5 AI Predictions for 2026: Agents, Chips, and Historic Exits

My co-founder Hyeonji Hwang and I share predictions privately all the time. Making them public is harder, because the downside is asymmetric: get it right and people say “obvious,” get it wrong and it’s on record. Still, the pace of events in early 2026 is unusual enough that I’d rather commit to a position than gesture vaguely at uncertainty. Each of these carries roughly 50% confidence, which I think is the honest number.

Developers Won’t Be Replaced This Year, But the Role Is Changing Fast

The era of surviving on coding skills alone is ending. What’s happening is a redefinition of roles, not replacement outright. That said, the transition is fast enough to strand people who don’t adapt.

As someone who studied bioengineering, the genome sequencing cost curve is the clearest analog I know. The Human Genome Project cost $2.7 billion 25 years ago. Five years ago sequencing dropped to $1,000. This week, Element Biosciences announced VITARI, a $100 sequencing device. Even in biotech, one of the slowest-moving fields, the curve is steep. Software moves faster.

During the mobile era, device replacement cycles gave the industry time to adapt. With AI, the tooling changes on a daily basis.

• 2024: Cursor proliferation → Bolt & Lovable full-stack app generation → Karpathy’s “vibe coding” → 2025: Claude Code, Opus 4.5, Gemini 3.0 Pro → January 2026: the SaaSpocalypse. Two years to get here.
• SaaSpocalypse: In the first week of February alone, $285 billion in market cap evaporated from the software sector. Anthropic’s Claude Cowork plugin was the trigger. The market reaction felt similar to early 2023 right after ChatGPT launched.
• Infrastructure software engineers remain in short supply in the US, but other roles are already taking a statistical hit. Junior software engineer job postings are down 45% compared to 2023.

Going forward, even keeping up with the information flow will be something only the few who run dozens of agents can manage. The pressure isn’t limited to developers: outsourced sales, social media, and investment income management are all areas where building alternative aptitudes now makes sense.

Software Survives as Data Providers or Model Packagers

From a user’s perspective, it doesn’t matter whether something is the original or a clone. Lawsuits waste time, so abuse is rising. What holds value in the AI era is data that models can’t easily learn from in training but can pull at inference time.

Two deal patterns made this concrete in January.

Data-source acquisition: connection, not training. Perplexity partnered with BlueMatrix to integrate institutional-investor financial research data directly into its Enterprise product (announced January 13). Manus partnered with SimilarWeb, connecting web and app traffic data via an MCP server so AI agents can analyze it directly (also announced January 13). For data like this, making it accessible at inference time beats training on it. Catching up to companies that have accumulated years of proprietary data is genuinely difficult, and most challengers won’t.

Model-access packaging: $100–$200/month delivering $10,000+ in value. Claude Max at $100–$200/month, ChatGPT Pro at $200/month, Higgsfield at $149–$249/month: usage that would cost $200–$400 via raw API calls is being wrapped into plans that make users think the pricing is almost unfair in their favor. An Anthropic product leader mentioned they’re “considering a $500/month plan,” reflecting strong demand for premium subscriptions. Exclusive model access, delivered faster and at a more effective price than anyone else, is the remaining source of durable value in AI software.

The conclusion: build a data API that feeds the first half of inference, package model access rights, or do enterprise outsourcing faster than competitors. Back-end analysis alone is not defensible; AI already does it better and cheaper.

AI Agents Ignite the 5th Hardware Boom

OpenClaw made this concrete. Built by Austrian developer Peter Steinberger, this open-source personal agent hit 60,000 GitHub stars within 72 hours and has since surpassed 145,000. It automatically handles email management, scheduling, web browsing, and shopping through messaging apps like WhatsApp, Telegram, and Slack. DigitalOcean released a one-click deployment, and Raspberry Pi published an official guide.

The hardware implication is straightforward:

• Agents must respond instantly when a user needs them, so each agent needs its own device or instance.
• The concept of one agent per person alone doubles current computing demand. Scale that to 10 or 100 personal agents per person and the numbers become very large.
• A “device” here means computing power (CPU) + storage (DRAM, SSD) + networking, running on servers or Mac Minis with each agent in its own Docker container.
• Legacy chips can handle some of this workload, which creates a real opportunity for Chinese firms. Samsung and SK Hynix pausing and then resuming fab expansion may be connected to exactly this demand signal.

(Feat. Samsung, SK Hynix, TSMC, SanDisk: compared to the Nvidia precedent, valuations may still be cheap. Unlike Nvidia, though, China is a viable substitute in this segment, and that changes the ceiling.)

The Era of Model-Specific Chips

Toronto-based Taalas unveiled the HC1, an ASIC chip built exclusively for Llama 3.1 8B. The result: 17,000 tokens per second, 73x faster than an Nvidia H200 and roughly 10x faster than Cerebras, the current speed champion. By etching model weights directly into transistors, HC1 needs neither HBM nor liquid cooling, and power consumption drops to 1/10.

Taalas has raised $219 million in total and plans to support models up to 20 billion parameters with the HC2.

The skeptical case against model-specific chips has always been obsolescence: etch one model’s weights and you’re stuck when the next model ships. Taalas says HC2 can adapt to a new model in about two months by swapping just two masks. That’s a shorter cycle than most people assumed, though it hasn’t been stress-tested against a major frontier model release yet.

Capital is moving into this space regardless:

• December 24: Nvidia licensed Groq’s LPU technology for $20 billion and brought in key talent (founder Jonathan Ross, president Sunny Madra), effectively an acquisition.
• Cerebras withdrew its IPO and raised over $1 billion, maintaining an independent path.

If the two-month adaptation claim holds, model-specific chips could reshape the entire inference cost structure. If it doesn’t, the capital will have funded a very expensive dead end.

An OpenClaw-Adjacent Startup Will Exit Big

The basis for this prediction is a pattern that already played out once.

The established pattern: Browser-use → Manus → Meta acquisition. In 2025, open-source Browser-use demonstrated AI automation’s potential. Manus combined Sonnet 4 with Browser-use to open the agent era (March 2025). The result: $100M ARR in just 8 months. On December 29, Meta acquired Manus for over $2 billion, one of the fastest unicorn exits in history.

The next candidate: OpenClaw → pi-mono → ? OpenClaw itself is open-source. Its creator, Peter Steinberger, confirmed he’s joining OpenAI on February 15; OpenClaw continues as an independent foundation. OpenClaw’s engine, pi-mono (developed by Mario Zechner, ~8,900 GitHub stars), is emerging as the core SDK for personal-agent services. In China, Alibaba, Tencent, and ByteDance have all released agents optimized for OpenClaw. Models and services like Minimax M2.5 and Kimi Claw are moving toward OpenClaw compatibility. User expectations are shifting from “ask ChatGPT” to “let the agent do it,” and even a small loosening of data-access permissions makes convenience overwhelming.

My expectation is that roughly three services will emerge that use pi-mono exceptionally well. One of them will be acquired. The harder question is which one, and that depends entirely on who builds the right data integrations in the next six months.