The Seven Vectors of Convergence: Why On-Device AI Is Inevitable
February 2026
Technology paradigm shifts do not arrive as single breakthroughs. They arrive as convergences -- multiple independent trends, each advancing on its own trajectory, reaching a critical density at the same moment in time. The PC revolution required cheap transistors, graphical interfaces, and spreadsheet software simultaneously. The mobile revolution required capacitive touchscreens, 3G networks, and app distribution simultaneously. Cloud computing required virtualization, broadband ubiquity, and pay-per-use billing simultaneously.
We are now witnessing a convergence of equal magnitude. Seven independent vectors -- in hardware, software optimization, regulation, economics, device proliferation, application architecture, and developer infrastructure -- are aligning toward a single, unavoidable conclusion: the future of AI inference is on-device, and the future of AI improvement is federated.
This paper traces each vector with specificity, projects where each leads, and demonstrates why their intersection creates one of the largest platform opportunities in the history of computing.
Vector 1: Device Compute Capacity Is Reaching Data Center Parity
The most visible trend is the relentless increase in neural processing capability on consumer devices. What is less appreciated is the rate of acceleration.
The Numbers
Apple's Neural Engine trajectory tells the story concisely:
| Chip | Year | NPU Performance |
|---|---|---|
| A11 Bionic | 2017 | 0.6 TOPS |
| A14 Bionic | 2020 | 11 TOPS |
| A17 Pro | 2023 | 35 TOPS |
| A18 Pro | 2024 | 35 TOPS (architectural efficiency gains) |
| M4 | 2024 | 38 TOPS |
| M5 | 2025 | 45 TOPS (+ GPU Neural Accelerators delivering 4x AI compute over M4) 1 |
That is a 75x improvement in eight years on the Neural Engine alone. But the M5 introduced something more significant than raw TOPS growth: dedicated Neural Accelerators embedded in every GPU core, yielding a 4x speedup in time-to-first-token for language model inference compared to the M4. Apple's research team demonstrated running DeepSeek's 670B parameter model locally on an M3 Ultra with 512GB unified memory -- at faster-than-reading-speed generation.2 On-device, not in the cloud.
Qualcomm's trajectory is steeper. The Snapdragon 8 Elite (2024) delivered approximately 45 TOPS. The Snapdragon 8 Elite Gen 5, announced at Qualcomm's Snapdragon Summit in late 2025, reaches 100 TOPS on a TSMC N3P process -- more than double the prior generation in a single year.3 The Hexagon NPU now supports INT2 precision, fused architecture, and GenAI encryption for on-device model security. Benchmarks show NPU acceleration providing up to 100x speedup over CPU execution for supported models.
Samsung's Exynos 2500, built on second-generation 3nm GAA (Gate-All-Around) process technology, delivers 59 TOPS with a 24K MAC NPU -- a 39% improvement over its predecessor.4 Samsung's partnership with Nota AI for on-device model optimization signals the vertical integration of hardware and compression tooling.
Google's approach is architectural rather than benchmark-driven. The Tensor G5 (2025) features a bespoke on-device TPU that is 60% more powerful than the G4, and the upcoming Tensor G6 (expected 2026, built on TSMC 2nm) will introduce a dual-TPU architecture: a full TPU for heavy workloads and a nano-TPU for lightweight, always-on inference tasks.5 This is a design philosophy that assumes AI inference is continuous, not episodic.
The Projection
The trajectory is clear. In 2020, a flagship phone offered roughly 11 TOPS of NPU performance. In 2026, the Snapdragon 8 Elite Gen 5 delivers 100 TOPS. That is a nearly 10x improvement in six years. If the current doubling cadence holds -- and the roadmaps from all four major silicon vendors suggest it will -- flagship phones will exceed 200 TOPS by 2028 and approach 400+ TOPS by 2030.
For context: an NVIDIA V100 data center GPU, the workhorse of AI training circa 2018-2020, delivered 125 TOPS at INT8. We have already surpassed that in a mobile phone. The NVIDIA T4, the standard cloud inference GPU, delivers 130 TOPS at INT8. A 2025 flagship phone matches it. By 2028, the phone in your pocket will have more dedicated AI compute than the GPU in a 2022 cloud inference server.
