The AI era is rapidly bringing change to all industries and occupations. The double exponentials at work in scaling AI model capabilities is in stark contrast to current rate of adoption at scale.
AI Adoption Divide
OpenAI paper GDPval evaluates frontier model performance on top 9 sectors contributing to U.S. GDP, with at least 30 tasks per occupation in the full set, across 44 occupations. The red line in the following chart (50% win rate vs industry professional mark) is parity with human industry experts. As on end of 2025, the frontier models are nearing parity. Every next generation of frontier models increase the win rate by around 10% every 3 months of release cycle. By this trend most frontier models will meet and exceed parity with human experts by mid-2026. The trillion-dollar question - is the world ready for this change?
GDPval frontier model performance across industries
Chart Source: GDPval leaderboard
On the other hand MIT published report on The GenAI Divide State of AI in Business 2025:
Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach.
The report further concludes that the core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time.
From the reported interviews, surveys, and analysis of 300 public implementations, four patterns emerged that define the GenAI Divide:
- Limited disruption: Only 2 of 8 major sectors show meaningful structural change
- Enterprise paradox: Big firms lead in pilot volume but lag in scale-up
- Investment bias: Budgets favor visible, top-line functions over high-ROI back office
- Implementation advantage: External partnerships see twice the success rate of internal builds
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Our Mission
This is why at NeoSignal we are on a mission to accelerate frontier technology diffusion.
In the AI era what is happening to software development is the leading indicator for transformative change across industries. The year 2025 was the year of code generation as one of the most advanced capabilities of frontier AI models like Anthropic Opus 4.5 and OpenAI GPT 5.2. The year 2026 will be the year of agentic capabilities. Both Anthropic and OpenAI are building their agentic tooling on top of code generation. Claude Code from Anthropic and Codex from OpenAI. The realization from both frontier labs is simple. If agents can code then they can generalize to solve any task.
Now code generation has hit a significant milestone as confirmed by two leading AI engineers.
Boris Cherny, lead engineer of Claude Code at Anthropic made this revelation in his X post on Dec 27, 2025.
In the last thirty days, 100% of my contributions to Claude Code were written by Claude Code. I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5. Claude consistently runs for minutes, hours, and days at a time... Software engineering is changing, and we are entering a new period in coding history.
Just a day earlier, Andrej Karpathy (previously Director of AI @ Tesla, founding team @ OpenAI, CS231n/PhD @ Stanford) posted this on his X feed:
I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.
At NeoSignal we believe AI engineering and code generation is the tip of the spear in driving frontier technology diffusion. We believe in starting here and expanding out to other important domains over time.
Productive Agentic AI
At NeoSignal we are also living this exponential change. NeoSignal represents a mature, production-ready codebase built on modern web technologies. The project demonstrates good code quality with an overall score of 72/100, reflecting strong engineering practices across security, reliability, and maintainability dimensions. NeoSignal app development, data management pipeline, and knowledge management operations are 100% executed using a well-crafted specifications-driven multi-agent system.
This is what 10x productivity feels like. The current development effort spans approximately 50 hours across 10 active development days, with a high commit velocity of 275 commits in the past week. The codebase comprises 193,421 lines of code organized across 11 modules, demonstrating a well-structured architecture.
From a product perspective, the platform manages 82 stack components across 5 categories, supported by 48 market signals and 29 knowledge documents. The UI layer consists of 98 React components distributed across 11 functional modules.
The technology stack leverages Next.js, React, TypeScript, Tailwind CSS, Supabase as the foundation, with 42 dependencies providing comprehensive functionality for state management, animations, payments, and testing. Test coverage includes 29 unit test files and 10 E2E test files, ensuring reliability across the application.
Our learning is benefiting NeoSignal customers via curated knowledge graph of signals, conversational intelligence, and leaderboard of agentic systems.
Scaling AI Training
The economics of training large language models have undergone a fundamental shift. DeepMind's Chinchilla paper established that compute-optimal training requires balancing model size with training tokens—doubling parameters should mean doubling data. Yet the industry quickly learned this creates what researchers call the "Chinchilla Trap": compute-optimal models are often too large and expensive for production inference.
