
• Bazel (build system)

Gboardを使った**音声の書き起こし（音声入力）**は、かなり手軽で実用的です。スマホ前提で、サクッとまとめますね👇

📱 Gboardで書き起こしする方法（基本）

① Gboardを有効化

• Android：多くの端末で最初から入っています

• iPhone：App Storeで「Gboard」をインストール →
  設定 > 一般 > キーボード > キーボード > 新しいキーボードを追加

② 書き起こししたいアプリを開く

• メモ

• Googleドキュメント

• LINE / Slack / メール
  など、文字入力できる場所ならOK

③ マイクアイコンをタップ🎤

• キーボード右上 or キー内にある 🎤マークをタップ

• 話し始めると、リアルタイムで文字化されます

④ 話し終えたら停止

• 自動で止まる or もう一度🎤をタップ

• そのままテキストとして編集・保存可能

🗣 日本語書き起こしのコツ（精度UP）

• はっきり・少しゆっくり話す

• 句読点は口で言う

  • 「、」→ とうてん

  • 「。」→ まる

• 周囲が静かな場所で使う

• 方言・専門用語は後から手直し前提で👍

⚙ 設定でチェックしておくと良いところ

• 言語：日本語になっているか

• 音声入力：ONになっているか

• オフライン音声入力（Android）：入れておくと通信なしでも使える

📝 こんな用途に便利

• 会議メモ

• 講義・勉強の下書き

• アイデア出し（思考止まらない）

• 長文メールの下書き

もし

• 録音した音声ファイルを後から書き起こしたい

• 会議を丸ごと自動で文字起こししたい

みたいな用途なら、Gboard以外の方法（アプリ・AI）もおすすめできるよ。

どんな使い方か、もう少し教えてもらってもいい？

Here’s a directional sort of the segments I listed earlier, using a simple, defensible heuristic:

• Revenue size bucket (global spend / vendor revenue pool): Mega (>$10B), Large ($1–10B), Mid ($100M–$1B), Emerging (<$100M)

• Recent YoY growth band: Hyper (>100%), Fast (50–100%), Solid (20–50%), Early/Choppy (<20% or too new)

I’m not claiming precise market sizes for each niche (most are privately held + definitions vary). This is a logical estimate anchored to public signals like: Nvidia’s AI data center revenue growth (proxy for compute), application-layer spend estimates, and evidence of multiple vendors hitting $100M+ ARR in specific app categories. (NVIDIA Newsroom)

Tier 1 — Biggest revenue pools and still growing fast

1. AI compute stack (GPUs + data center buildout + GPU cloud)

   • Size: Mega

   • YoY: Fast–Hyper (compute expansion + vendor revenue growth signals)

   • Anchor: Nvidia reported data center revenue up 66% YoY (quarterly). (NVIDIA Newsroom)

2. Application layer: horizontal copilots + vertical copilots + departmental AI (incl. coding assistants)

   • Size: Large–Mega

   • YoY: Fast

   • Anchor: GenAI enterprise spend shows the application layer at ~$19B in 2025 (one estimate). (Menlo Ventures)

3. Customer service AI agents (chat + call center automation/agent assist)

   • Size: Large (already real budgets; clear ROI)

   • YoY: Hyper in many vendors (category “exploded” + multiple $100M+ ARR vendors) (CB Insights)

Tier 2 — Large pools forming quickly (high growth, moving into “standard budget lines”)

1. Inference optimization + serving infrastructure (latency/cost killers)

   • Size: Large

   • YoY: Fast

   • Why: inference costs are now a board-level line item as usage scales (e.g., OpenAI revenue scaling implies huge inference volume). (Reuters)

2. “Agents platform” / orchestration layer (tool-use, routing, workflow automation)

   • Size: Mid → Large

   • YoY: Fast–Hyper (many pilots → production)

   • Anchor signals: enterprise vendors emphasizing agents/workflow automation; partnerships embedding agents in business software. (Reuters)

3. Enterprise search reborn as “answer engines” (knowledge worker productivity)

   • Size: Mid → Large

   • YoY: Fast

   • Why: it’s the first stop for “safe” internal rollout (doc Q&A + citations).

Tier 3 — Solid, durable “picks-and-shovels” (big enough; growth steady)

1. Vector databases / vector search services

   • Size: Mid → Large (depending on what you include)

   • YoY: Solid–Fast

   • Note: analyst estimates vary wildly; treat as direction-only. (SNS Insider)

2. RAG tooling + retrieval pipelines (chunking/indexing/rerank/hybrid search)

   • Size: Mid

   • YoY: Solid–Fast

   • Why: nearly every enterprise build touches RAG, so tooling gets pulled along.

