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Fine-Tuning the Future of AI: Argonautic is proud to support the evolution of the AI ecosystem and the entrepreneurs powering the innovation

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SEATTLE, Sept. 30, 2024 /PRNewswire/ — In the evolving landscape of artificial intelligence and machine learning, foundation models – the backbone of predictive tasks – have captivated the tech world. Their ability to perform learning tasks are transforming the way we approach natural language processing, computer vision, and signal processing. At Argonautic, while we acknowledge the pivotal role foundation models play in the AI value chain, it is our perspective that the verticalization of these models under direction of teams with unparalleled subject matter expertise and access to proprietary training data aligns with our capital efficient thesis versus new generalized foundation models which require high upfront training costs initially and face price commoditization in the long run.

Foundation models are pre-trained deep learning models that serve as the versatile and general base for computationally-intensive predictive tasks. Foundation models are then ‘fine-tuned’ to perform function specific tasks for a given use case. The term was coined by Stanford Academics in 2021 and surged in popularity in 2022 to describe the models which were breaking out at the time.  Foundation models are especially recognized for their ability to perform ‘zero-shot’ and ‘few-shot’ learning tasks, where a task is performed with few or zero examples previous given to the model.

Argonautic believes that while the evolution of foundation models is crucial for the advancement of AI and technology overall, business models which focus on building these baseline models have high capital requirements and likelihood of commoditization over time. Instead, we believe teams building models with strong subject matter expertise and knowledge of the problem to be solved (which may be built on these generalized models) will reliably come out ahead in solving the most important problems. These teams have unique access to proprietary data that can be used to train their fine-tuned models and unique distribution channels that more seamless inserts AI-powered tools into business workflows.

Foundation models are commonly used for a variety of natural language processing, computer vision, and signal processing tasks. Open AI’s “GPT-N” series captured the general public’s attention with its ability to craft coherent seeming text given a wide range of prompts. At its core, GPT-N simply predicts the next word in a sentence, which when scaled produces coherent seeming responses. It is trained on a large “corpus” of text data sourced mainly from the open internet but also from, forums, publications and  books.

Argonautic maintains a strategic focus on industries with use cases that require verticalized models. While recognizing the importance of foundation models in advancing AI and machine learning, it is clear that the concentration on constructing baseline models carries inherent limitations. Instead, Argonautic partners with teams with strong subject matter expertise to build models tailored to specific problem domains, leveraging their unique access to proprietary data and distribution channels. In the context of foundation models, Argonautic acknowledges the widespread applicability in natural language processing, computer vision, and signal processing tasks. By emphasizing the importance of fine-tuning to achieve verticalization, Argonautic underscores the ability of companies to specialize their models while benefiting from the underlying foundation model’s conversational interface. In this landscape, Argonautic positions itself against significant capital deployment in general foundation models due to diversification risks and concerns about the potential disruption posed by open-source models and new architectures.

For a generalized foundation model to become ‘verticalized’, it must be “fine-tuned” by passing in an extra set of domain specific data to tailor the generalized model for a use case. This allows companies to specialize their models while still benefiting from the conversational interface of the underlying foundation model. OpenAI’s ChatGPT, Alphabet’s Gemini, Meta’s Llama and others are foundation model driven businesses, which enables teams to build while avoiding billions of dollars of initial training costs.

Training a foundation model from scratch is a large, expensive, and important data engineering undertaking. The architecture of these models typically rely on transformers, which have been the industry standard for a number of years. Where it differs is the scale. The success of foundation models depends on the ability to seamless aggregate vast amounts of data with trillions of parameters. At the time of writing, this costs in the order of billions of dollars and will only grow as customer demands outpace the cost trends of computation and storage.

Argonautic does not believe general foundation models to be an area of capital deployment given our investment style. First, they require large checks which create diversification risk for our investors. Second, we are wary of the risk of open-source foundation models and new architectures disrupting the economics of proprietary models. For example, Retrieval Augmented Generation has changed the way enterprises look at retraining. Staying on are ahead of the curve is expensive and risky. 

Pre-dating the explosion of interest in private sector machine learning models, Argonautic believes the value of a model comes from a few areas: (1) unique architecture which gives it a technical or economic advantage (2) proprietary data which lets the model produce unique insights (3) ability to integrate seamlessly into existing workflows. Unique architecture is often spun off from academic institutions with heavy financial backing. As such our area of interest is in teams who have demonstrated the ability to use their unique insight to solve a specific problem. Teams in the space tend to work on verticalized foundation models which take general foundation models a step further with proprietary expertise.