Meta's response with Llama demonstrates the counter-strategy. Llama 3's 8B model trained on 15 trillion tokens—a ratio of 1,875 tokens per parameter, far exceeding Chinchilla's recommendations. The result: smaller models that punch above their weight class while remaining deployable on accessible hardware. This overtraining approach now dominates the industry as organizations prioritize inference economics alongside training efficiency.
A recent Nature Machine Intelligence paper introduces the "Densing Law"—capability per parameter doubles approximately every 3.5 months. Equivalent model performance is achievable with exponentially fewer parameters over time. The implication for practitioners: the optimal training configuration from six months ago is already obsolete.
The memory demands of training compound these decisions. Training LLaMA-7B requires 112GB of memory for model states alone—exceeding a single 80GB A100's capacity. Scaling to larger models without distributed strategies is impossible. The ZeRO optimization, implemented in DeepSpeed and PyTorch FSDP, addresses this through progressive sharding: ZeRO-1 distributes optimizer states, ZeRO-2 adds gradient sharding, and ZeRO-3 extends to parameters themselves. The technique enables trillion-parameter training on existing hardware by eliminating redundant memory allocations across GPUs.
However, ZeRO introduces communication overhead. AMSP research shows that collective communication for model state management creates substantial transmission costs at scale. Hybrid sharding strategies—parameter sharding within nodes, replication across nodes—offer trade-offs between memory savings and inter-node communication latency.
NeoSignal translates this complexity into actionable tools for practitioners navigating training infrastructure decisions.
The Memory Calculator estimates GPU memory requirements before you commit to hardware. Input your model parameters, batch size, sequence length, precision format, and parallelism strategy. The tool computes memory breakdown across parameters, gradients, optimizer states, and activations—then applies ZeRO stages, activation checkpointing, and CPU offloading configurations to show effective memory per GPU. Output includes whether your configuration fits on target hardware, specific GPU recommendations from NeoSignal's component database, and the memory-compute trade-offs at each optimization level.
The Parallelism Advisor recommends optimal tensor, pipeline, and data parallelism configurations for your specific model and hardware. Input your architecture details, GPU type, and available GPU count. The advisor calculates memory distribution across parallelism dimensions, generates ready-to-use DeepSpeed ZeRO and PyTorch FSDP configuration snippets, and estimates communication overhead for each strategy. The tool draws on NeoSignal's accelerator compatibility data—knowing which GPUs support which precision formats, which interconnects enable efficient collective operations, and which configurations avoid the common pitfalls that surface only in production.
Training decisions compound. The model architecture chosen today determines inference costs for years. The parallelism strategy affects training time and debugging complexity. NeoSignal surfaces these trade-offs before infrastructure commitments are made, not after.
Optimizing AI Inference
If training is the frontier of capability, inference is the frontier of economics. According to the Stanford AI Index 2025, the inference cost for a system performing at GPT-3.5 level dropped over 280-fold between November 2022 and October 2024. Yet OpenAI's 2024 inference spend reached $2.3 billion—15 times its GPT-4 training cost. The AI Inference Market is projected to grow from $106 billion in 2025 to $255 billion by 2030.
Inference now consumes 80-90% of all AI computing power. By 2026, analysts estimate inference demand will outpace training by 118x. This complete reversal—from training-dominated to inference-dominated resource allocation—reshapes every infrastructure decision.
The cost compression is accelerating. In early 2025, DeepSeek's R1 demonstrated 20-50x cheaper inference than comparable OpenAI models. Tasks costing $50 per million tokens on GPT-4 run at $1-2 on DeepSeek. This price pressure cascades through the ecosystem—API providers cut prices, organizations recalculate build-versus-buy decisions, and the threshold for self-hosting shifts.
Serving engine selection determines realized inference economics. Benchmarks show dramatic variance across engines. vLLM achieves 14-24x higher throughput than base Hugging Face Transformers through PagedAttention—treating KV cache memory like virtual memory pages for efficient reuse. TensorRT-LLM on H100 with FP8 reaches over 10,000 output tokens per second at peak throughput, with approximately 100ms time-to-first-token. But TensorRT-LLM requires model-specific engine builds, explicit compilation steps, and weeks of expert tuning for optimal results.