Why This Matters
Raw compute was the bottleneck that kept AI in the cloud. That bottleneck is dissolving. The question is no longer whether devices can run meaningful models. The question is whether the surrounding infrastructure -- deployment, versioning, monitoring, improvement -- exists to make it practical. It does not. Yet.
Vector 2: Software Optimization Is Multiplying the Hardware Gains
Hardware gains alone do not tell the full story. Optimization frameworks are delivering multiplicative improvements on top of silicon advances, making model classes that were cloud-only two years ago deployable on mobile devices today.
Quantization: Doing More with Less
Quantization -- reducing the numerical precision of model weights and activations -- has matured from a research technique to a production necessity. The progression from FP32 to INT8 to INT4 to mixed-precision schemes has been transformative:
- INT8 quantization reduces model size by 4x versus FP32 with less than 1% accuracy loss on most tasks.
- INT4 quantization reduces model size by 8x versus FP32. A model requiring 32GB in FP32 fits in 4GB at INT4. Google's Gemma 3 (27B parameters), which requires 54GB in BF16, runs in just 14.1GB with INT4 quantization.
- Mixed-precision quantization (e.g., INT4 weights with INT8 activations) intelligently allocates precision where it matters, achieving near-lossless compression. Frameworks like HOBBIT combine INT4 and INT2 precision for Mixture-of-Experts models, loading lower-precision experts on cache misses to reduce latency without significant accuracy degradation.
- Quantization-Aware Training (QAT) now supports FP8, NVFP4, MXFP4, INT8, and INT4 formats, recovering accuracy that naive post-training quantization sacrifices. Qualcomm's Snapdragon 8 Elite Gen 5 natively supports INT2, pushing the compression frontier further.
Inference Engines: The Speed Multipliers
Alibaba's MNN-LLM framework demonstrates what hardware-aware software optimization can achieve.6 On Android (Xiaomi 14, Snapdragon 8 Gen 3), MNN-LLM delivers:
- 8.6x faster prefill than llama.cpp on CPU (4 threads)
- 25.3x faster prefill and 7.1x faster decoding than llama.cpp on GPU (OpenCL)
- 2.8x faster prefill than MLC-LLM
These gains come from hardware-driven data reordering (exploiting ARM i8mm instructions for 2x throughput), multicore workload balancing, DRAM-Flash hybrid storage, and combined quantization strategies. The follow-on work, MNN-AECS, achieves 39-78% energy savings and 12-363% speedup over competing engines.
Apple's MLX framework, purpose-built for Apple silicon's unified memory architecture, showcased at WWDC 2025, leverages Metal 4 Tensor Operations to exploit M5 GPU Neural Accelerators. The result: 4x speedup in time-to-first-token for LLM inference on M5 versus M4, and 3.8x faster image generation with FLUX-dev-4bit (12B parameters).7
Google's AI Edge (the evolution of TensorFlow Lite) now provides seamless delegation to NPU and GPU backends. LiteRT on Qualcomm NPU achieves peak performance through hardware-specific optimization paths.
The Compounding Effect
Here is the critical insight: software optimization is not additive with hardware gains -- it is multiplicative. A 2x hardware improvement combined with a 5x software optimization improvement yields a 10x real-world gain. The combination of 100 TOPS hardware (Snapdragon 8 Elite Gen 5) with MNN-LLM-class optimization means practical on-device inference performance that would have required a dedicated GPU server rack three years ago.
Pruning, knowledge distillation, and neural architecture search are making models simultaneously smaller and more capable. The 2025-era 3B parameter model, properly optimized, matches the accuracy of a 2023-era 7B model at a fraction of the compute cost.
Vector 3: Privacy Regulation Is Making Centralized Data Collection Untenable
While hardware and software make on-device AI possible, regulation is making it necessary.
The Enforcement Escalation
GDPR enforcement has shifted from theoretical to punitive. Aggregate GDPR fines from May 2018 through January 2026 total EUR 7.1 billion (USD 8.4 billion).8 The acceleration is what matters: over 60% of that total -- more than EUR 3.8 billion -- has been imposed since January 2023. The first half of 2025 alone saw over EUR 3 billion in fines, more than any previous full year.9
The largest penalties are instructive:
| Entity | Fine | Year | Violation |
|---|---|---|---|
| Meta | EUR 1.2B | 2023 | US data transfers without adequate protections 10 |
| TikTok | EUR 530M | 2025 | Data transfers to China, transparency violations 11 |
| Google LLC | EUR 200M | 2025 | Non-consensual ad insertion in Gmail 10 |
| SHEIN | EUR 150M | 2025 | Cookie placement without consent 10 |
| Vodafone Germany | EUR 45M | 2025 | Inadequate data protection controls 10 |
The pattern is clear: regulators are fining not just for breaches, but for architectural decisions -- specifically, the decision to move user data to centralized servers for processing. Meta's EUR 1.2 billion fine was not for a data breach. It was for the act of transferring European user data to US servers. The implication for centralized ML training on user data is direct and unambiguous.
