The 2026 Tech Pulse: Open Models, Solid‑State EVs, and the Rise of Bespoke Biotech

Introduction: The Three Frontiers Moving Fastest

Technology cycles are compressing. In the span of a few years, we have watched AI platforms leap from curiosity to critical infrastructure, electric vehicles move from early adoption to mass‑market competition, and biotech jump from lab‑bound breakthroughs to real clinical impact. These three frontiers—AI models and providers, automotive electrification, and gene‑editing biotech—are evolving in parallel, and the overlaps between them are starting to matter. AI is reshaping materials science and drug discovery. EVs are becoming software‑defined machines that increasingly rely on model‑driven control systems. Gene editing is adopting the same platform mindset that made cloud software scalable.

This article is a field guide to what is trending right now in non‑political technology. We focus on the practical shifts: open‑weight AI models that are changing procurement decisions, solid‑state batteries that are finally stepping out of the lab, and gene‑editing frameworks that are making bespoke therapies more realistic. The goal is not hype but clarity—what is real today, what has credible evidence behind it, and what is likely to become headline‑worthy in the next 12–24 months.

AI Models and Providers: The Platform Layer Is Fracturing

The AI model landscape is no longer a single‑vendor story. Enterprises now treat models as a supply chain: some are proprietary and high‑performance; others are open‑weight and customizable; and a growing number are domain‑specific. That diversity is the story of 2026.

1) Open‑weight models are moving into production workloads

A key shift is the increased credibility of open‑weight models. MIT Technology Review’s “What’s next for AI in 2026” points to the rapid ascent of open models, including Chinese releases such as DeepSeek R1 and the Qwen family, and highlights their growing usage outside China, including among Silicon Valley startups. Open‑weight models allow fine‑tuning, distillation, and on‑prem deployment—advantages that matter for cost, control, and data governance.

Open‑weight models are not a single category; they come in “families” with different sizes and capabilities. Qwen, for example, spans a wide range of sizes and includes specialized versions for math, coding, vision, and instruction following. This breadth is a practical advantage: teams can pick a model size that aligns with latency budgets and hardware constraints rather than being locked into a single “frontier” option. When budgets tighten or privacy requirements intensify, these models become the default.

Why it matters: the move toward open models changes procurement logic. Instead of buying a single premium model, organizations can assemble a “portfolio” of specialized models and swap them in and out. This pattern resembles the early cloud era when teams shifted from a single enterprise stack to a more modular approach using open‑source frameworks.

Source: MIT Technology Review, “What’s next for AI in 2026”.

2) Reasoning‑centric models and “world models” define the frontier

Another major trend is the rise of reasoning‑centric models and so‑called “world models.” Reasoning models—built to handle multi‑step problem‑solving rather than just surface‑level generation—are becoming the standard for high‑stakes tasks like scientific analysis, code reliability, and operations planning. Meanwhile, world models, which simulate environments and can generate realistic scenes, are opening new possibilities in robotics, simulation, and synthetic data generation. MIT Technology Review highlights this trajectory and cites examples like DeepMind’s Genie family and other platforms advancing this direction.

These advances are not simply about model size. They are about training strategies, data curricula, and the orchestration of multi‑model pipelines. Many providers are building “mixture of experts” systems where specialized sub‑models are orchestrated for better performance and lower cost. This creates a practical expectation that the winning products will be those with robust tooling and monitoring rather than those with only the most impressive single‑model benchmark scores.

Why it matters: the next generation of AI products will be less about single‑prompt creativity and more about multi‑step workflows that combine reasoning, tool usage, retrieval, and human oversight. The winners will be providers with strong developer tooling, auditability, and clear operational guarantees.

Source: MIT Technology Review, “What’s next for AI in 2026”.

3) Model platforms are becoming a layered market

Large providers still dominate headline performance, but the market is fragmenting into three layers:

• Frontier proprietary models: used when top‑tier reasoning or multimodal performance is required, especially in competitive product experiences or high‑stakes analysis.

• Open‑weight models: adopted when customization, lower cost, and control matter more than raw benchmark performance.