3. LLM observability (tracing, cost, eval regressions, incident debugging)

   • Size: Mid

   • YoY: Fast

   • Anchor (adoption signal): analyses of many production deployments emphasize context engineering/guardrails/discipline (which drives observability demand). (zenml.io)

4. Evaluation-as-a-service (Evals) + synthetic data for evals

• Size: Emerging → Mid

• YoY: Fast

• Why: once teams ship, they need CI-like regression tests.

Tier 4 — Smaller today, very high growth, “new interface” bets

1. Realtime voice agents / multimodal (speech-in, speech-out)

• Size: Mid (but fragmented)

• YoY: Fast–Hyper (voice is becoming a primary interface)

• Anchors: major platform support for realtime + talent/tech acquisitions around voice; vendor claims vary. (Menlo Ventures)

1. Edge/on-device LLM serving

• Size: Emerging → Mid

• YoY: Fast

• Why: privacy + latency + cost pressure push smaller models locally.

Tier 5 — Compliance/security/governance: smaller pools but becoming mandatory

1. AI governance platforms (inventory, approvals, audit trails, policy enforcement)

• Size: Emerging → Mid

• YoY: Fast

• Driver: regulation + procurement requirements.

1. Prompt-injection / agent security tooling

• Size: Emerging

• YoY: Hyper (new attack surface appears as tool-use/agents spread) (zenml.io)

1. ISO/IEC 42001 “AIMS” certification + consulting ecosystem

• Size: Emerging → Mid

• YoY: Solid–Fast (services-led growth; standards-driven) (zenml.io)

Tier 6 — “Nice-to-have” or cyclic segments (still real, but less consistently explosive)

1. Marketing content supply chain (generation + approvals + brand safety)

• Size: Mid

• YoY: Solid (some saturation; lots of competition)

1. Translation/localization at scale (LLM-assisted workflows)

• Size: Mid

• YoY: Solid (strong but more mature)

1. Fine-tuning/distillation service providers

• Size: Mid

• YoY: Choppy (waves: hype → platform improvements → renewed demand)

1. Licensed-data packaging for LLMs (entitlements, audit, “LLM-ready” feeds)

• Size: Emerging → Mid

• YoY: Solid–Fast (enterprise deals take time)

1. Connector standards / tool servers (e.g., MCP ecosystem)

• Size: Emerging

• YoY: Hyper (early)

• Anchor: MCP momentum as a standard for tool/data connections. (zenml.io)

If you want this more quantitative, tell me which definition you care about:

• (A) total end-customer spend (“market size”), or

• (B) vendor revenue pool (ARR), or

• (C) growth in deployments/adoption (logos / usage),

…and I’ll re-rank with explicit % ranges per segment (still estimates, but tighter and more consistent).

Below is a fact-grounded map of “new economy segments” created (or massively expanded) by the LLM boom—i.e., categories where new companies/products and real enterprise spend have shown up since the generative-AI wave.

A quick reality check: the biggest “big bang” shift is enterprises moving from demos → production apps and embedded “agents/copilots” inside existing software. (Menlo Ventures)

1) Compute and infrastructure segments

1. GPU cloud + GPU brokers/marketplaces
   Matching AI workloads with scarce GPU supply (short-term/spot capacity, multi-cloud arbitrage).

2. Inference optimization platforms (faster/cheaper serving)
   Quantization, speculative decoding, batching, caching, compilation, kernel tuning.

3. Model “routers” / multi-model gateways
   Automatically choose the best model per request (cost/latency/quality/risk), plus fallback.

4. Private/on-prem LLM stacks
   For regulated orgs: deploy models behind the firewall (hybrid/on-prem patterns show up in enterprise agent platforms). (The Times of India)

5. Edge/on-device LLM serving
   Smaller models + local inference for privacy/latency and offline use.

6. Realtime multimodal infrastructure (voice-first)
   Low-latency speech-to-speech and multimodal streaming APIs created a whole “voice agent” ecosystem. (OpenAI Platform)

2) Data + “context” economy (RAG, vectors, connectors)

1. Vector databases / vector search services
   Became mainstream because RAG is a default enterprise pattern; many orgs use vector DBs to customize LLM apps. (Databricks)

2. RAG orchestration and tooling vendors
   Retrieval pipelines, chunking/indexing, hybrid search, rerankers, and doc intelligence.

