The AI Landscape in 2026: How OpenClaw Is Reshaping AI Entrepreneurship — New Shifts in Models and Applications | BlueRun Ventures AI Annual Outlook
The AI industry has never lacked for new stories.
"The AI industry never runs short of new stories."
As 2026 began, OpenClaw, Claude Cowork, and Seedance 2.0 went viral one after another. New product formats and collaboration paradigms kept emerging, and every so often, the industry would flash another "this time is different" signal.
Amid all this change, we had some burning questions: What new trends are brewing in 2026 that haven't been fully discussed yet? What does OpenClaw really mean for startups and users? From 2025 to 2026, are we any closer to AGI?
This is the second installment of BlueRun Ventures' annual AI outlook. We spoke with founders from portfolio companies including Moonshot AI, AgiBot, Galaxy Universal, and Yuanli Intelligence — entrepreneurs operating across wildly different battlefields: foundation models, embodied AI, materials, entertainment, social, and recruiting. Yet all are accumulating firsthand knowledge from the front lines of the AI wave.
In their eyes, the future doesn't have a single standard answer. They describe large models in terms of "taste" and "values," and they care whether AI can truly understand joy, aesthetics, and emotion.
This article reads more like a stage-by-stage observation log. We hope to see, as time passes, which consensus views are loosening, and which "seemingly non-mainstream" ideas are quietly taking root.
If you have fresh perspectives on the AI industry, we welcome you to share them in the comments. Looking back at the history of frontier technology, many truly important innovations began from non-consensus.
The buzzword of the AI industry in 2026 is undoubtedly OpenClaw. It even boosted Mac mini sales.
But in entrepreneurs' eyes, OpenClaw's significance goes far beyond being a hit product. It changed users' mental model of AI.
"Early Agents were like reliable interns who 'responded to everything' — they made implicit workflows explicit, so you'd feel comfortable handing tasks to them," said Whisper, founder of Trooly.AI. "But OpenClaw, this kind of Proactive Agent, doesn't just execute commands. It guesses your intentions and proactively allocates resources to push things forward. Like the hit game Travel Frog back in the day — it would surprise you every now and then."
This shift from "passive execution" to "active initiative" is a qualitative change. Sam Gao, co-founder of DINQ, believes OpenClaw helped the entire industry complete a crucial market education: "In 2025, people discovered AI could handle single tasks. In 2026, users began expecting Agents to systematically and proactively solve complex tasks."
This shift in mindset directly sparked founders' thinking about product form. The Hyper3D.AI team put it bluntly: "When model capability reaches usable levels, inference costs drop dramatically, and interaction frameworks innovate — when these three conditions align, the habit of starting with an app as the product entry point may be too limiting. Future AI applications will take far more diverse forms — maybe a Telegram bot, maybe voice-activated, maybe even without a traditional GUI. The key isn't the form of entry, but whether AI can truly understand user intent and complete tasks."
Whisper of Trooly.AI believes, "We're like boats on water — the higher the water level, the higher we float. There will be many fun directions emerging this year." Trooly.AI is working on integrating into customers' workflows to proactively solve problems. "Often customers don't even know what questions to ask their users. When you dig deep into their business, you know what to ask."
Daqian Tech founder Qiuqiu's thinking points further into the future. She believes AI companies need to consider not individual features, but how different Agents collaborate, assign tasks, and even spawn new tasks within an entire ecosystem. "If there aren't tools suitable for AI to call, you may need to think about how to produce compatible tools."
In the view of Peng Zhihui (aka "Zhiyuan Jun"), co-founder of AgiBot, long-running AI platforms like OpenClaw and embodied intelligent systems will both become highly active directions in 2026. "This isn't a sudden fad, but the natural overflow of years of accumulated progress across models, engineering, and data in the AI industry."
An even more intriguing non-consensus view comes from Wang Ziboyan, a participant in the fifth cohort of the Buming Entrepreneurship Camp. He ran OpenClaw on a $5 chip using C, and posed a thought-provoking question: "If AI is for everyone to use, can its command layer become lighter, cheaper, and more private?"
Changes at the application layer ultimately stem from evolution in underlying model capabilities. As the industry crosses from LLMs to VLMs, entrepreneurs' attention has shifted toward more abstract, fundamental qualities — things like "values" and "integration."
On January 27, Moonshot AI released and open-sourced its K2.5 model. Speaking on the challenges of training VLMs, founder Zhilin Yang introduced a strikingly anthropomorphic concept: "taste."
"Intelligence itself is non-fungible, and a model's personality is also an expression of 'taste,'" Yang said. "Some reports noted that K2.5 is less inclined to pander to users. This may actually be a good character trait, because constantly reinforcing users' existing views can be dangerous in certain situations. Building a model is, in essence, creating a set of values."