The trade-offs are configuration-specific. LMDeploy delivers up to 1.8x higher request throughput than vLLM through persistent batching and optimized kernels. TGI v3 processes 3x more tokens than vLLM on long prompts with prefix caching enabled. Hardware matters equally: Cerebras Inference delivers 1,800 tokens per second for Llama3.1 8B—outperforming GPU-based solutions by 20x with claimed 100x better price-performance. Google TPUs now deliver 4x better performance-per-dollar for inference workloads; Midjourney reportedly slashed inference costs 65% by switching to TPUs.
Quantization compounds these gains. INT8, FP8, GPTQ, AWQ, GGUF—each method trades model quality for memory and compute savings. Studies show well-executed quantization preserves 97% of original performance while halving memory requirements. 74% of organizations planned to use model distillation in 2024 to create compact, production-ready models.
NeoSignal makes these infrastructure decisions tractable through tools grounded in real-world deployment data.
The Serving Engine Advisor recommends inference engines based on your latency and throughput requirements. Input your model, target latency, target throughput, and GPU configuration. The advisor compares vLLM, TensorRT-LLM, SGLang, and llama.cpp against your specific requirements—predicting performance based on NeoSignal's benchmark data and real-world deployment patterns. Output includes predicted performance metrics, batching configuration, deployment manifests, and autoscaling guidance. The tool surfaces which engines require compilation steps, which support your target quantization methods, and which achieve acceptable cold-start latency for your use case.
The Quantization Advisor recommends methods based on your deployment target and quality tolerance. Input model, hardware (GPU, CPU, or edge), quality priority, and serving engine. The advisor scores quantization methods against your requirements—INT8 for broad compatibility, FP8 for modern NVIDIA hardware, GPTQ and AWQ for aggressive compression, GGUF for CPU deployment. Output includes memory savings estimates, quality impact predictions based on published benchmarks, and configuration snippets for your target serving engine.
The TCO Calculator closes the loop on inference economics. Input your monthly request volume, average token counts, and model size. The calculator computes monthly costs across Anthropic, OpenAI, and self-hosted options—then generates break-even analysis showing when self-hosting becomes cost-effective. The tool draws on NeoSignal's pricing data across cloud providers and API services, updated as the market evolves.
Inference optimization is where the GenAI Divide compounds most severely. Organizations that master this layer extract millions in value. Those that don't remain stuck with costs that don't scale. NeoSignal exists to compress the learning curve from months to minutes—putting the infrastructure decisions that define AI economics within reach of every practitioner.
Navigating the Accelerator Landscape
The silicon layer beneath AI workloads is undergoing its own exponential transformation. NVIDIA dominates with approximately 80% market share, but the landscape is fragmenting in ways that create both risk and opportunity for practitioners.
The current generation battle pits NVIDIA's H100/H200 against AMD's MI300X. According to SemiAnalysis benchmarks, for most training workloads, H100/H200 beats MI300X by more than 2.5x in effective TFLOP/s. The gap isn't just hardware—it's the CUDA ecosystem built over 20 years with 4+ million developers and 3,000+ optimized applications. AMD's MI300X offers 192GB of HBM3 memory (versus H100's 80GB) and 1.31 petaflops at FP16 (versus 989.5 teraflops), but software immaturity means real-world performance often lags specifications.
However, the inference picture differs. For massive models like Llama3 405B and DeepSeek V3 670B, MI300X's memory advantage becomes decisive. A single MI300X node with 1,536GB HBM capacity can fit models that require multiple H100 nodes. Organizations increasingly follow a pattern: train on NVIDIA, infer on AMD—utilizing NVIDIA's mature training ecosystem then deploying to AMD hardware where memory bandwidth and capacity advantages overcome software optimization gaps.
The next generation reshapes these trade-offs entirely. NVIDIA's Blackwell B200, shipping in 2025, promises 192GB memory, 8TB/s bandwidth, and claimed 4x training performance with 30x energy efficiency relative to H100. AMD's MI325X and upcoming MI350 series target this same window with 256GB HBM3e and 6TB/s bandwidth. Meanwhile, specialized inference chips—Cerebras, Groq, and Google TPUs—achieve 20-100x better price-performance on specific workloads by abandoning general-purpose architectures.