For example, our portfolio company Cognaize, which automates financial spreading for large financial institutions, has accumulated years of financial data which allows it to fine-tune a defensible, verticalized foundation model in the financial technology space. Similarly, Document Crunch, which analyzes construction contracts for conflicting language, uses a corpus built over a number of years to produce exceedingly accurate results for its customers. ConCntric’s platform allows it collect data which will eventually inform its own powerful predictive model. The specific problems our protein engineering teams solve cannot be adequately addressed by a general model. The model’s differentiation for our teams is only possible because of the expertise of the overall team and is not reliant on a lasting technical edge.

As such, more important than ever, Argonautic is interested in teams that know the problem and market they are solving better than anyone else. This also protects companies from future disruption. Even with the next generation of trends, such as automated ‘AI agents’, we believe that teams with strong subject matter expertise are equipped to stay ahead of the pack. It is our view that general foundation models will never be able to solve a specific more reliably than a combination of an elite team that understands a problem and verticalized foundation model.

Argonautic is proud to have been deploying into AI since our founding. As technologists, we are excited to watch the field continue to change the world and as investors we see the opportunity to support this growth.

About Argonautic:

Founded in 2017, Argonautic is a AI/ML B2B venture capital fund investing across Fintech, Construction Tech and Biotech. Argonautic invests in entrepreneurs who are redefining the future of technology and innovation.

For more information, visit argonauticventures.com.

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Faith: The Spark Born by the Yudu Riverside

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BEIJING, Oct. 18, 2026 /PRNewswire/ — An interview from China.org.cn with Wang Xiaohui and David Ferguson on the Long March:

David Ferguson:

This is the Yudu River. One dusk in October, 1934, more than 86,000 soldiers of the Red Army crossed this river and embarked on the Long March, a two-year saga that has left its mark in history.

Yet, lingering here by the river, I am struck by a simple question: If you have faith in a bright future, and reality seems to thwart you at every turn, where do you find the moral strength to keep believing?

Wang Xiaohui:

At dusk on Oct. 17, 1934, an army of more than 86,000 troops gathered along the banks of the Yudu River. Over 800 boats of all sizes floated on the water, bound together with planks collected from villagers’ home gates, timber beams, and even coffin boards reserved for elderly locals. The people of Yudu built this pontoon bridge, determined to help the army led by the Communist Party of China (CPC) cross the river.

At that time, no one knew that the great expedition embarked upon by the banks of the Yudu River would go on to transform China and exert far-reaching influence across the world.

China in the 1920s and 1930s was plagued by chaos and despair.

The Kuomintang (KMT), the ruling party back then, was not capable of unifying the nation, leaving warlords locked in endless fighting. In 1931, Japanese invaders launched an assault on northeastern China, beginning their invasion. Crushed under domestic turbulence and foreign aggression, countless civilians were displaced, with many even starving to death on the streets; in certain regions, severe drought resulted in zero harvests, yet the farmers were forced to pay land taxes assessed for more than a decade into the future.

Amid this turmoil, the only glimmer of hope for the Chinese nation was kindled by the revolution ignited by the Communist Party of China.

The CPC, dedicated to winning liberation for the people and national independence, built contingents of workers’ and peasants’ Red Army troops and revolutionary base areas across the countryside. Impoverished farmers were granted land ownership, as well as unprecedented political dignity. On Nov. 7, 1931, the Chinese Soviet Republic was officially proclaimed in Ruijin, then a small village in southeastern China’s Jiangxi Province, establishing a Soviet system under which workers, peasants and soldiers elected representatives to run regional affairs and make policies.

The communist ideal of a fair society free from exploitation and equality for all blazed like a spark in the darkness; it kindled hopes among impoverished Chinese and became the wellspring of faith that enabled the Red Army — a force made up of poor workers and peasants — to overcome all hardships in the days to come.

Yet, bright as the spark was, it burned thin and fragile at times. Could this flame still endure when the ideals it carried were repeatedly battered by harsh reality?

In September 1933, flocks of warplanes clouded the skies over the Red Army’s revolutionary base areas. By then, Japanese invaders were steadily advancing towards North China, but Chiang Kai-shek’s KMT turned a blind eye to the looming national crisis. They mobilized over a million troops and more than 200 aircraft to launch a massive offensive at the Red Army’s base areas. Over the previous three years, Chiang Kai-shek had mounted four major military “encirclement” campaigns aimed at exterminating the Red Army, but all ended in failure. He was desperate for a pivotal victory to restore his prestige and to wipe out the CPC and the Red Army.

At the time, the commander of the Red Army abandoned mobile warfare tactics pioneered by Mao Zedong and other commanders, opting instead for rigid positional warfare involving conventional head-on clashes. But the reality was, some Red Army units didn’t even possess a single light machine gun; how could they confront the heavily-armed KMT army head-on?

