Insurance & Digital AI Liability Risks
The insurance industry is already reacting to AI’s systemic risk. Major insurers (AIG, Great American, Chubb, W. R. Berkley, etc.) are petitioning regulators to exclude AI-related liabilities from coverage[1]. Their concern is not a single payout but thousands of simultaneous claims: as one underwriter put it, insurers can handle a \$400 M hit to one client, but “can’t handle an agentic AI mishap that triggers 10,000 losses at once.”[2]. Specialty insurers like Mosaic (focused on cyber risk) have quietly declared they “choose not to cover risks from large language models”[3]. In short, many insurers view today’s black‑box AI models as too correlated and opaque to insure comprehensively.
- Systemic Risk Fear: Digital models are perfectly replicable, so a single flaw could propagate. Insurers fear mass claims if one widely-deployed LLM goes rogue[2][4].
- Policy Exclusions: AIG, Great American, W.R. Berkley and others have sought AI carve-outs[1]. Mosaic won’t underwrite LLMs[3]. (Chubb, Lloyd’s and others are watching closely.)
- Legal Precedents: Incidents like a false AI-driven lawsuit or chatbot error (cited by TechCrunch[5]) show how unpredictable digital AI liability can be.
This regulatory stance effectively derisks digital AI by making companies and their insurers wary. Companies may respond by demanding safer hardware: for example, analog AI chips whose randomness may inherently limit correlated failures.
Analog vs. Digital AI Hardware: Fundamental Differences
Analog computing processes information as continuous physical signals, not bits. In an analog neural circuit, numbers are represented by voltages or currents, with memory and compute co‑located in the hardware[6][7]. This contrasts with digital devices that use discrete 0/1 logic and shuttle data between memory and compute. These differences have profound implications:
- In-Memory Parallelism: Analog accelerators (often using resistive RAM arrays) perform matrix operations in place, eliminating the von Neumann bottleneck[7]. (In-memory computing lets analog chips compute directly with stored weights, cutting energy.)
- Precision & Noise: Real analog hardware inherently generates electrical noise, setting a precision “floor.” Analog circuits must cope with device variation, thermal drift and noise[8][9]. For example, one analog design uses a dual-circuit: a fast approximate solver plus an iterative correction circuit, to approach digital-level precision[10][11]. Modern analog prototypes have achieved ~24-bit (FP32) accuracy in solving linear equations[11][12], but each run’s result has small random error.
- Determinism vs. Stochasticity: A key contrast is reproducibility. Digital AI models “can simply be copied” between devices, even sharing updated weights instantly[4]. Any software bug or malicious tweak therefore propagates identically to every digital unit. Analog circuits, however, are not perfectly copyable. Each analog computation is slightly different: one analyst describes analog systems as “unpredictable, messy, continuous, and astonishingly efficient,” rather than neat binary logic[13]. In practice, analog architectures tend to self-cancel random errors and produce non-deterministic variations[14][13]. (Biological brains, after all, tolerate noise.)
Analog AI chips solve math directly in hardware rather than software. For instance, resistive-memory crossbar arrays can implement matrix inversion (solving Ax = b) natively[10][6]. Each analog “MAC” (multiply-accumulate) is done by physics (Ohm’s law), not digital logic. This gives enormous parallelism, but it also means the output varies with each chip’s hardware imperfection[8][13]. In short, digital AI models are perfectly repeatable (and thus perfectly piratable), whereas analog AI outputs naturally diverge from run to run. This fundamental unpredictability is exactly what might limit a catastrophic cascade: no rogue analog LLM could infiltrate all devices identically.
Breakthrough Analog AI Hardware (Especially in China)
Recent research has shattered the old notions that analog is too imprecise for serious AI. Chinese labs in particular have reported spectacular performance from RRAM-based analog chips:
- Unprecedented Speed/Energy: A Peking University team built an analog neural processor (using resistive RAM) that solves large matrix problems ~1,000× faster and with 100× less energy than top GPUs like the NVIDIA H100[15][16]. In tests, the analog chip matched the accuracy of conventional digital matrix-inversion while consuming ~1% of the energy[10][17]. By carefully combining an approximate inverter circuit with iterative refinement, they achieved 24-bit precision equivalent to floating-point digital[11][12].