NeoSignal tracks this accelerator landscape through real-time component data and compatibility scoring.
The Component Browser enables advanced filtering and side-by-side comparison across NeoSignal's accelerator database. Filter by memory capacity, TFLOPS, architecture generation, power efficiency, and availability status. Compare up to four accelerators simultaneously with metrics matrices showing specifications, compatibility with target models and frameworks, and current market pricing across cloud providers. Each accelerator card links to signals—new benchmark data, availability changes, price movements—connecting static specifications to dynamic market intelligence.
The Stack Builder extends this to compatibility validation. Select a target model and the builder shows which accelerators support it efficiently—accounting for memory requirements, precision formats, and framework compatibility. Select an accelerator and see which models are optimized for its architecture. The compatibility matrix surfaces pairwise relationships before you sign cloud contracts or place hardware orders.
Accelerator decisions lock in for years. The GPU cluster ordered today will run workloads through 2027. NeoSignal's accelerator tracking ensures those decisions are informed by current data—specifications, benchmarks, availability, pricing—not six-month-old blog posts.
Mastering the Framework Ecosystem
The software layer orchestrating AI workloads has bifurcated into distinct domains. PyTorch now dominates model training with 63% adoption according to the Linux Foundation, while LangChain leads LLM application orchestration with the application development segment growing at 70%+ CAGR.
PyTorch's dominance reflects ecosystem effects compounding over years. The framework has contributions from 3,500+ individuals and 3,000+ organizations. Its stable API and backward compatibility make it the default choice for teams building long-term ML infrastructure. TensorFlow remains relevant but has ceded leadership in research and increasingly in production.
The application layer tells a different story. LangChain emerged from 2022 obscurity to become a cornerstone for operationalizing generative AI. Companies like Dropbox and Zapier build on LangChain as their primary AI framework. The 2024 State of AI Agents report shows 90% of respondents in non-tech companies have or are planning to put agents in production—driving demand for orchestration frameworks that connect models to retrieval systems, tools, and multi-step workflows.
The framework landscape is stabilizing. From 2023 to 2024, all-in-one frameworks like LangChain dominated with pioneering task orchestration and rich tool integrations. By late 2024, fewer new frameworks entered the ecosystem as the market consolidated around proven options. The notable shift: from predominantly retrieval workflows to multi-step, agentic architectures. Framework choice now determines not just what you can build, but how quickly you can adapt as agent capabilities mature.
Serving frameworks represent the third critical layer. vLLM, TensorRT-LLM, SGLang, llama.cpp—each targets different deployment scenarios with distinct trade-offs. vLLM's PagedAttention innovation enabled breakthrough throughput; TensorRT-LLM extracts maximum performance from NVIDIA hardware at the cost of deployment complexity. The right choice depends on hardware, model, latency requirements, and operational capacity.
NeoSignal tracks frameworks through the same scoring methodology applied to models and accelerators.
Every framework gets a Stack Card—the same standardized view used for models and accelerators. Framework cards display GitHub stars, weekly downloads, community size, and release velocity alongside dimensional scoring for stability, documentation, ecosystem integration, and performance characteristics. The compatibility layer shows which frameworks support which models, which accelerators, and which serving backends—surfacing integration paths before you commit to architectural decisions.
The AI chat grounds framework recommendations in NeoSignal's curated knowledge base. Ask which framework supports your target model on your target hardware and the response draws on current compatibility data, benchmark results, and community signal. Ask about migration paths between frameworks and the chat references documented breaking changes, deprecation timelines, and community experiences.
Framework selection compounds across the stack. The training framework determines model compatibility. The serving framework determines inference economics. The orchestration framework determines agent capabilities. NeoSignal surfaces these interconnections through compatibility scoring and knowledge-grounded intelligence.
Optimizing Cloud Infrastructure
The cloud layer providing GPU access has fragmented beyond the hyperscalers. AWS, GCP, and Azure remain dominant, but specialty GPU cloud providers—CoreWeave, Lambda Labs, RunPod—have grown 1,000%+ year-over-year by offering better GPU availability, clearer pricing, and purpose-built AI infrastructure.
