The Practical Tech Wave: Multimodal AI, the EV Charging Pivot, and CRISPR’s Clinical Moment

Introduction: A year of tangible tech, not just hype

When people talk about “trending tech,” the conversation often drifts into vague futurism. But the current wave is different: it’s concrete, shipping, and measurable. In AI, the market is seeing a shift from “chatbots that write text” to multimodal systems that understand and generate text, audio, and images in near real time. In mobility, the push toward a common EV charging standard is finally moving from press releases to standards bodies and infrastructure plans. In biotech, CRISPR has crossed the symbolic line from lab promise to FDA‑approved therapies, and the industry is now grappling with manufacturing, access, and scale rather than feasibility alone. These developments are non‑political, but they are consequential—because they change what developers can build, how products are designed, and where the next waves of investment will land.

This post summarizes the most important, real‑world tech trends across AI models and providers, cars and charging infrastructure, and biotech. The goal isn’t to predict the far future. It’s to explain what’s already happening, why it matters for the next 12–24 months, and how these pieces connect. We’ll ground the discussion in primary sources from OpenAI, Google, Anthropic, SAE/DOE, Toyota, and public reporting on CRISPR approvals. Then we’ll interpret the patterns that are emerging underneath the headlines.

AI models and providers: the multimodal pivot becomes the default

1) GPT‑4o and the shift to real‑time multimodality

OpenAI’s GPT‑4o (“omni”) announcement is a clear marker of the industry’s trajectory: single models that can natively handle text, audio, and vision. The company describes GPT‑4o as a unified model that processes multimodal inputs end‑to‑end, which dramatically lowers latency and preserves information that used to be lost in pipeline approaches. Instead of “speech‑to‑text → LLM → text‑to‑speech,” the model operates as a single system that can respond to audio in a few hundred milliseconds, closer to human conversational timing. The company also highlights improved performance in non‑English languages, stronger vision capabilities, and lower inference costs relative to prior GPT‑4‑class models.

Practically, this shifts AI from “typing into a box” to “talking and showing.” The design space changes: voice assistants become more natural, computer‑vision tools can provide real‑time explanations, and mixed‑media workflows (say, a design critique where you show a mockup and ask for spoken feedback) become feasible without stitching together several models. For product teams, this means a single provider can now supply the core intelligence for a complex interface—rather than gluing together three or four APIs and hoping the interaction feels coherent.

2) Gemini 1.5 and the long‑context leap

Google’s Gemini 1.5 announcement is a different but equally important signal. The headline is long context: the ability to process huge amounts of information in a single request. Google says Gemini 1.5 can consistently handle up to a million tokens in testing, which moves the ceiling from “long document” to “entire codebase or full video transcript.” To get there, the model uses a Mixture‑of‑Experts (MoE) architecture that targets efficiency—higher capability without proportional compute growth. The result is a foundation model that can absorb much larger context windows while maintaining high quality.

Why this matters: long context unlocks a new class of products. Developers can now ask models to reason across thousands of pages, analyze entire logs, or summarize multi‑hour meetings without the brittle chunking strategies that were common even a year ago. It also changes the way we build tools. Instead of elaborate retrieval systems that carefully select and inject small snippets, teams can sometimes “just include the whole thing” and let the model decide what’s relevant. That doesn’t remove the need for retrieval, but it does reduce the engineering complexity for many workflows.

3) Claude 3.5 Sonnet and the productivity‑first mindset

Anthropic’s Claude 3.5 Sonnet reinforces a different trend: models are getting faster and more cost‑effective while still improving on benchmarks. The company positions Claude 3.5 Sonnet as its first release in the 3.5 family, with improved reasoning, coding performance, and vision understanding. Notably, it targets a “mid‑tier” price point with high quality, emphasizing speed and cost rather than only raw capability. That’s a key shift: the market is increasingly segmented by latency and economics, not just accuracy scores. The best model for a task is often the one that gets you 90% of the result at 20% of the cost.