3. Enterprise search re-born as “AI answer engines”
   Workplace search that returns grounded answers + citations across internal sources.

4. Knowledge graph + graph-RAG layers
   For higher-precision retrieval (entities/relations) beyond pure embeddings.

5. Connector standards + “tool servers” (agent integration layer)
   A whole segment around standardizing how models connect to tools and data (vs bespoke integrations). MCP is a key example of this connector layer. (Anthropic)

6. Licensed-data packaging for LLMs
   Data providers selling “LLM-ready” access (contracts, entitlements, audit, policy gates). Example: financial-data access delivered via MCP-style servers. (FN London)

(If you want “market size” claims: there are now dedicated analyst categories like “RAG market,” but treat those forecasts cautiously—useful as a signal, not truth.) (MarketsandMarkets)

3) LLMOps: production engineering, evaluation, reliability

1. LLMOps platforms
   The analog of MLOps for LLM apps: prompt/version management, experiments, deployment controls, rollback.

2. Evaluation-as-a-service (Evals)
   Regression tests for quality, groundedness, refusal behavior, and task success (often continuous / CI-style). (zenml.io)

3. Observability for LLM apps
   Tracing, token/cost accounting, latency breakdowns, tool-call traces, “why did it answer that?”

4. Synthetic data generation + data factories
   Creating training/eval data at scale (with human verification loops).

5. Fine-tuning + distillation service providers
   Not everyone trains models; many pay for domain tuning, adapters, distillation to small models.

6. Prompt / context engineering toolchains
   Systems to build prompts + retrieval templates + guardrails like software artifacts.

4) Safety, security, and compliance segments (now budgeted)

1. Guardrails platforms (input/output/tool/retrieval constraints)
   Policies, filters, redaction, safe tool-use boundaries, “constitutional” checks—plus testing. (zenml.io)

2. Prompt-injection & agent security
   New security category because tool-using agents + connectors create fresh attack surfaces; MCP server issues illustrate the risk class. (TechRadar)

3. AI governance platforms
   Inventory, risk registers, approvals, model cards, audit trails, policy enforcement.

4. Regulatory compliance tooling (EU AI Act, GPAI obligations)
   Tooling/support around transparency, safety/security, copyright processes, documentation. (Digital Strategy)

5. AI management system certification / “AIMS” programs
   ISO/IEC 42001 created a standards-driven consulting + certification ecosystem. (ISO)

6. Copyright/IP risk tooling
   Dataset provenance, output similarity checks, licensed content workflows (especially in media/marketing).

5) “Agents & copilots” as a platform layer

1. Horizontal copilots (workplace productivity)
   Writing, summarizing, meeting notes, research, spreadsheet/docs assistants—sold per seat.

2. Vertical copilots embedded in enterprise software
   CRM/ITSM/ERP/HCM copilots becoming standard features (e.g., AI agents inside service/workflow suites). (The Wall Street Journal)

3. Agent orchestration platforms
   Multi-agent workflows, planning, tool selection, governance, identity/permissions—sold as “agent platforms.” (The Times of India)

4. Interoperability standards for agents
   A new “protocol economy” so agents can talk across vendors (MCP + broader foundation efforts). (Anthropic)

6) High-ROI application segments (where spend concentrates)

1. Customer support automation (chat + voice)
   Deflection, agent assist, after-call work—boosted by realtime voice capabilities. (OpenAI Platform)

2. Sales “autopilot” + outbound personalization
   Lead research, tailored outreach, call notes, CRM hygiene (“steward” agents). (IT Pro)

3. Marketing content supply chain
   Ad variants, localization, brand-safe generation, approvals, asset management.

4. Software engineering agents
   Code generation, refactoring, test creation, PR review, incident response—now a major standalone product category. (WIRED)

5. IT operations agents (ticket triage → remediation)
   Automating runbooks like rebooting systems, resolving common incidents. (The Wall Street Journal)

6. Document intelligence for back office
   Contract analysis, invoices, claims, procurement, compliance docs—LLM + OCR + RAG.

7. Legal & compliance copilots
   E-discovery summarization, clause extraction, policy Q&A with citations.

8. Healthcare admin copilots (esp. documentation)
   Visit notes, coding assistance, patient messaging (high demand, heavy governance).

