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

Honestly, I debated whether to write a predictions piece at all. Sharing thoughts privately with my co-founder Hyeonji Hwang is one thing; putting them out publicly is another.

Get it right and people say “obvious.” Get it wrong and it’s embarrassing. But the pace of events since the start of 2026 has been anything but ordinary, so here’s my attempt to make sense of it all.

Developers Won’t Be Replaced This Year

The era of surviving on coding skills alone is ending. What’s happening isn’t replacement: it’s a redefinition of roles.

As someone who studied bioengineering, one piece of news from a few days ago hit me hard. The cost of sequencing the human genome was $2.7 billion 25 years ago (the Human Genome Project). Five years ago it dropped to $1,000. This week, Element Biosciences announced VITARI, a $100 sequencing device. Even in biotech, one of the slowest-moving fields, change is happening at this speed. Most industries will turn over faster.

And software is much faster. During the mobile era, device replacement cycles gave us time to adapt. With AI, things change on a daily basis. That’s the nature of software.

• 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 vibe feels exactly like early 2023 right after ChatGPT launched (December 2022).
• 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 just keeping up with the flow of information will be something only the few who run dozens of agents can manage. I used developers as the example, but everyone should start cultivating alternative aptitudes now, including outsourced sales skills, social media communication, stable investment income management, and more.

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 just waste time, so abuse is rising. What holds value in the AI era is data that’s hard for models to learn from but can be pulled in real-time at inference.

The trend became crystal clear 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/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 beats training on it. Overtaking companies that have accumulated years of data is incredibly difficult.

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, “This much value at this price?”
• An Anthropic product leader mentioned they’re even “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 only remaining source of value in AI software. Seedance 2.0, GPT-3.5-Codex, and similar offerings are proof.

The conclusion: build a data API that feeds the first half of inference, package model access rights, or do enterprise outsourcing faster. Back-end analysis is meaningless: AI already does it better and cheaper.

AI Agents Ignite the 5th Hardware Boom

OpenClaw made this unmistakably clear. 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.

Here’s where it gets interesting:

• 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. What about 10 or 100 personal agents per person?
• A “device” = computing power (CPU) + storage (DRAM, SSD) + networking. These run on servers or Mac Minis, with each agent/user in its own Docker container.
• Legacy chips can handle some of this workload, creating a massive opportunity for Chinese firms. Samsung and SK Hynix pausing and then resuming fab expansion may well be connected to this.

(Feat. Samsung, SK Hynix, TSMC, SanDisk: compared to the Nvidia precedent, valuations may still be cheap. But unlike Nvidia, China exists as a viable substitute, and that’s the catch.)

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, 73× faster than an Nvidia H200 and roughly 10× 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.

Everyone said these chips would never achieve power efficiency or scalability. Yet specialized-chip startups keep attracting massive capital:

• 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.
• Model-specific chips can adapt to a new model in about two months by swapping just two masks. Combined with frontier models, this could reshape the entire inference cost structure.

A new semiconductor era is clearly unfolding.

An OpenClaw-Adjacent Startup Will Exit Big

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

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).
• 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.

Ingredients for the next exit: OpenClaw → pi-mono → ?

• OpenClaw itself was 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 pivoting to OpenClaw compatibility.
• User expectations are shifting from “ask ChatGPT” to “let the agent do it.” Even a small loosening of data-access permissions makes convenience overwhelming.

I’m confident that about three services will emerge that leverage pi-mono exceptionally well, and one of them will be acquired.