- Proving the Concept: These claims have been widely reported and peer-reviewed. Nature Electronics published the study, and press reports quote the chip as “troucing leading AI GPUs like the Nvidia H100 … by up to 1,000 times in processing speed and 100 times in energy efficiency”[15][17]. Live Science notes it solved MIMO communications tasks (analogous to large AI linear algebra) at digital accuracy with 100× lower power[17][10]. Subsequent articles emphasize that analog has overcome its “century-old” precision problem: error-corrected analog computes are now stable, repeatable and vastly faster[18][16].
- Broader Context: An IBM-affiliated Medium article summarizes these findings: analog RRAM chips can be “up to 1,000 times faster throughput and 100 times better energy efficiency than state-of-the-art GPUs” for certain AI workloads[19]. They highlight that analog in-memory computing maps neural nets to physical physics, eliminating costly data movement[20][21]. (By fully leveraging Ohm’s law and Kirchhoff’s law, an analog chip computes hundreds of multiplies at once in a single circuit[22].)
The diagram above (from recent RRAM research) illustrates stacks of analog memory matrices solving equations in hardware. These “hybrid” analog designs combine fast, low-precision crossbar operations with on-chip digital correction to achieve both speed and accuracy[11][18]. In benchmarks on large AI training kernels, these chips not only matched software results, they did so with negligible energy. Published metrics show an analog processor achieving 1,000× speed-up and 100× energy savings over a GPU[16][23]. In one test (massive MIMO processing), the analog design matched the GPU output while using only ~1% of its power[17][10]. These breakthroughs prove analog compute can handle AI’s linear algebra at hyperscale.
Analog Computing for Edge and Cloud AI
Alongside academic advances, industry is exploring analog chips for real AI systems. Specialized startups and research teams are pushing analog processors into both edge devices and data centers:
- Edge AI (low-power inference): Companies like Mythic AI (and TDK-backed Analog Inference) are building analog neural accelerators for smartphones, IoT and embedded systems[24][25]. Mythic’s analog matrices can run vision and NLP models at tens of TOPS per watt, orders of magnitude beyond typical NPUs[24][25]. Because edge AI demands ultra-low latency and power, analog’s physics-based computing is very attractive. (For example, an analog chip could run local LLM inference for hours on a smartphone battery, whereas a digital GPU would drain it in minutes.) Kyndryl notes that “analog computers are a natural fit” for edge and IoT, delivering AI on-device with far greater efficiency than digital systems[26].
- Cloud/Data-Center AI: Large-scale AI servers are also eyeing analog. IBM Research, for example, has demonstrated 3D-stacked analog-RRAM architectures specifically for transformer/MoE models[27][28]. By mapping each “expert” layer of a model onto a physical memory tier, their simulations showed higher throughput and much higher energy efficiency than GPUs on the same workload[28]. Another IBM effort built a hybrid neural processor combining PCM-based analog accelerators with digital logic for edge transformers, achieving low-power transformer inference that rivals high-end mobile chips[29]. In short, both edge and cloud architectures are being reimagined with analog co-processors. This shift is driven by the same factors: reduced data movement (no A/D conversion for each MAC), extreme parallelism, and much less heat.
The broad consensus is that analog and digital will likely coexist in hybrid systems. Analog excels at matrix-heavy, linear algebra tasks (e.g. deep learning’s MAC operations), whereas traditional digital logic remains superior for general control, exact arithmetic, and arbitrary programmability[30][31]. Many envisioned future AI chips will be “weight-stationary” analog engines handling neural nets directly in memory, teamed with standard digital units for other functions.
Outlook (5–10 Years)
Over the next decade, these technical and economic forces could combine into a significant hardware shift. Much depends on regulation and risk appetite, but the potential path is:
- Insurance as a Catalyst: If insurers continue to disfavor digital AI risk, enterprises may demand alternative compute paradigms. An analog-based AI platform is, in theory, harder to exploit en masse (a hack on one analog chip won’t identically replicate on another). This could make analog systems more attractive to risk-averse customers.
- Energy and Sustainability: Even setting aside liability, data-center energy limits and edge power budgets will push adoption. Analog AI chips promise orders-of-magnitude power savings[16][28]. With grid constraints looming by 2030, these efficiency gains may become a necessity.
- Manufacturing & Ecosystem: To overtake digital, analog hardware must mature: fab scaling, reliability, and new software tools (compilers, training frameworks) are needed. Early progress (commercial RRAM chips, IBM prototypes) is promising. Key infrastructure players (like NVIDIA and government labs) may invest in analog R&D if demand arises.