For teams that are shipping products, this matters more than top‑line benchmarks. The “best” model is the one that can run reliably at scale without blowing up your unit economics. Claude 3.5’s positioning is a reminder that the model arms race is now about cost curves and inference efficiency as much as raw capability. The customers aren’t just AI enthusiasts; they are enterprises who need predictable, scalable pricing.

4) The model market is getting layered

Put these releases together and a pattern emerges: the market is layering. There are flagship multimodal models (GPT‑4o) that aim for real‑time interaction. There are long‑context models (Gemini 1.5) that aim for large‑scale reasoning across documents and data. There are fast, cost‑efficient models (Claude 3.5 Sonnet) aimed at production workloads. For developers and companies, this is a healthy shift. It means you can pick the model that matches the product’s dominant constraint—latency, context, or cost—rather than always betting on the single most capable model.

It also pressures providers to differentiate not only by model quality but by platform features. Tool‑use frameworks, safety and evaluation tooling, on‑device inference options, and enterprise integrations are now as important as core model quality. That’s why providers are investing heavily in APIs, compliance tooling, and deployment options across major clouds. The “model” is increasingly a component of a larger platform.

5) What this means for builders

For builders, the short‑term playbook is becoming clearer. First, design products around the model’s strength: if you need live conversation or audio‑first workflows, prioritize GPT‑4o‑class models. If you need deep context over large documents, prioritize long‑context models like Gemini 1.5. If you need cheap, reliable throughput at scale, mid‑tier models like Claude 3.5 Sonnet will often be the most pragmatic choice. Second, avoid over‑engineering. The new generation of models can handle more context and modalities natively, which simplifies stacks that used to be a patchwork of smaller systems.

Third, invest in evaluation. As models become more capable, the failure modes become subtler. Automated evals, regression tests, and human review loops are no longer optional. The bigger the model, the easier it is to be impressed by a demo while missing edge cases. Teams that treat AI as a product component—with QA, monitoring, and clear metrics—will win.

Cars and mobility: charging standards and battery roadmaps drive the next EV wave

1) J3400/NACS standardization is the quiet, high‑impact shift

The EV market has been stuck with a problem that is more boring than breakthrough: incompatible charging connectors. In December 2023, SAE International published the Technical Information Report (TIR) for J3400, based on Tesla’s North American Charging Standard (NACS). The U.S. Joint Office of Energy and Transportation notes that this standardization enables suppliers and manufacturers to deploy the J3400 connector across vehicles and charging stations. It’s a foundational step that moves NACS from “Tesla’s connector” to a shared industry standard that can be cited in regulations and adopted across the ecosystem.

This matters because interoperability is the real accelerant for EV adoption. A consistent connector reduces consumer anxiety, lowers infrastructure costs, and encourages faster build‑outs by charging providers. It also aligns with federal funding rules that require open access. If you’re an automaker or an infrastructure operator, a standard connector means you can focus on scaling networks and improving reliability rather than juggling multiple hardware standards.

2) Toyota’s battery roadmap shows the strategic focus: range, cost, and fast charging

Toyota’s published battery technology roadmap provides a rare, detailed view of how a major automaker plans to shift the economics of EVs. In its 2023 roadmap, Toyota describes multiple battery paths—“Performance,” “Popularization,” and “High Performance”—aimed at increasing driving range and reducing costs. It also outlines a solid‑state battery program targeting fast charging and higher energy density, with a stated goal of commercial readiness around 2027–2028. The roadmap emphasizes a 10‑minute or less charging target for its first solid‑state battery and significant range improvements relative to current lithium‑ion packs.

Why this matters for the market: EV adoption is still constrained by a triad—range, charging speed, and cost. Battery roadmaps like Toyota’s show that automakers are attacking all three simultaneously. Even if specific timelines slip, the broader trend is clear: high‑energy‑density packs, faster charging, and lower costs are on the near‑term roadmap, not the distant horizon. That has ripple effects across mining, supply chains, and charging infrastructure planning.