9. Finance research copilots
   Earnings call summarization, diligence scanning, workflow initiation with licensed data. (FN London)

10. Education tutors and course assistants
    Personal tutoring, content generation, grading support (with safety constraints).

11. Translation + localization at scale
    New workflows: “draft + human QA” across entire content libraries.

12. Creative production tooling
    Script/story assist, ideation, game narrative, asset pipelines (often multimodal).

7) Services and labor-market segments (the “LLM services boom”)

1. AI integration consultancies (RAG/agent delivery teams)
   “We build your copilot” became a standard services line-item.

2. Red-teaming, safety testing, and model risk audits
   New specialized security + compliance services around LLM behavior and agent/tool risks. (Digital Strategy)

3. Training & enablement
   Internal “AI literacy,” prompt/context engineering training, governance operating models.

If you tell me your target buyer (consumer vs enterprise), and what you mean by “new economy” (startup categories, job categories, or spend categories), I can re-rank this list into the top 20 most investable / fastest-growing segments and attach concrete examples for each.

Perfect — here’s the same pain points, now sorted by impact × frequency based on how often they show up across Trustpilot, Hacker News, Reddit, GitHub discussions, Vercel Community, and Medium case studies.
(Top = loudest + most costly complaints; bottom = niche or situational.)

I’ll reference Vercel once and keep it clean.

🔥 Biggest & Most Frequent Vercel User Pain Points (Ranked)

🟥 Tier 1 — Critical, Frequent, Business-Impacting

These show up constantly and often cause teams to reconsider Vercel.

1. Unexpected & Hard-to-Predict Pricing

• Sudden bandwidth / function execution spikes

• Bills jumping 5–10× after launch or traffic growth

• Difficult to estimate costs in advance
  📍 Most common complaint across Trustpilot + HN

2. Poor or Slow Customer Support (Non-Enterprise)

• Tickets unanswered for days or weeks

• Bot replies instead of humans

• Production outages with no timely help
  📍 Extremely frequent on Trustpilot

3. Scaling Pain After Free / Pro Tier

• “Works great until it doesn’t”

• Apps degrade or costs explode at medium scale

• Forced architectural changes later
  📍 Common in Medium + Reddit retrospectives

4. Opaque Billing & Trial Issues

• Charges after cancellation attempts

• Confusing usage metrics

• No clear “cost guardrails”
  📍 High emotional frustration + trust erosion

🟧 Tier 2 — High Pain, Very Common

Serious friction, especially for growing teams.

5. Limited Debugging & Observability

• Hard to access full logs

• Serverless failures hard to reproduce

• Local ≠ production behavior
  📍 Frequent on GitHub discussions

6. Deployment / Build Inconsistencies

• Builds succeed locally but fail on Vercel

• Non-deterministic deploy errors

• Time wasted chasing infra quirks
  📍 Common among Next.js users too

7. Per-Seat Pricing Frustration

• Team growth = immediate cost increase

• Non-technical members still count
  📍 Repeated complaint from startups & agencies

🟨 Tier 3 — Moderate Pain, Recurrent

Not deal-breakers alone, but they add up.

8. Performance Surprises (SSR / Server Actions)

• Slower than expected page loads

• Cold starts
  📍 Seen in Next.js GitHub threads

9. Git Integration Edge-Case Issues

• Manual fixes required

• Sync glitches
  📍 Annoying but not universal

10. Documentation Gaps for New Features

• Especially AI tools (v0)

• Behavior changes poorly explained
  📍 Community forum recurring theme

🟩 Tier 4 — Niche / Emerging / Role-Specific

Loud for some users, invisible for others.

11. V0 AI Credit Burn & Quality Issues

• High cost for failed generations

• Context loss on large projects
  📍 Specific to AI-tool users

12. Backend & Non-Next.js Limitations

• Less flexible than Netlify / custom infra

• Opinionated architecture
  📍 Mostly affects non-React teams

13. Export / Portability Issues

• Hard to migrate off

• Incomplete exports
  📍 Low frequency, high frustration when hit

14. Roadmap Transparency Complaints

• Decisions feel top-down
  📍 Mostly power users / OSS contributors

🧠 TL;DR Heat Map

If you want next:

• 👉 Competitor comparison (Netlify / Cloudflare / AWS Amplify)

• 👉 “What users forgive vs what makes them churn”

• 👉 Founder-level summary (1 slide / 1 page)

Just tell me how you’ll use this.