Zhang Fan, founder of Yuanli Intelligence, takes a different angle: "From first principles, not only is energy conserved, but attention is conserved too. A model's attention is a constant — the more information you feed it, the more diluted its attention becomes." Zhang noted that "the first principle of harnessing models is signal-to-noise ratio. You need to compress prompts and express your goals with maximum information density to fully 'squeeze' the model and achieve higher intelligence."
Meanwhile, model architecture itself seems to be gestating toward "grand unification." Sam Gao of DINQ observed that multimodal AI is currently in a "Warring States period," and what he looks forward to is "an era where all modalities are unified, eliminating the need for cross-modal calling." He pointed to the Mercury model (dLLM), which is attempting to bridge text, images, and video through a single paradigm.
Peng Zhihui of AgiBot directs his gaze toward more expansive systems. He believes the decisive factor in 2026 will be "system-level intelligence" — a closed loop composed of foundation models, memory modules, tool use, planning and execution, and feedback learning. "The marginal returns of simply scaling model size are diminishing. Going forward, AI's value won't be measured by how big or smart it is, but by whether it can operate stably and self-improve in real-world scenarios."
In Peng's view, three practical foundations enable "system-level intelligence": First, general-purpose foundation models have achieved usable levels of understanding, reasoning, and planning, sufficient to serve as "cognitive kernels" embedded in complex systems. Second, compute costs continue falling, coupled with maturing inference acceleration, model compression, and distributed scheduling technologies, making long-running AI and multi-agent collaboration engineering-feasible. Third, data is shifting from text-dominated to process-oriented forms like behavioral logs, interaction trajectories, and environmental feedback — naturally driving AI toward closed-loop optimization in the real world.
Jia Peng, CEO of Zhijian Power, echoed this observation: "The biggest AI company in the future may not be the strongest AI company today, but the one with the strongest system integration capability — using engineering prowess to turn models into products with both high ceilings and high floors."
Once AI possesses "initiative" and "values," it still needs to enter the physical world and face real-world testing.
At the 2026 Spring Festival Gala, Galaxy Universal's Galbot G1 made its debut as the first autonomous working robot in Gala history, sharing the stage with Shen Teng and Ma Li. It performed a series of actions — spinning walnuts, picking up debris, fetching items, folding clothes, and skewering sausages — all without pre-programmed routines. Relying on Galaxy Universal's self-developed embodied foundation model "AstraBrain," the robot made real-time decisions and completed tasks autonomously. The scene was deeply symbolic: AI stepped out of virtual chat windows and touched the tangible, everyday world.
Wang He, founder of Galaxy Universal, believes that while large language models excel at processing information, they lack understanding of physical space. Therefore, embodied AI's value lies not in offices, but in factories, warehouses, and logistics — serving more down-to-earth scenarios.
Zhang Fan of Yuanli Intelligence also observed that as models' input-output boundaries keep expanding, the learning curve steepens, and the scenarios where intelligence can intervene multiply rapidly. But he emphasized that AI still has a gap to cross before truly embedding into complex, stable commercial systems. In Jia Peng's view, 2026 is the year the AI industry's focus must shift from competing on models to competing on productivity.
This "productivity-first" mindset is equally clear in AI for Science. Yang Mengyang, co-founder of Kaiwuji, brought the discussion back to value itself: "Rather than chasing hot concepts, it's better to deeply bind AI capabilities to specific problems. Language models need to understand corpora; materials models need to understand structure and properties — the underlying logic isn't that different."
Beyond productivity scenarios, AI is also gradually embedding itself into entertainment and leisure.
The problem is that joy and aesthetics resist abstraction into concrete rules. Even when models can achieve high quality in single-generation content, there's still considerable distance to truly mature, sustainably mass-consumable product forms. Qiuqiu, founder of Daqian Tech, noted, "It's hard enough for people to align with each other, let alone getting a large model to understand."
Qiuqiu believes, "For consumer entertainment products, 2025 wasn't the year this track exploded — video-based interactive content hasn't reached consumer-grade yet. 2026 will be a good year." In her view, interactive content follows a progressively deepening path: from watching, to experiencing, to deep participation, to real-time generated active interaction. Text-and-image products and search-style interactions are expanding toward video and richer sensory experiences.
The Hyper3D.AI team believes multimodal generation's competitive focus is shifting from "can generate" to "controllable, editable." Controllability for images and video has advanced considerably, and 3D generation has reached a tipping point. But because the toolchain is longer and productization started later, the industry's accumulation in controllability remains early-stage. "After a single generation, if users are only partially satisfied but can't edit, they have to start over — and often end up losing the parts they originally liked."