3) Software‑defined vehicles are no longer a niche concept

Beyond batteries and connectors, cars are becoming software platforms. Over‑the‑air updates, centralized vehicle operating systems, and integrated AI assistants are now table stakes for high‑end EVs and increasingly for mainstream models. The same multimodal AI progress that powers chatbots is also feeding into in‑car experiences: natural voice controls, predictive maintenance insights, and smarter driver assistance interfaces. The long‑term value is not just the hardware; it’s the ability to update and improve the vehicle after it leaves the factory.

For manufacturers, this changes business models. Recurring revenue from software services, better telemetry, and user personalization can become as valuable as the initial sale. For consumers, it means vehicles that improve over time—if, and only if, manufacturers maintain long‑term software support. This is where the competition between automakers will intensify: not just on horsepower or range, but on software quality, reliability, and trust.

4) The practical takeaway for the next 12–24 months

Expect 2025 and 2026 to be years of “infrastructure catch‑up.” As J3400/NACS standardization progresses, infrastructure providers will need to retrofit or expand networks, while automakers align their future models with a common connector. Meanwhile, battery improvements will continue to emerge in incremental steps, rather than a single giant leap. The wins will be measured in minutes of charging time and tens of miles of range—small numbers that add up to major shifts in consumer perception.

For buyers and fleet operators, the practical strategy is to watch charging network availability and connector compatibility as closely as you watch vehicle range. Standards don’t just affect the future; they affect resale value and usability today. For developers building services around mobility (route planning, charging optimization, fleet management), the improving stability of standards will reduce complexity and open opportunities for better, more consistent user experiences.

Biotech: CRISPR moves from breakthrough to deployment

1) The first CRISPR therapy approvals mark a historic inflection

In December 2023, the FDA approved the first gene‑editing treatment for a human illness. Reporting from NPR notes that the FDA approved two gene therapies for severe sickle cell disease, including a CRISPR‑based therapy (Casgevy) developed by Vertex Pharmaceuticals and CRISPR Therapeutics. This is a landmark decision: it transforms CRISPR from a research tool into a clinically approved medical intervention. The approvals are also a reminder that “biotech progress” is not just new papers—it’s regulatory clearance, manufacturing readiness, and patient access.

Technically, the therapy involves extracting a patient’s cells, editing them with CRISPR to increase fetal hemoglobin production, and then reinfusing them. The goal is to eliminate or drastically reduce painful crises associated with sickle cell disease. This is not a trivial, one‑day outpatient procedure; it’s a complex, high‑cost treatment that requires specialized infrastructure. But it works, and that changes the risk calculus for future gene‑editing therapies.

2) The industry now faces manufacturing and access challenges

The biggest challenge for CRISPR therapies is no longer “does it work?” but “can it be delivered at scale?” Autologous gene therapies are complex: each dose is personalized, manufacturing is slow, and costs are high. The near‑term trend to watch is the transition from bespoke treatments to more standardized, scalable processes. This includes improvements in cell collection, automation in labs, and better logistics for handling patient‑specific material.

Another crucial issue is access. Regulatory approval does not automatically translate into widespread availability. Health systems need to build capacity, insurers need to determine coverage, and patient referral pathways need to be established. This is where biotech’s progress will be measured over the next few years—not just in lab results, but in the number of patients who can realistically receive the therapy.

3) AI’s role in biotech is becoming more operational than experimental

AI’s impact on biotech is increasingly practical. Rather than abstract “AI will discover all drugs,” the trend is toward operational improvements: prioritizing drug candidates, optimizing clinical trial design, and accelerating literature review. Long‑context models are particularly relevant here because they can digest large volumes of scientific literature and trial data. Multimodal models can analyze images, lab results, and patient records. The result is a “lab assistant” that doesn’t replace researchers but speeds up decision‑making and reduces time spent on repetitive analysis.

The biotech sector is also seeing increased adoption of automated lab systems and robotics. These tools generate enormous amounts of data—imaging, sequencing, assay results—that are a natural fit for AI‑driven analysis. As CRISPR and gene therapies expand, the need for data‑centric operational tooling will grow. That creates opportunities for startups and platform companies focused on lab informatics, dataset curation, and compliance tooling.

