The Future of On‑Device Assistants: Apple, Google, and the Opportunity for Third‑Party Plugins

Hook: Why on-device Siri, powered by Gemini, matters to your job—and your product roadmap

Recruiters and founders keep asking the same thing in 2026: "Can you build the future assistant stack that runs locally, respects privacy, and still integrates third-party tools?" For engineers and product leaders, that question is a career-defining moment. For toolmakers, it's a potential gold rush: a new assistant ecosystem where Apple’s Siri (now powered by Google’s Gemini in parts) moves from siloed convenience to a plugin-first platform that runs much of its intelligence on-device.

In this piece I’ll explain the technical and commercial landscape you need to know, show specific product and hiring plays, provide salary benchmarks for the roles that will power this transition, and map practical steps for building plugins, APIs, and business models tuned for an on-device-centric assistant era.

Executive summary — the opportunity in one paragraph

Apple’s partnership with Google to incorporate Gemini signals a tactical blend of cloud scale and on-device privacy-first computing. Combine that with rising on-device hardware capabilities (Apple Silicon Neural Engines, the mainstreaming of accessible devices like Raspberry Pi 5 with AI HATs) and you get a plausible path to a plugin ecosystem for Siri. That ecosystem would require new APIs, sandboxed plugin runtimes, distribution models, and commercial models from subscriptions to enterprise licensing—creating immediate demand for mobile/edge ML engineers, platform SDK developers, privacy engineers, and product managers. Salaries and contractor rates for these specialties are rising accordingly in 2025–26.

Why 2026 is the inflection point for on-device assistant ecosystems

1) Strategic partnerships changed the game

Apple’s move to leverage Google’s Gemini for Siri (announced in early 2026) is more than a headline: it shows Apple’s willingness to combine external large-model capabilities with its long-standing emphasis on device privacy and energy efficiency. That hybrid approach creates architectural room for plugins that can run local inference for latency-sensitive tasks and call cloud models for rare or heavy computations.

2) Hardware democratization and developer tooling

On-device compute is no longer niche. Single-board computers like Raspberry Pi 5 plus AI HAT expansions—and new ARM SoCs in laptops and phones—mean developers can prototype production-grade, edge-optimized models affordably. That accessibility shortens the path from experimental plugin to shipped SDK.

3) Regulatory and privacy pressure

Global privacy and AI regulations (EU AI Act, ongoing data-protection updates) plus consumer demand for local processing favors solutions that minimize cloud data flow. Apple’s brand advantage on privacy makes an on-device-first plugin model commercially defensible and attractive to enterprises that need compliance guarantees.

4) Market signals: money follows platform control

Platform owners and marketplace operators will monetize plugin discovery and distribution, creating recurring revenue streams for toolmakers and platform fees. Expect new monetization primitives—per-query licensing, per-device subscriptions, and enterprise SLA agreements—beyond the traditional one-time SDK license.

Key takeaway: The confluence of Gemini-powered intelligence, hardware acceleration, and privacy expectations makes 2026 the earliest year an on-device plugin ecosystem is commercially realistic and attractive to both startups and incumbent vendors.

What an on-device Siri plugin ecosystem likely looks like

Core components

• Sandboxed plugin runtime: A secure process model that limits data access and enforces entitlements (similar to modern app sandboxing but more granular for assistant intents).
• Edge-optimized model formats: Support for Core ML (Apple), ONNX Runtime Mobile, TF Lite, and quantized formats to run efficiently on Neural Engines and NPUs.
• Hybrid model orchestration: Policies and APIs for deciding when to run on-device vs. cloud (e.g., privacy, latency, battery, accuracy heuristics).
• Plugin discovery and trust: A marketplace or App Store-integrated directory with signatures, provenance, and enterprise attestation.
• Billing & entitlements: Built-in microbilling, enterprise SSO/metering, and per-device licensing controls.

Developer-facing APIs and primitives

• Intent contracts: Strongly typed interfaces describing inputs, outputs, and constraints for assistant actions.
• Data minimization hooks: Callbacks that let plugins request specific data and justify it to a permission manager.
• Stateful session APIs: Local encrypted storage for long-term personalization, with user-managed keys in Secure Enclave.
• Fallback orchestration: Easy declaration of cloud fallbacks, with policy-controlled telemetry for model updates and failure modes.