The Global Proliferation
Privacy regulation is no longer a European phenomenon:
- United States: Twenty states now have comprehensive privacy laws in effect as of early 2026, up from one (California) in 2020.12 Eight new state laws became enforceable in 2025 alone, with Indiana, Kentucky, and Rhode Island joining in January 2026. Maryland's law imposes data minimization requirements that explicitly limit collection to data "reasonably necessary" for the requested service. No federal preemption is expected under the current administration.
- India: The Digital Personal Data Protection Act (2023) is ramping enforcement, covering the world's largest population of smartphone users.
- Brazil: LGPD enforcement continues to expand in scope and severity.
- EU Health Data Space: New regulations specifically governing health data add compliance complexity for any cloud-based medical ML.
Apple's App Tracking Transparency (ATT), introduced in 2021, demonstrated the market impact: centralized data collection models lost an estimated $10 billion in advertising revenue in the first year alone.13 ATT was not a regulation -- it was a product feature. It previewed what happens when data collection requires affirmative consent.
The Economic Calculus
The compliance cost of cloud-based ML is compounding. Each new jurisdiction, each new data residency requirement, each new consent mechanism adds cost. Legal review of data processing agreements, data protection impact assessments, cross-border transfer mechanisms -- these are not one-time expenses. They are ongoing operational costs that scale with the number of jurisdictions you serve.
On-device processing is inherently compliant. Data that never leaves the device cannot be transferred to a non-compliant jurisdiction. Data that is processed locally does not require a cross-border transfer mechanism. Privacy-preserving ML is not just a technical architecture -- it is a regulatory arbitrage that compounds in value as regulatory complexity increases.
Vector 4: Inference Economics Are Breaking the Cloud Model
The economics of AI are undergoing a structural inversion that makes cloud-based inference unsustainable at scale.
The Great Inversion
The ratio of training to inference spending has flipped:
| Year | Training Share | Inference Share |
|---|---|---|
| 2023 | 67% | 33% |
| 2025 | 50% | 50% |
| 2026 | ~45% | ~55% |
| 2030 (projected) | 20-25% | 75-80% |
Inference now represents over 55% of AI-optimized infrastructure spending in early 2026, surpassing training costs for the first time.14 The AI inference market is projected to grow from $106 billion in 2025 to $255 billion by 2030 at a 19.2% CAGR.15
The OpenAI Case Study
OpenAI's financials illustrate the structural problem. Internal Microsoft financial documents reveal that OpenAI spent $8.7 billion on inference compute through Azure in the first nine months of 2025 -- nearly double its revenue for the same period.16 CEO Sam Altman publicly acknowledged that the company loses money on $200/month ChatGPT Pro subscriptions. The inference cost alone consumed more than OpenAI earned.
This is not a startup scaling problem. This is a structural economic constraint of the cloud inference model. Inference is 80-90% of the lifetime cost of a production AI system because it runs continuously. A $1 billion training cost becomes $15-20 billion in inference costs over the model's lifetime.
The Jevons Paradox of AI
Per-token inference costs have dropped dramatically -- from $20 per million tokens in late 2022 to roughly $0.07 in 2025, nearly a 280x reduction.17 Yet total inference spending has surged. AI cloud infrastructure spending hit $37.5 billion in 2026, a 105% increase from $18.3 billion in 2025. Hyperscaler capital expenditure reached $600 billion in 2026, with 75% (~$450 billion) tied directly to AI infrastructure.18
This is the Jevons Paradox at work: efficiency gains drive adoption, which drives total consumption beyond the efficiency savings. Cheaper inference means more inference. More inference means higher total cost. The only way to break this linear cost curve is to move inference off the cloud entirely.