Hyper3D.AI's judgment: The inflection point for 3D applications isn't just single-generation quality, but whether it can enter an iterable workflow. Only as 3D controllability strengthens and toolchains gradually connect can long-standing 3D demands in gaming, e-commerce, 3D printing, design, and film move from scattered trials toward scalable supply and continuous iteration.
Improvements in models' multimodal capabilities are also reshaping AI hardware.
Wang Tao, founder of Shentingji, believes that in 2026, AI's emotional interaction will upgrade from fuzzy perception to building user emotional models through multimodal data, enabling personalized interaction. "We want robots to remember user preferences, identify emotional triggers, and better accompany users."
Li Yong, founder of Haivivi, also believes that large models have already solved the "understand and chat" problem, and 2026 will be the year edge models and multimodal interaction explode on hardware. Toys may be the most natural, lowest-barrier physical vessel for these technologies. "Pure AI hardware feels 'cold,' but infused with well-known IP, personality, and world-building, it gains a soul. AI toys don't provide cold command-based interaction, but warm emotional connection."
From technological evolution to product landing, we also discussed a grand question with these entrepreneurs: What is AGI (Artificial General Intelligence), and are we any closer to it now?
An interesting phenomenon: perhaps a thousand entrepreneurs have a thousand definitions of AGI.
In Jia Peng's view, a simplified version of AGI is essentially a general-purpose foundation model. "The essential characteristics of life manifest in perception, thinking, and action. When a foundation model can truly achieve this closed loop, it means AGI isn't far off."
One entrepreneur even discussed his own existential anxiety as a human being. He believes that "all life eventually perishes, and the universe will end in silence. But the flip side of perishing is rebirth. If there's a chance to create new species through AGI, it means the continuation of humanity as a species."
Another entrepreneur proposed that we may already have touched AGI at certain levels. Zhang Fan of Yuanli Intelligence pointed out that many people's expectations of AGI actually hew closer to ASI — an omnipotent intelligence that comprehensively surpasses humans. In reality, today's AI already possesses certain AGI-like characteristics in localized capabilities. For instance, in highly closed scenarios like Go, superhuman intelligence was achieved long ago.
As for when AGI can be achieved more broadly, multiple entrepreneurs converged on a consensus: it won't appear suddenly all at once, but will land gradually in fragments or components.
In Wang Tao's view, AGI itself is a continuously evolving path. From a temporal perspective, AGI won't be fully realized in the short term, nor is it impossibly distant. The more realistic picture is that over the coming years, capability modules with AGI-like characteristics will keep emerging, gradually assembling into a more complete intelligent system.
Han Zheng, founder of Hillbot, believes AGI must achieve long-thread, step-by-step logical reasoning. The signature signal would be providing complex, self-consistent reasoning for problems humans haven't yet solved. "AGI isn't a single-point breakthrough, but a breakthrough achieved by combining biology, medicine, materials, physical intelligence, and various other technologies."
Peng Zhihui of AgiBot believes that humanoid robots, as the most complex robotic systems, represent the physical form most likely to lead to AGI. Before leaving Huawei to start his company, he wrote: "If programmers are gods of the digital world, then giving robots physical form with your own hands, and imbuing them with soul through AI — that's the true geek's romance."
While imagining AGI's capability boundaries, we must also set constraints. Wang He of Galaxy Universal noted that AGI should operate within human-set rules, serving people rather than replacing them. Future robots shouldn't merely follow commands, but should possess continuous learning ability — self-iterating and self-optimizing through constant interaction with people and environments across vast scenarios.
The Moonshot AI team once asked their model: If the arrival of AGI/ASI threatens humanity, should we continue developing it? The model responded: "AGI/ASI is an amplifier capable of fundamentally transforming human civilization... even helping us redefine human identity and meaning..." In Moonshot AI's view, abandoning AGI/ASI development would mean abandoning the upper limits of human civilization.
These observational notes from 2026 may be overturned or reconstructed in the future. But at this very moment, these entrepreneurs shuttling between algorithms and the physical world are collectively pushing open the door toward AGI.
🎙️ Future Visions of AGI 🎙️ How do you understand "AGI"? Is it Skynet from sci-fi films, or the ultimate form of human-machine symbiosis? 🤔 How do you think people will achieve "AGI"? Algorithmic emergence, compute explosion, or brain-computer interface breakthroughs? 🚀 By March 12, we'll select 5 readers with the "wildest imaginations" from the comments to receive a custom gift carefully prepared by BlueRun. We look forward to your insights — see you in the comments!
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