Business models for third‑party toolmakers

Toolmakers who go early will need flexible monetization plans. Here are the realistic, near-term models that will work in an Apple/Google/edge world:

• Marketplace revenue share: Simple, store-like model—works for consumer and SMB plugins but pressure on pricing.
• Subscription + freemium: Basic on-device functionality free, premium cloud-enhanced features paid (common for productivity plugins).
• Per-device/per-seat licensing: For enterprises that need control and compliance. Offers predictable ARR and aligns with device management.
• Usage-based API billing: For hybrid plugins that call cloud endpoints when needed—metered per query or compute unit.
• Consulting & integration premiums: Tooling that needs enterprise configuration, custom intents, or private model fine-tuning can charge professional services.

Technical roadmap for building a successful on-device plugin

Below is a practical, step-by-step developer playbook. Aim for an incrementally deployable product that proves value locally before adding cloud dependencies.

Phase 0: Research & constraints

• Identify target devices and neural-capability range (A-series phones, M-series laptops, mid-tier ARM devices).
• Define worst-case memory and latency budgets. On phones, aim for 50–200ms inference for interactive flows.
• Pick model formats: Core ML for iOS/macOS, ONNX or TF Lite for cross-platform proof-of-concepts.

Phase 1: Edge-first MVP

• Ship a minimal on-device model that handles the common 70–80% of use cases offline (e.g., intent detection, entity extraction).
• Use quantization (int8 or smaller) and pruning to reduce model size; use hardware-backed acceleration APIs.
• Design permissions to request only necessary data and display clear UX around data use.

Phase 2: Hybrid features & trust

• Add cloud-only features behind opt-in (document summarization, multimodal retrieval) with transparent fallbacks.
• Implement attestation and signature verification for plugin updates and model bundles.
• Build analytics with privacy-preserving aggregates and differential privacy when sending data back to improve models.

Phase 3: Distribution & monetization

• Integrate with the platform’s marketplace (or offer enterprise distribution through MDM and private listings).
• Implement metering for cloud calls and an in-app/subscription flow compatible with platform policies.
• Offer SDKs and sample intents so other developers can extend or embed your plugin’s capabilities.

Security, privacy, and compliance: must-have features

Design decisions will be judged by regulators and security teams. Prioritize these:

• Data ephemeralization: Avoid indefinite local storage unless user explicitly chooses to keep personal profiles.
• Secure Enclave / TPM integration: Use hardware-backed key stores for personalization tokens and authentication.
• Attestation and signed model bundles: Prevent tampering through signature chains and version pinning.
• Fine-grained permission prompts: Users should approve data scopes at intent level, not just global toggles.
• Differential privacy & federated updates: Ship model improvements without centralizing raw user data.

Hiring and salary benchmarks (2025–2026 market snapshot)

Demand has tightened for engineers who can bridge ML model engineering, mobile SDKs, and platform security. Below are market ranges for U.S. remote roles and freelance rates—adjust for geography and company size. These figures reflect mid-2025 to early-2026 hiring data from job boards, salary surveys, and market placements in the remote tech hiring market.

Full-time roles (U.S., remote-friendly)

• Edge / Mobile ML Engineer: $140k–$220k base. Skills: model quantization, Core ML, ONNX, TF Lite, latency profiling.
• SDK & Platform Engineer (iOS/macOS focus): $130k–$200k base. Skills: Swift, Core ML integration, entitlements, background tasks.
• Privacy & Security Engineer (Edge/AI): $150k–$240k base. Skills: Secure Enclave, attestation, threat modeling.
• Platform Product Manager (Assistant/Plugins): $150k–$260k total comp. Skills: API design, marketplace economics, platform governance.
• ML Infrastructure / Ops Engineer (Hybrid): $140k–$230k base. Skills: model deployment lifecycle, federated learning infrastructure.

Contract / freelance rates

• Senior Edge ML Consultant: $120–$250/hr
• iOS SDK Contractor: $80–$180/hr
• Product/Strategy Advisor (platforms): $150–$400/hr

Note: Enterprises pay premiums for candidates with proven experience integrating with platform-level entitlements, App Store governance, or private MDM distribution—expect a 10–30% uplift for demonstrable platform expertise.