The On-Device Arbitrage
On-device inference has a fundamentally different cost structure. The marginal cost of an additional inference call on a device the user already owns is effectively zero. The compute is already purchased, the power is already consumed, and the silicon sits idle most of the time. An NPU running at 100 TOPS uses a fraction of the device's power budget.
For a company running 1 billion inference calls per day in the cloud at $0.001 per call, that is $1 million per day -- $365 million per year. On-device, the same workload costs nothing incremental. The economics are not incrementally better. They are categorically different.
Vector 5: Device Heterogeneity Demands a Unifying Platform
A less obvious but equally important vector is the proliferation of AI-capable hardware across an increasingly diverse set of device types. This heterogeneity is not a problem to solve -- it is a market to serve.
The Fragmentation
AI-capable silicon is no longer confined to smartphones and data centers. It has spread to:
- Smartphones: Apple Neural Engine, Qualcomm Hexagon NPU, Samsung Exynos NPU, Google TPU, MediaTek APU
- Laptops/PCs: Apple M-series, Qualcomm Snapdragon X2 (80 TOPS NPU), Intel Core Ultra NPU, AMD Ryzen AI
- Automobiles: NVIDIA DRIVE Orin (254 TOPS) and Thor (1,000 TOPS),19 Mobileye EyeQ, Horizon Journey 5
- Wearables: ARM Ethos-U NPU (optimized for microcontroller-class devices)
- Smart home/IoT: Google Edge TPU, Nordic Semiconductor nRF with AI accelerators
- AR/VR headsets: Meta Quest NPU, Apple Vision Pro Neural Engine
- Industrial edge: NVIDIA Jetson AGX Thor (2,070 FP4 TFLOPS), Intel Movidius
Each device type has fundamentally different constraints: compute budget (from 1 TOPS on a wearable to 1,000 TOPS in a vehicle), memory (from 256KB to 128GB), power envelope (from milliwatts to hundreds of watts), supported frameworks (CoreML, TFLite, ONNX Runtime, TensorRT), and model format requirements.
The Scale
The numbers are staggering. There are over 5.5 billion smartphones in active use. Connected IoT devices reached 21.1 billion in 2025 and are projected to exceed 25 billion in early 2026, heading toward 40 billion before 2030.20 By 2026, edge computing AI chip shipments will reach 1.6 billion units. Seventy percent of IoT edge devices manufactured in 2025 now ship with AI processing capabilities from Intel and Qualcomm.
The edge AI market was valued at $25-36 billion in 2025 and is projected to reach $100-386 billion by the early 2030s, depending on scope definition.21 Ninety-seven percent of CIOs in the United States have included edge AI in their 2025-2026 technology roadmaps.
Why Heterogeneity Creates Platform Opportunity
This fragmentation makes DIY on-device ML increasingly untenable. An organization targeting smartphones alone must optimize for at least four different NPU architectures (Apple, Qualcomm, Samsung, Google). Add automotive, wearables, and IoT, and the optimization surface expands to dozens of hardware targets, each with different quantization support, memory hierarchies, and runtime APIs.
ONNX serves as a common interchange format, but interchange is not deployment. Converting a model to ONNX does not automatically optimize it for a Hexagon NPU versus a CoreML backend versus an Edge TPU. That requires platform-level intelligence -- the kind of cross-device abstraction that no hardware vendor has incentive to build (because each wants lock-in) and no individual company can economically build for themselves.
The pattern is identical to cloud infrastructure circa 2008. Raw compute existed (EC2, bare metal). What was missing was the developer platform -- the Heroku, the Vercel -- that abstracted the complexity and let developers focus on their application rather than the infrastructure. The more heterogeneous the hardware landscape becomes, the more valuable the unifying platform layer becomes.
Vector 6: Agentic AI Exponentially Multiplies Inference Demand
The application architecture of AI is shifting in a way that makes the inference cost problem dramatically worse -- and the on-device solution dramatically more valuable.