CoreWeave exemplifies the neocloud model. From 3 data centers in 2023 to 28 by end of 2024, the company hit an estimated $3.52B in revenue by mid-2025. Leading AI labs—OpenAI, Mistral AI, IBM—build on CoreWeave's infrastructure. The platform offers NVIDIA's latest GPUs including GB200 instances, Kubernetes-native orchestration, and InfiniBand networking optimized for distributed training. Clear public pricing and strong availability contrast with hyperscaler GPU waitlists.
Lambda Labs targets smaller organizations with lower price points. H100 PCIe GPUs at approximately $2.49/hour compared to CoreWeave's $4.25/hour. However, Lambda doesn't offer the more powerful HGX H100 configurations suited for large-scale training. The company raised $1.5B in 2024-2025, positioning as a "Superintelligence Cloud" for AI-native development.
The economics shift depending on usage patterns. For training runs requiring hundreds or thousands of GPUs with InfiniBand interconnects, neoclouds offer purpose-built infrastructure that hyperscalers struggle to match. For inference at scale, spot instances can reduce costs 60-80% with appropriate checkpointing strategies. For burst compute, the difference between "available now" and "3-month waitlist" determines whether you ship on time.
Cloud choice interacts with hardware choice. AMD GPUs remain scarce on neoclouds, leading to elevated rental rates despite competitive hardware performance. Google Cloud offers TPU access that no other provider can match. Multi-cloud strategies are increasingly necessary for organizations optimizing across training, inference, and burst compute requirements.
NeoSignal translates cloud complexity into actionable intelligence through tools and real-time signals.
The Spot Instance Advisor analyzes spot pricing across regions and cloud providers for your GPU requirements. Input workload type, interruption tolerance, and target hardware. The advisor estimates savings versus on-demand pricing—typically 60-80% for fault-tolerant workloads—while recommending checkpointing strategies appropriate to each cloud's interruption patterns. Output includes expected interruption rates, recommended fallback mechanisms, and risk assessment for your specific configuration.
The TCO Calculator extends to cloud comparison. Input your training and inference requirements—GPU hours, memory needs, network bandwidth. The calculator computes costs across hyperscalers and neoclouds, factoring pricing tiers, committed use discounts, and current availability. The break-even analysis shows when reserved capacity becomes cost-effective versus on-demand, and when self-hosted infrastructure beats cloud rental entirely.
The Signals feed captures cloud infrastructure movements in real-time. GPU availability changes—when H100s become available without waitlist on a specific provider. Price adjustments as providers compete. New region launches expanding geographic options. Partnership announcements like exclusive GPU supply agreements. Each signal carries confidence scoring and links to affected components—connecting market intelligence to infrastructure decisions.
Cloud infrastructure is where agility meets economics. The organization that secures GPU capacity at the right time, at the right price, on the right provider captures compounding advantages. NeoSignal surfaces the signals that matter for these decisions, grounded in current market data rather than outdated documentation.
Bridging the Divide
The GenAI Divide persists because frontier technology diffusion requires mastering interconnected complexity—models, accelerators, frameworks, cloud, agents—each evolving at AI speed. The 5% of organizations extracting millions in value have developed internal capabilities to navigate this landscape. The 95% stuck with no measurable impact lack the time, resources, or information to keep pace.
NeoSignal exists to bridge this divide. Not by simplifying the complexity—it is genuinely complex—but by making the current state of that complexity accessible. Real-time component data. Compatibility scoring across the stack. Tools that compute memory, parallelism, serving, and cost trade-offs. Signals that surface market movements before they appear in blog posts. Chat grounded in curated knowledge rather than training data frozen in time.
The frontier keeps moving. AI capabilities double every few months. Hardware generations ship annually. Framework ecosystems evolve weekly. Cloud pricing changes daily. Organizations that compress the time between signal and action capture disproportionate returns. Those that fall behind face compounding disadvantages.
NeoSignal moves at AI speed. The infrastructure landscape changes, and NeoSignal changes with it—updating component scores, generating signals, refreshing knowledge. The goal is simple: ensure every practitioner can make infrastructure decisions informed by today's data, not yesterday's assumptions.
Accelerate frontier technology diffusion. That's the mission. That's why NeoSignal exists.