Zhong Ming, a Red Army medic, could never erase the memories of gravely wounded soldiers being carried to his aid station. He remembered how a soldier whose chest was blown open begged his comrades to end his life. At the frontline, a whole battalion of Red Army soldiers religiously followed orders to defend crude earth-and-wood bunkers, but under the constant barrage of KMT artillery and airstrikes, every soldier in the battalion laid down their lives; not a single man survived.

What’s more heart-wrenching is that no matter how fearlessly the Red Army fought, the steep imbalance in military strength, paired with erroneous military command led to irreversible losses. By October 1934, the KMT’s iron-clad encirclement closed menacingly around Ruijin.

Faced with this dead-end, what decision would you make?

This stood as the most agonizing decision for the Red Army. The biggest challenge lay not in sacrificing one’s life for an ideal, but in finding a way to keep the revolutionary cause alive.

To stay meant defeat — the Red Army had no room to maneuver within the Central Revolutionary Base Area. To break through the encirclement, however slim the odds, offered a fighting chance. Preserving the Red Army as a living force mattered much more than clinging to isolated strongholds. Where the spark survives, the path ahead remains lit.

The air already turned chilly in October. Units of the Central Red Army assembled in Yudu to prepare for a strategic shift. The plan was kept strictly classified; none of the soldiers knew they were evacuating the base areas, only sensing an unusual vibe.

At dusk on Oct. 17, crowds lined up at the Yudu riverbank. Tens of thousands of villagers flocked from every corner to see the Red Army off. They walked alongside the marching columns, slipping food like eggs and glutinous rice balls into the soldiers’ pockets, praying for their swift homecoming.

The main force of the Central Red Army set off. Within days, they left behind revolutionary base areas where they had survived, grown and taken root. No one anticipated that the journey they embarked on would become the Long March, a trek spanning over 10 provinces. Still less could anyone imagine that this journey would ultimately pave the way for a brand-new China.

Looking back on this chapter of history, many claim that the Red Army only set out on this expedition because the enemies left them no other choice. To a degree, yes, the Long March was indeed a forced strategic shift, yet this fact does nothing to diminish its greatness and glory.

The ordinary Red Army soldiers had no idea how far they would march, or where they were heading. But they clung firmly to their founding mission: to deliver liberation for the people and independence to the nation. This unswerving conviction kept them from disintegrating through countless perils, sustaining them as they marched into the unknown with resolve.

David Ferguson:

I now realize that faith and ideals untested by harsh reality may have no true substance.

Since its founding, the Communist Party of China has always upheld the realization of communism as its fundamental goal. Despite devastating losses and extreme adversity, the Red Army continued to press forward resolutely and never faltered. Its inner strength came from an unshakable belief in the cause, an unwavering commitment to the founding aspiration, and a profound sense of mission. The Red Army was never fleeing pursuers in search of safety; it was marching forward to a future in which it had a steadfast belief.

Wang Xiaohui:

The Long March went on for two years. The Red Army trekked 25,000 li across some of China’s harshest terrain, but they kept their heads high, seeking hope amid despair and marching towards light through unending darkness. The lofty ideals and sense of mission they upheld have also become the torch that guides generations of CPC members in the following centennial, tempering them into steel as they press forward unswervingly toward national rejuvenation.

That spark lit along the Yudu River has never dimmed since.

China Mosaic
http://www.china.org.cn/video/node_7230027.htm

Faith: The Spark Born by the Yudu Riverside
http://www.china.org.cn/2026-10/16/content_118736835.shtml

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Linktel Technologies Highlights Breakthrough 1.6T LPO Performance & Strong Market Pull at OCP 2026

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SAN JOSE, Calif., Oct. 18, 2026 /PRNewswire/ — At the recent 2026 OCP Global Summit, Linktel Technologies showcased its cutting-edge optical interconnect portfolios engineered specifically for next-generation AI computing networks. Among the heavy lineup, Linktel’s 1.6T Linear Pluggable Optics (LPO) solutions commanded immense industry attention, drawing heavy customer interest for their unprecedented energy efficiency, ultra-low latency, and supply-chain resilience.

Spotlight: 1.6T LPO Performance & High Customer Traction

AI data centers face immense scaling pressures, demanding higher bandwidth densities with tighter thermal and power budgets. Linktel’s 1.6T LPO solutions—highlighted by the 1.6T LPO 2DR4—delivered standout performance metrics that captured the spotlight at the summit:

Ultra-Low Power Consumption: Operates with power consumption as low as an impressive 10W, drastically mitigating data center cooling and power loads.Minimal Latency: Features a linear optics architecture that cuts down signal processing delays, making it a critical asset for high-performance AI scale-out fabrics.Enhanced Supply-Chain Resilience: Eliminating the optical DSP mitigates component choke points, ensuring robust scalability for mass deployments.Proven Interoperability: Following its breakthrough performance and multi-switch platform interoperability at the early September 2026 1.6T LPO-MSA Plugfest, Linktel has laid a solid foundation for the 200G-per-wavelength ecosystem.