How to position yourself as a hireable expert in this niche

Whether you’re a job-seeker or founder hiring for the stack, these are the highest-ROI actions in 2026:

1. Ship a lightweight, open-source on-device demo: an assistant plugin prototype that performs a useful task offline. Publish benchmarks (latency, memory) and CI that runs on a Raspberry Pi or iPhone simulator.
2. Document integration with Core ML/ONNX and show hybrid fallback patterns—employ a simple policy engine and publish code samples.
3. Highlight security patterns on your resume: Secure Enclave usage, signed model bundles, and differential privacy pipelines.
4. Contribute to or publish a small library for intent contracts, or an SDK for permission flows. Community traction is persuasive to hiring managers.
5. Negotiate with market data: ask for 10–20% above baseline if you bring platform-signature experience or shipped plugins in a marketplace.

GT-Market strategies for toolmakers and startups

Building a product is one thing; getting it into users’ hands is another. Here’s a GTM checklist tailored for the Siri+Gemini/on-device era.

• Start with enterprise pilots: Enterprises care about privacy and device control. Pilot through MDM and private listings to prove integration and security before going consumer.
• Offer both on-device and cloud tiers: The freemium model works—on-device for day-to-day tasks, cloud for heavy-duty or collaborative features.
• Partner with device OEMs and accessory makers: Companies shipping edge hardware (like AI HATs) will look for software partners. Co-marketing can accelerate adoption.
• Build for discoverability: If Apple exposes a Siri plugin marketplace, invest in metadata, sample dialogs, and usage demos to rank in discovery flows.
• Provide enterprise SLAs: For B2B plugins, provide per-device licensing, analytics dashboards, and compliance documentation.

Risks and countermeasures

No opportunity is without friction. Here are the main risks and how to mitigate them:

• Platform gatekeeping: Apple controls distribution and entitlements. Mitigate by designing features that can exist as standalone apps or via enterprise channels while you gain platform trust.
• Model maintenance costs: Keeping accurate on-device models requires updates. Use differential updates, delta shipping, and model compression to reduce bandwidth and cost.
• Battery & UX impacts: On-device inference can drain battery. Prioritize energy-efficient models and let users opt into high-energy features.
• Regulatory changes: Stay current with AI and privacy regulation; provide auditable processes and whitepapers for enterprise buyers.

What to watch in 2026

• Apple's official APIs for Siri plugins—watch for timeline and entitlements announced at major events (WWDC-style updates).
• Gemini iterations and any new on-device licensing terms from Google after its expanded role with Apple.
• New hardware announcements (Apple Silicon, mid-tier ARM NPUs) that change on-device performance baselines.
• Marketplace rules and revenue-share models—these will determine monetization viability.
• Regulatory rulings affecting AI marketplaces and data portability—these could expand enterprise demand for on-device solutions.

Real-world example: a plugin product blueprint

Imagine "MeetingNotes+", a plugin that captures and summarizes meeting highlights locally, then optionally enriches them with cloud-based action-item extraction. A practical implementation path:

1. Edge model for voice activity detection and local transcription snippets (quantized Core ML).
2. Local summarization for 80% of short meetings—fast, private, and offline.
3. Cloud-enhanced extraction for multi-user, cross-account intelligence (opt-in, metered).
4. Enterprise distribution with per-seat licensing and MDM integration.
5. Compliance pack (data retention rules, audit logs, signed model bundles) for larger customers.

Final thoughts — why you should care and act now

We’re at the start of a platform cycle. Apple’s Gemini relationship accelerated realistic engineering paths to combine cloud-scale models with on-device guarantees. For engineers, product leaders, and indie toolmakers, that creates an immediate demand signal: build lightweight, secure, local-first experiences that can scale into hybrid offerings. For job-seekers, this is an opportunity to specialize in a high-value intersection—mobile ML, platform SDKs, and privacy engineering—where compensation and demand are both increasing in 2026.

Actionable next steps

• If you’re building: prototype a local-first plugin and publish benchmarks; target enterprise pilots for trust.
• If you’re hiring: prioritize candidates with cross-cutting skills (Core ML + entitlements + performance tuning) and budget for the 10–30% platform-expertise premium.
• If you’re job-seeking: build a small open-source assistant plugin; include measured latency/memory numbers on your resume.

On-device assistants aren’t a futuristic dream—they’re a near-term market shift. The winners will be the teams that combine thoughtful privacy engineering with practical product economics and a clear route to distribution.

Call to action

Ready to build or hire for the on-device assistant era? Start by shipping a 1–2 week prototype that proves your core inference works offline. If you want templates, job-ready hiring briefs, or salary benchmarking tailored to your hiring region or stage, reach out—our platform matches employers with vetted remote candidates who know how to build these exact systems.

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