From Request-Response to Agentic Loops
Traditional AI interactions follow a simple pattern: one user request, one model call, one response. Agentic AI -- autonomous systems that plan, execute, observe, and iterate -- fundamentally changes this equation. A single user action can trigger:
- Planning: The agent reasons about how to accomplish the task (1-5 inference calls)
- Tool use: The agent invokes external tools and APIs (2-10 calls)
- Observation: The agent processes tool outputs (1-5 calls per tool)
- Reflection: The agent evaluates whether the result meets the objective (1-3 calls)
- Retry/refinement: The agent loops if the result is insufficient (multiplied by 2-5x)
A Barclays research report estimated that agentic "super agents" generate 25x more tokens than a basic chatbot interaction.22 Benchmark data from MCPMark shows complex agentic tasks averaging 16.2 execution turns per task.23 Academic research documents "dozens of inference calls to satisfy a single user request," with production agentic workflows requiring "dozens or hundreds" of calls per task.
The Compounding Architectures
The multiplier effect is not limited to agents:
- RAG pipelines: Retrieval + re-ranking + generation = 3-5x calls per query
- Chain-of-thought / tree-of-thought: Multiple reasoning passes per request
- Multi-modal pipelines: Vision + language + audio processing = compounding inference
- Always-on AI features: Continuous inference for smart cameras, voice assistants, health monitoring, predictive text -- these are not request-response patterns but ambient, ongoing computation
Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025.24 The agentic AI market is projected to reach $8.5 billion in 2026 and $35-45 billion by 2030.25
The Economic Cliff
The math is unforgiving. If agentic workflows multiply inference calls by 10-25x per user session, and you are paying per-token cloud pricing, your AI costs scale 10-25x. For a company running AI features for millions of users, this is the difference between a viable product and an economic impossibility.
On-device, the cost multiplier is 1x regardless of how many inference calls the agent makes. The NPU is already there. The power is already consumed. Whether your on-device agent makes 1 call or 100 calls per user interaction, the incremental infrastructure cost is zero.
This is not a minor efficiency gain. It is a structural economic advantage that becomes more valuable as AI applications become more sophisticated. Every advance in agentic AI architecture -- every additional reasoning step, every new tool integration, every reflection loop -- widens the gap between cloud economics and on-device economics.
Token consumption is growing approximately 10x per year while effective token costs are falling approximately 50% per year. That combination does not just enable more AI usage; it demands a different infrastructure topology. As one infrastructure analysis noted: "routing trillions of inference calls through a handful of centralized regions quickly runs into the limits of physics, networking, and economics. In 2026, that pressure will push inference workloads outward -- into edge networks, on-prem environments, and on-device."
Vector 7: The Missing Layer -- Why the Platform Matters More Than Ever
The six vectors above establish the inevitability of on-device AI. Hardware can run it. Software can optimize it. Regulation demands it. Economics favor it. Devices are everywhere. Application architectures require it.
But there is a gap. A large one.
What Exists Today
Hardware vendors have built runtimes:
- Apple built CoreML
- Google built AI Edge (TFLite/LiteRT)
- Qualcomm built the AI Engine SDK
- ONNX Runtime handles cross-platform inference
These are execution engines. They can load a model and run inference. They are necessary infrastructure. They are not sufficient infrastructure.
What Does Not Exist
No runtime answers these questions:
- Model versioning: Which version of the model is running on which device? Can I roll back to the previous version if the new one degrades performance?
- A/B testing: Is model v2.3 actually better than v2.2 across my device fleet? What about on older hardware versus newer hardware?
- Deployment orchestration: How do I push a model update to 10 million devices without overwhelming my network infrastructure? How do I handle devices that are offline?
- Observability: What is the inference latency distribution across my device fleet? Where is the model failing? Is there drift?
- Continuous improvement: How do I improve the model using signals from device-level inference without collecting user data?
- Cross-platform consistency: How do I ensure the same model behaves equivalently on CoreML, TFLite, and ONNX Runtime?
- Compliance reporting: Can I demonstrate to a regulator that user data never left the device?
These are not research problems. These are production infrastructure problems. Every company deploying AI to devices must eventually solve all of them, and the solutions are non-trivial, cross-cutting, and have nothing to do with the company's core product.
The Historical Pattern
This pattern has repeated in every computing paradigm:
- Raw infrastructure emerges (EC2, bare metal servers)
- Runtimes standardize (Linux containers, JVM)
- The developer platform captures the value (Heroku, AWS Lambda, Vercel)
For on-device AI:
- Raw infrastructure exists (NPUs, Neural Engines, TPUs)
- Runtimes are standardizing (CoreML, TFLite, ONNX Runtime)
- The developer platform does not yet exist
Whoever builds that platform -- the unified layer that handles model deployment, versioning, A/B testing, observability, federated improvement, and cross-platform abstraction -- will own the developer relationship for on-device AI. Just as Stripe captured the payment layer by making it simple, just as Twilio captured the communications layer by making it programmable, the platform that makes on-device AI as simple as a pip install and a five-line integration will capture the on-device AI layer.