Surging Market Interest

Throughout OCP 2026, Linktel’s live demonstrations of its 1.6T LPO, LRO, and FRO modules drew crowds of data center architects and industry partners. With volume production already in place, Linktel is fully equipped to meet the exploding market demand for energy-efficient, high-density 1.6T connectivity.

Overview of Other Innovations at OCP 2026

In addition to the heavily requested 1.6T LPO lineup, Linktel showcased two other major high-speed architectural pillars:

12.8T Liquid-Cooled XPO:Featured a live thermal demonstration integrating advanced liquid cooling to guarantee stable operations in high-density AI nodes.As a founding member of the XPO MSA, Linktel is actively scaling XPO series with FRO, LRO, and LPO variants for comprehensive scale-up, scale-out, and scale-across frameworks.6.4T/7.2T NPO Modules:Displayed with covers removed to reveal internal architectural designs supporting both ELS and ILS configurations.Combines compact high density with low power and low latency, backed by an established 6.4T/7.2T NPO roadmap.DMD Technology & Executive Presentation:Linktel Engineer Director Dr. Grace Zhang hosted a heavily attended Executive Session titled “Dispersion Managed Transceiver for Next-Gen AI Fabrics,” detailing cost-effective 800G (20km) and 1.6T (10km) solutions for OCS SuperPOD architectures, as well as the feasibility of 400G per lambda (3km).With its DMD product line already in volume production, Linktel actively calls upon ecosystem partners to join forces in collaborative development. To drive ecosystem maturity, Linktel is open to sharing its U.S.-filed patents, paving the way for industry-wide adoption and advancements in next-gen AI fabrics.

About Linktel Technologies

Linktel Technologies is a global leader in optical transceiver development and manufacturing, supplying high-speed optical I/O connectivity—including pluggable transceivers, optical engines, and ODM/JDM services.

For more information, visit www.linktel.com or follow Linktel Technologies on LinkedIn.

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Advanced Manufacturing in Focus: Smart Life and Robotics Take the Spotlight at 140th Canton Fair

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GUANGZHOU, China, Oct. 18, 2026 /CNW/ — Phase 1 of the 140th Canton Fair, themed “Advanced Manufacturing,” opened on October 15 and will run through October 19. As one of the event’s most closely watched sections, the Consumer Electronics and Information Products zone once again serves as a showcase for the latest achievements in China’s manufacturing and technology industries.

Smart Life Solutions Address an Aging and Globalized Market

A highlight of this section is the focus on smart living, which brings together smart solutions designed to support healthier, safer, and more independent lifestyles.

For example, wearable devices and continuous glucose monitoring systems enable convenient tracking of key health indicators such as heart rate and blood glucose levels. Meanwhile, wearable exoskeletons and other AI-powered devices support mobility training and functional recovery. For home and elderly care, companion robots, electric nursing beds and home-use hyperbaric oxygen chambers, offer a combination of daily care, wellness support and lifestyle assistance. All these solutions highlight a growing shift toward technology-enabled living, where health management, personal care, and independent living are becoming more accessible and connected.

Service Robots Demonstrate Expanding Real-World Applications

The Service Robot Zone remains one of the fair’s most popular attractions in this edition.

Among the crowd favorites is a humanoid robot standing roughly 70–80 centimeters tall. With its expressive movements and playful appearance, the robot draws constant attention as it dances, approaches visitors and occasionally performs flips to the delight of onlookers. Behind the product is a company that has overcome key challenges in compact joint modules and modular humanoid robot development. Its independently developed high-power-density joints deliver remarkable agility and explosive movement, while modular architecture provides flexibility for a wide range of future applications.

More broadly, the Service Robot Zone features exhibits spanning a wide range of application areas, including embodied intelligence, specialized operations, urban services, and smart living. Full-size humanoid robots, dexterous hands with multimodal sensing capabilities and wheeled robots highlight the latest advances in artificial intelligence, perception technologies and motion control. For demanding industrial and emergency environments, exhibitors are presenting robotic inspection systems, live-line maintenance robots and underwater propulsion robots designed to improve safety and operational efficiency.

The commercial services sector is also well represented through reception robots, façade-cleaning robots and fully automated coffee kiosks, while everyday life applications include wearable exoskeletons, robotic lawn mowers and educational robots for home use. Together, these exhibits demonstrate how China’s robotics industry is accelerating its transition from technological innovation to large-scale commercialization.

As the 140th Canton Fair gets underway, the showcased innovations highlight China’s growing strength in advanced manufacturing, turning cutting-edge technologies into practical, market-ready solutions for global customers.

For more convenient attendance, please click here for pre-registration:
https://buyer.cantonfair.org.cn/register/buyer/email?source_type=16

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