• Edge and specialized models: optimized for specific devices, verticals, or workloads—think customer support, embedded robotics, or real‑time inference on constrained hardware.

This layered market puts pressure on pricing models. We are seeing more “usage‑based” pricing at the top, but also fixed‑cost models for enterprise deployments and on‑prem licensing. The tension between these approaches is shaping how organizations plan for AI spend in 2026.

4) AI for science is graduating from novelty to pipeline

AI for scientific discovery has moved from isolated demos to larger, programmatic initiatives. MIT Technology Review notes that major labs are building dedicated AI‑for‑science groups, a sign that the field is being treated as a core platform rather than an experimental branch. This matters because the constraints in science—data quality, explainability, and experimental validation—are stricter than in consumer generative AI. The teams that can meet those constraints are likely to become the trusted providers in biotech and materials research.

Expect more model‑driven lab workflows in 2026: protein design pipelines, materials discovery, and automated hypothesis generation. The effect is cumulative: each successful model shortens the R&D cycle, which feeds better data back into the next model. The labs that can connect the AI layer to wet‑lab validation will create an advantage that is hard to replicate.

Source: MIT Technology Review, “What’s next for AI in 2026”.

Automotive Tech: Solid‑State Batteries and the Software‑Defined Car

Electric vehicles are now a mainstream product category, but the next wave is about more than just range. It is about charging time, material sustainability, and the ability to turn vehicles into software platforms. The single most watched hardware shift is the march toward solid‑state batteries.

1) Solid‑state batteries are edging toward commercialization

Solid‑state batteries promise higher energy density, faster charging, and improved safety compared to traditional lithium‑ion. Toyota has repeatedly positioned itself as a leader in this transition. A 2025 update reported by Electrek notes Toyota’s stated goal of bringing its first solid‑state battery EV to market within the next few years, with executives reinforcing timelines that point to late‑decade commercialization. The same report highlights Toyota’s claims of sharply improved range and charging performance and points to new supply‑chain partnerships supporting production.

These batteries are not a near‑term replacement for today’s lithium‑ion packs. Instead, they are likely to appear first in premium models and limited production runs. The emphasis is on proving manufacturing scale and real‑world durability. If those milestones are met, solid‑state could become the differentiating feature in the premium EV segment by the late 2020s.

What to watch: pilot‑line yields, charging‑cycle durability, and partnerships with materials suppliers. Progress in these areas is more predictive than marketing timelines.

Source: Electrek, “Toyota’s solid‑state EV battery dreams might actually come true”.

2) The supply chain is as important as the chemistry

Solid‑state batteries require different materials—such as solid electrolytes—and a manufacturing approach that can deliver consistent quality at scale. The Electrek report notes Toyota’s partnerships to secure materials supply and emphasizes a domestic manufacturing push. These supply‑chain investments are not just logistical; they define the cost curve. Without scale, solid‑state batteries remain too expensive for mass adoption.

The broader EV market is watching how quickly companies can build repeatable manufacturing processes. This is similar to what happened with lithium‑ion a decade ago: early breakthroughs were less important than the ability to produce reliably at a price that could compete. The difference now is that the industry is racing to build a supply chain that can support two chemistries at once—today’s lithium‑ion and tomorrow’s solid‑state.

Source: Electrek, “Toyota’s solid‑state EV battery dreams might actually come true”.

3) Software‑defined vehicles are the next competitive edge

While batteries dominate headlines, the silent shift is software. Vehicles are increasingly managed like “rolling computers,” with centralized architectures, over‑the‑air updates, and AI‑assisted driver features. This allows automakers to treat features as software releases rather than hardware revisions. The result is a new competitive axis: user experience and continuous improvement.

As vehicles become software‑defined, the boundary between automotive and tech companies blurs. Expect more partnerships between automakers and AI providers, particularly around in‑cabin intelligence, predictive maintenance, and energy‑optimization systems. The next generation of EVs will compete not just on range but on the quality of their software ecosystems.