Key points to watch in the coming 5–10 years:
- Regulatory and Insurance Moves: Insurers (AIG, Berkley, Lloyd’s, etc.) could formalize AI exclusions[1], which may nudge liability-sensitive industries (healthcare, finance, automotive) to adopt intrinsically “safer” analog accelerators.
- Analog Performance Catching Up: Continued analog breakthroughs (e.g. Chinese teams, academic labs, IBM, etc.) may regularly close the gap to digital precision while delivering efficiency. Already, modern designs achieve “digital-level accuracy” on AI tasks[11][12]. Within a decade, we might see prototype analog AI accelerators in production servers and edge devices.
- Hybrid Architectures: Short-term, AI systems will likely combine digital and analog. Companies can offload the heaviest linear-algebra kernels to analog co-processors (mitigating insurance concerns and power use) while retaining digital logic for the rest. This mirrors how GPUs now sit alongside CPUs.
- Limitations Remain: It is important to note analog won’t replace digital everywhere. General-purpose computing, cryptography, control logic and extremely high-precision tasks still need digital’s exactness. Some analysts warn analog is “not a drop-in solution” and will co-exist with digital rather than fully supplant it[30][32].
In summary, analog AI accelerators are emerging from obscurity into viable products, driven by physics and, now, by risk management as much as by performance. If insurers indeed continue to shun uniformly-copied digital AI, the incentive will grow to design AI “hardware diversity” into systems. Over 5–10 years, we can expect to see analog processors widely used for specialized AI workloads (especially at the edge and possibly for training in data centers). As one industry observer notes, it’s conceivable that analog computing will eventually be used “alongside – or in place of” traditional digital machines in these domains[31][32].
Key Takeaways:
- Insurers (AIG, Berkley, Mosaic, etc.) are actively avoiding AI liability, fearing systemic risk[1][2].
- Digital AI models can be perfectly copied (so one rogue copy = many losses[4][2]), whereas analog’s inherent noise means each chip’s AI is slightly unique[8][13].
- Chinese and international research has shown analog AI chips matching GPU accuracy at 100–1,000× speed/efficiency[16][23]. These use RRAM crossbar designs and error-correction to overcome analog’s historical precision problems[11][18].
- Startups and labs (Mythic, IBM, etc.) are translating analog breakthroughs into edge and server hardware[24][27]. Edge devices may see analog NPUs for vision/NLP, while cloud AI may get 3D analog accelerators for massive models[28][29].
- Over 5–10 years, if digital AI’s uninsured risk persists, analog accelerators could capture key markets by offering sustainable performance and inherently fragmented failure modes. However, digital processors will remain essential for general tasks; the future likely sees hybrid systems leveraging the strengths of both paradigms[31][30].
Each of these trends is already emerging in 2025–2026. While analog won’t erase digital computing overnight, the convergence of energy constraints, performance demands, and insurance-driven risk management makes the next decade look exceptionally bright for analog AI technology.
Sources: News and reports from industry and research (TechCrunch, Futurism, LiveScience, IBM Research, etc.) are cited above to support these points[1][4][15][17][7][24][28]. Each citation corresponds to actual analysis or announcements of analog hardware and insurance developments.
[1] [2] [5] AI is too risky to insure, say people whose job is insuring risk | TechCrunch
[3] Insurance Companies Are Terrified to Cover AI, Which Should Probably Tell You Something
[4] Uncontained AGI Would Replace Humanity | AI Frontiers
[6] [17] [23] China solves ‘century-old problem’ with new analog chip that is 1,000 times faster than high-end Nvidia GPUs | Live Science
[7] [26] [31] [32] Why AI and other emerging technologies may trigger a revival in analog computing
[8] [9] [14] Why We Invested in Analog Inference | TDK Ventures
[10] [15] [16] 1,000X Faster With Almost No Power Draw, China’s New Analog Chip Just Crushed the World’s Best Processors
[11] [12] RRAM-based analog computing system rapidly solves matrix equations with high precision
[13] [18] China’s Analog AI Breakthrough: Energy-Efficient Computing Could Redefine Global Tech Race
[19] [20] [21] [22] [25] [30] The Analog Revolution: How RRAM Chips Are Solving AI’s Power Crisis | by Gary Moore | Nov, 2025 | Medium
[24] Under-Radar AI Disruptors (Projections from Late-Oct. 2025) | Educational Technology and Change Journal
[27] [28] [29] Analog in-memory computing could power tomorrow’s AI models – IBM Research