The science of federated learning is proven. Google demonstrated it at scale with Gboard, training models across hundreds of millions of devices. Flower built an open-source framework with 35+ aggregation strategies. The academic literature is extensive and validated. What is missing is not the science. What is missing is the product -- the developer experience that takes proven federated learning and makes it accessible to every enterprise with a mobile app or edge deployment.
The Convergence
Each vector is powerful on its own. Their convergence is what makes this moment singular.
Hardware provides the compute. Optimization frameworks multiply it. Regulation mandates on-device processing. Inference economics demand it. Device heterogeneity creates the need for abstraction. Agentic AI makes the cost advantage exponential. And the platform that ties it all together -- that is the opportunity.
Timeline: What We Expect to See
2026: The Tipping Point
- Flagship phones surpass 100 TOPS (Snapdragon 8 Elite Gen 5 already there)
- Inference spending exceeds training spending for the first time in cloud infrastructure budgets
- 20+ US states have comprehensive privacy laws in effect
- 40% of enterprise applications begin incorporating AI agents (Gartner)
- On-device LLMs (3-7B parameters, quantized) become standard features in flagship smartphones
- Edge AI market exceeds $30 billion
2027: The Migration
- Mid-range smartphones reach 50+ TOPS NPU performance
- Enterprises begin migrating latency-sensitive and privacy-sensitive inference workloads from cloud to device at scale
- Federated learning moves from research to production deployments at companies handling health, financial, and personal data
- ONNX and cross-platform model formats become critical infrastructure
- The first major "inference cost crisis" forces a prominent AI company to restructure its pricing model
2028: The New Default
- Flagship phones exceed 200 TOPS -- surpassing cloud inference GPUs from 2023
- On-device becomes the default deployment target for consumer AI features
- Regulatory enforcement makes cloud-based training on personal data prohibitively risky in healthcare, finance, and consumer applications
- Agentic AI workflows running entirely on-device become commercially viable
- The edge AI market crosses $75 billion
- Cross-platform model deployment becomes as routine as cross-platform app deployment
2030: The Paradigm
- Flagship phones approach 400+ TOPS, sufficient for real-time inference of 7B+ parameter models without quantization
- 75-80% of AI compute spending goes to inference; on-device inference handles the majority of consumer-facing workloads
- Global connected IoT devices approach 40 billion, the majority AI-capable
- Federated learning is the standard methodology for model improvement in privacy-regulated industries
- The on-device AI platform layer is as essential as the cloud provider layer is today
- Centralized inference becomes what mainframe computing became: still present, still necessary for certain workloads, but no longer the default assumption
What the Smart Money Should Do About It
The evidence across all seven vectors points to a single conclusion: on-device AI inference is not an alternative to cloud inference. It is the successor to cloud inference for the majority of consumer and enterprise AI workloads. The transition will not happen overnight, but it is happening now, and it will accelerate.
The companies that will capture disproportionate value in this transition are not the hardware vendors (who will compete on silicon), nor the runtime providers (who are commoditizing), but the platform builders -- the companies that build the developer infrastructure that makes on-device AI as simple as cloud AI is today.
That platform must solve model deployment, versioning, and rollback across heterogeneous devices. It must enable A/B testing and observability at fleet scale. It must provide federated learning for continuous model improvement without data collection. It must abstract the complexity of CoreML, TFLite, ONNX Runtime, and whatever comes next behind a simple, unified API. And it must do all of this with a developer experience that feels like five lines of code, not five hundred.
This is exactly what Octomil is building.
We are not building another research framework. The science is proven. We are not building another runtime. The runtimes exist. We are building the developer platform for on-device AI -- the layer that makes deploying, monitoring, testing, and improving models on billions of edge devices as simple as uploading a file to the cloud.
The seven vectors of convergence are not predictions. They are measurements of trends already in motion. The window to build the platform that serves them is open now. It will not stay open indefinitely.
Octomil is building the developer platform for federated learning and on-device AI. For more information, visit octomil.com.