4) Charging, grid integration, and energy management

Another trend is the role of EVs as grid resources. Vehicle‑to‑grid (V2G) concepts—where vehicles can discharge power back to the grid—are gaining attention as renewable energy adoption grows. Solid‑state batteries could make this more viable by enabling higher cycle life and faster charging. But V2G adoption depends on policy and infrastructure, so the near‑term focus is on smarter charging and demand‑response features.

In 2026, watch for utility partnerships and pilot programs that integrate EV fleets into grid management. While not as flashy as new battery chemistries, these programs can create meaningful cost savings for fleet operators and provide grid stability benefits.

Biotech: CRISPR, Personalized Therapies, and the Platform Shift

Biotech has reached a stage where gene editing is no longer a future concept but an emerging clinical reality. The most important trend is not just the tools themselves, but the regulatory and business frameworks that allow those tools to scale.

1) CRISPR therapies are moving from breakthrough to pipeline

The Innovative Genomics Institute’s 2025 clinical trials update notes a pivotal milestone: the first approvals of CRISPR‑based medicines, such as Casgevy for sickle cell disease and beta thalassemia. These approvals represent the transition from experimental to commercialized gene editing. The update also notes growing clinical sites and early results in additional therapeutic areas, such as liver‑targeted editing and cardiovascular disease indications.

This shift matters because it proves that complex gene‑editing therapies can be delivered safely at scale. It also puts pressure on reimbursement systems and health‑care payers, given the high upfront cost of these treatments. The key trend is a move toward outcome‑based pricing and broader reimbursement agreements that can make such therapies financially viable.

Source: Innovative Genomics Institute, “CRISPR Clinical Trials: A 2025 Update”.

2) Personalized, “N‑of‑1” therapies are becoming realistic

Another significant development is the rise of bespoke therapies for individual patients. The IGI update highlights a case in which researchers developed a personalized in vivo CRISPR therapy for a critically ill infant in a matter of months. This example demonstrates that, with the right infrastructure, tailored treatments can be created rapidly. But scaling this approach requires a regulatory pathway that accepts platform‑based logic rather than traditional one‑drug‑one‑trial methods.

That is where new regulatory frameworks come in. BioPharma Dive’s report on Aurora Therapeutics describes how the company aims to use the FDA’s “plausible mechanism” pathway to accelerate approvals for multiple therapies targeting a single disease, each matched to different genetic mutations. This approach resembles a software update model: once you prove a mechanism works, you can adapt it to new “variants” with a lighter approval burden.

Source: BioPharma Dive, “Aurora sets out to capitalize on FDA’s new framework for bespoke drug therapies”.

3) The platform model is reshaping biotech economics

Biotech historically depended on blockbuster drugs with large patient populations. Gene editing flips the script: many of the most meaningful conditions are rare. The platform model—where a core technology can be adjusted for different mutations—promises a way to make rare disease therapies economically viable. Aurora’s approach, for example, is to develop multiple therapies for a single condition to build a “commercial foothold” while expanding to rarer mutations over time.

This has two implications. First, investors are now evaluating biotech platforms similarly to software platforms—by their ability to reuse components and accelerate development. Second, data infrastructure becomes central. The ability to identify, validate, and track mutations is as important as the editing machinery itself. Expect more partnerships between sequencing providers, AI‑driven analytics firms, and gene‑editing companies.

Source: BioPharma Dive, “Aurora sets out to capitalize on FDA’s new framework for bespoke drug therapies”.

4) The operational bottlenecks are manufacturing and reimbursement

The major bottlenecks in biotech are no longer just scientific. Manufacturing gene‑editing therapies is complex, and scaling it without compromising quality is difficult. Additionally, reimbursement models must adapt to the fact that these therapies are expensive up front but potentially curative in the long run. Many health systems are experimenting with payment models that spread costs over time or link payments to outcomes. The success of these models will determine how broadly gene‑editing therapies can reach patients.

Cross‑Cutting Patterns: Platforms, Supply Chains, and Trust

These three fields might seem unrelated, but their trajectories share common patterns.