4) The practical takeaway for the next 12–24 months

Expect the CRISPR wave to move from first approvals to a slow, deliberate expansion. The next milestones will likely focus on additional indications, manufacturing efficiency, and pathways to reduce cost. The companies that master operational scale—rather than just scientific novelty—will shape the next phase of the field. For technology builders, the opportunity lies in tooling: clinical trial analytics, compliance automation, data pipelines, and AI‑assisted analysis that can be trusted in regulated environments.

How these trends connect: a shared push toward “usable scale”

AI, EVs, and biotech look like separate domains, but they share a common theme: usable scale. GPT‑4o, Gemini 1.5, and Claude 3.5 Sonnet represent models that are increasingly capable while also targeting real‑world constraints like latency, cost, and context limits. J3400 standardization is about scaling EV adoption by removing friction. Toyota’s battery roadmap aims to scale EV usability by improving range and charging speed. CRISPR approvals are about scaling gene editing from a lab success into a widely available therapy.

In each case, the move from “cool demo” to “usable product” is driven by practical trade‑offs. AI models need lower latency and cost. EVs need reliable, standardized charging. Gene editing needs manufacturing and access solutions. This is why the next year is likely to be less about brand‑new inventions and more about accelerating deployment and integration.

What to watch next (2026 horizon)

1) In AI: evaluation and governance tooling

As models become more capable, it becomes harder to understand their failure modes. Expect a surge in eval frameworks, automated testing, and monitoring tools that let teams track performance regressions across model versions. The highest‑impact tools will be those that integrate into CI pipelines and can be run automatically across product workflows. Providers that ship strong evaluation tooling alongside models will gain a durable advantage.

2) In mobility: charging network reliability and pricing transparency

Connector standardization will be necessary but not sufficient. The real competitive battleground will be uptime, repair speed, and pricing transparency at charging stations. In other words, “reliability as a service.” This will matter more to consumers than raw charging speed. The companies that win on reliability will likely become the default networks for long‑distance travel.

3) In biotech: scaling manufacturing capacity

Biotech’s next bottleneck is manufacturing. Whether it’s CRISPR, mRNA, or other advanced therapies, the capacity to produce treatments at scale is becoming the constraint. Expect investments in modular manufacturing, automation, and higher‑throughput processes. This will also create demand for new software systems that handle compliance, traceability, and quality assurance at scale.

Conclusion: the practical tech cycle has begun

The dominant tech trends right now are not speculative. They’re practical. AI models are becoming multimodal and cost‑efficient. EV infrastructure is converging toward a common standard. Biotech is proving that gene editing can be a real therapy rather than a hypothetical future. Each domain is moving from “possible” to “deployable.” That’s a much harder phase than the invention stage because it requires integration, reliability, and scale.

For builders and operators, the opportunity is to ride that deployment wave: build products that take advantage of multimodal AI, design services around stable EV standards, and create tools that make biotech operations more efficient and compliant. The next two years will reward teams that focus on practical value over flashy demos. The most meaningful innovations will be the ones that work reliably, at scale, in the messy real world.

Sources

OpenAI: “Hello GPT‑4o” (May 2024) — https://openai.com/index/hello-gpt-4o/

Google: “Our next‑generation model: Gemini 1.5” (Feb 2024) — https://blog.google/innovation-and-ai/products/google-gemini-next-generation-model-february-2024/

Anthropic: “Introducing Claude 3.5 Sonnet” (Jun 2024) — https://www.anthropic.com/news/claude-3-5-sonnet

Joint Office of Energy and Transportation: “SAE J3400 Charging Connector” — https://driveelectric.gov/charging-connector

Toyota Europe: “Our battery technology roadmap to change the future of cars” (Sep 2023) — https://www.toyota-europe.com/news/2023/battery-technology

NPR: “FDA approves first gene‑editing treatment for human illness” (Dec 2023) — https://www.npr.org/sections/health-shots/2023/12/08/1217123089/fda-approves-first-gene-editing-treatments-for-human-illness