1) Platform thinking is the default

In AI, platforms revolve around model families and developer tooling. In EVs, platforms are defined by battery architecture and software updates. In biotech, platforms are gene‑editing toolkits and regulatory pathways. Across all three, the winners are those who can standardize a core system while allowing rapid customization.

2) Supply chains are strategic assets

AI depends on compute supply chains and semiconductor capacity. EVs depend on battery materials and manufacturing partnerships. Biotech depends on reagents, sequencing inputs, and specialized manufacturing. These are not background logistics; they are strategic resources that shape cost curves and speed to market.

3) Trust and validation are essential

Each domain is moving into a phase where trust matters more than novelty. Enterprises need to trust AI outputs for mission‑critical work. Consumers need to trust that new battery technologies are safe and durable. Patients and regulators need to trust that gene‑editing therapies are effective and ethical. That trust is earned through rigorous testing, transparent data, and clear standards.

What to Watch in the Next 12–24 Months

Here are the most concrete signals to watch across the three sectors:

AI Models & Providers

• Open‑weight adoption: Watch whether large enterprises publish case studies showing production‑scale adoption of open models for customer service, analytics, or developer workflows.

• Reasoning benchmarks that matter: Expect a shift away from single‑prompt benchmarks toward multi‑step task success metrics, such as agentic workflows or multi‑tool integration.

• Model orchestration tools: The vendors that build durable “model routing” and monitoring platforms will likely become infrastructure defaults.

Automotive & EVs

• Solid‑state pilot‑line yields: These are the most reliable indicators of whether solid‑state can scale beyond prototypes.

• Charging time milestones: Real‑world tests claiming sub‑10‑minute charging for significant range will be pivotal, especially if they happen in commercial settings rather than controlled labs.

• Software‑defined features: Automakers that ship meaningful OTA improvements—range optimization, adaptive driving modes, or subscription‑based features—will set the tone for customer expectations.

Biotech & Gene Editing

• Expansion of regulatory pathways: The FDA’s “plausible mechanism” pathway is a blueprint. If more companies use it successfully, it could dramatically increase the pace of rare‑disease therapies.

• Cost‑sharing and reimbursement innovation: Watch how payers handle high‑cost therapies and whether new financial models gain traction.

• Scaling personalized treatments: If more N‑of‑1 therapies are produced on timelines of months rather than years, the entire economics of rare‑disease treatment could change.

Practical Takeaways for Builders and Investors

For product builders, the takeaway is clear: strategy should be modular. AI systems should be architected to swap models and providers. EV platforms should be designed with software extensibility in mind. Biotech pipelines should treat regulatory frameworks as a core part of product design, not a late‑stage hurdle.

For investors, the signal is that infrastructure bets matter as much as product bets. The most durable companies will be those that own a critical platform layer—model orchestration, battery manufacturing IP, or gene‑editing workflow tooling. They will be hard to displace because they sit at the core of multiple value chains.

Conclusion: The Convergence Era

We are entering a period where AI, automotive technology, and biotech are no longer independent trajectories. They are interconnected systems, each pushing the other forward. AI accelerates material discovery and drug design. EVs are becoming intelligent devices that will increasingly rely on model‑driven software. Gene editing is adopting platform strategies that echo the software industry’s success with reusable components.

The most important trend is not any single breakthrough, but the shift toward platform thinking across all three sectors. That shift is what turns innovation into deployment at scale. In 2026, that is the difference between a lab curiosity and a market‑changing product.

Sources

MIT Technology Review – “What’s next for AI in 2026”: https://www.technologyreview.com/2026/01/05/1130662/whats-next-for-ai-in-2026/

Electrek – “Toyota’s solid‑state EV battery dreams might actually come true”: https://electrek.co/2025/10/30/toyotas-solid-state-ev-battery-dreams-might-actually-come-true/

Innovative Genomics Institute – “CRISPR Clinical Trials: A 2025 Update”: https://innovativegenomics.org/news/crispr-clinical-trials-2025/

BioPharma Dive – “Aurora sets out to capitalize on FDA’s new framework for bespoke drug therapies”: https://www.biopharmadive.com/news/aurora-doudna-urnov-gene-editing-plausible-mechanism-pku/809089/