The artificial intelligence industry has now entered the ecosystem-building stage. That ecosystem will most likely be structured to entrench the positions of a small number of frontier AI companies. If my prediction is correct, the instrument for securing those positions will be patents. Over the past few months, there have been notable movements in the AI field — or rather than the field as a whole, movements that look particularly significant from the perspective of someone who works in IP.
SAIL Foundation — The Frontrunners Have Started to Care About Patents
In April 2026, the leading companies in AI established the SAIL Foundation. Companies such as Anthropic, Meta, IBM, and Microsoft formed a community premised on mutual cross-licensing. Leading AI companies forming a coalition is not a sudden development. A presentation we delivered to a major Korean software company in 2018 introduces a foundation called the "Partnership on AI," whose founding members included Amazon, Apple, DeepMind, Google, Facebook, IBM, and Microsoft. Many of the founding members overlap.
<PI IP LAW presentation, January 2018: Introduction to the Partnership on AI>
<PI IP LAW presentation, January 2018: Proposed patent response strategy for a major corporation>
What has changed between then and now is this: while the Partnership on AI functioned as a declaratory consultative body for steering AI toward directions beneficial to humanity, the SAIL Foundation singles out the patent issue specifically — a group with the concrete purpose of providing comprehensive cross-licenses among its members (in other words, an agreement not to fight each other with patents). From the perspective of AI patents in particular, 2018 and 2026 are worlds apart. In 2018, most companies were still learning which parts of AI technology could even be patented — many of PI IP LAW's seminars covered exactly this. As of 2026, the areas across the full AI technology lifecycle where patents arise have become well established, and AI companies have built substantial infrastructure for securing them. The patent fields defined by the SAIL Foundation are divided into (a) foundation models themselves, (b) methods for training models, (c) verification and testing of foundation models or their outputs, (d) interaction between foundation models and internalization of functions within a model, and (e) technologies related to model security and safety — a classification that largely matches how the industry categorizes AI patent technologies.
In our experience, AI researchers in the early 2010s were beneficiaries of open innovation, and a naive view of patents was widespread. The moment we published a column pointing out that the Batch Normalization layer had been patented by Google, it became a talking point across the AI community. Traces of that naive view persist even now: early this year, when Upstage CEO Sung Kim commented that "the Transformer is not patented," my post showing the relevant patent drew dozens of times more attention on LinkedIn and Facebook than any of my other posts. The implication of the SAIL Foundation's establishment — and the fact that it was created precisely with patents as its target — is clear. AI patents are no longer something a business can naively put off. The industry's frontrunners are banding together so that patents do not become blades pointed at one another — and soon, those blades will be pointed outward.
Demis Hassabis's Declaration — Building a Framework to Govern AI
On July 14, 2026, Demis Hassabis — founder of DeepMind and a Nobel laureate in Chemistry — published a long-form post on X titled "A Framework for Frontier AI and the Dawning of a New Age." His message is clear: the birth of AGI is imminent, and a system is needed to manage it in an orderly way.
<Demis Hassabis on X: "A Framework for Frontier AI and the Dawning of a New Age">
Citing the example of the U.S. Financial Industry Regulatory Authority (FINRA), Hassabis proposes establishing an industry-operated independent body under government oversight to guide and manage the development of AI. He proposes that this new institution receive models voluntarily shared by companies thirty days before release, for review. Through this process, Hassabis argues, the new body could serve two functions: 1) standardizing AI technology, and designating frontier models that meet standardized performance criteria — along with the frontier labs capable of developing them; and 2) conducting safety verification of frontier models.
Had this been the moment, over a decade ago, when AlphaGo stunned humanity, Demis Hassabis calling for standardization, regulation, and supervisory oversight of AI would have felt deeply out of place. But AI is no longer a research project for a handful of scientists. Many of you reading this already use AI in your daily life and consume commercial outputs produced by AI. AI has entered the industrial domain, and industry frontrunners like Hassabis are now preparing for the changes its industrialization will bring. The industrialization of AI is a new phenomenon, but industrialization itself is a familiar phase in the technology development cycle. Hints of what will happen as AI industrializes can be found in Hassabis's post — but the trajectory can also be predicted by looking back at how other technologies industrialized, including the role that intellectual property, such as patents, played in that process.
Had this been the moment, over a decade ago, when AlphaGo stunned humanity, Demis Hassabis calling for standardization, regulation, and supervisory oversight of AI would have felt deeply out of place.
Intangible Innovation, Standardization, and Intellectual Property
LTE is an everyday term now, but back in the 2000s when I began my career as a patent attorney, LTE was simply the project name that 3GPP — the body formed to standardize third-generation mobile communications — gave to its preparations for the next generation. The reason I encountered the term (a plain acronym for Long Term Evolution) so early is that I handled patents in that field at a firm specializing in telecommunications standard patents.
In the early days of wireless mobile communications, the agencies responsible for industrial policy in each country adopted mobile technology standards individually. Korea's ETRI commercialized CDMA technology, but it was incompatible with Europe's GSM. In the early 2000s, it was common for business travelers heading to Europe to buy a separate GSM-based phone just to stay in contact with European buyers.
In introducing third-generation mobile technology, 3GPP set out to solve these problems by defining the criteria that 3G technology should satisfy and selecting technologies that met them as the 3G standard. 3GPP's members — national and regional standards bodies from the U.S., Europe, Korea, Japan, and elsewhere, along with private companies developing mobile technology such as Qualcomm, Samsung, and Nokia — adopt the selected standards and develop products and services accordingly. The most intuitive benefit of 3GPP standardization is probably roaming: the ability to use mobile communications seamlessly across carriers.
Standard technologies and patents are bound together under one keyword: SEP (Standard Essential Patent). The member companies of telecom standards bodies devote enormous effort to securing SEPs — patents essential to implementing the standard. They declare their patents as SEPs and then collect royalties from companies that practice the standard.
The combination of standardization and patents gives technology leaders a powerful means of recouping their investment. This is especially true in fields where technological progress is cumulative and the technology itself is intangible — invisible in product form — such as communication protocols or AI software inventions. In these fields, gathering evidence that another party is infringing your patent is difficult: both your patented technology and the other party's infringing acts occur out of sight. Here, the fact that "our company's patents map onto the standardized technology" gives patents in intangible domains powerful wings: the very fact that the other party practices the standard becomes synonymous with the fact that they fall within the scope of your patent rights.
In communications, the technology itself presupposes that both parties use compatible technology, so standardization may have been a natural choice. Through the standardization process, the giants leading the technology wrote their inventions into the standards and, wielding SEPs, collected royalties from the companies benefiting from the technology — naturally completing the food chain of the telecommunications ecosystem.
The Industrial Landscape Drawn by AI's Leading Technologies — Standardization and Regulation Entrench Incumbency
When convolutional neural networks (CNNs) were the mainstream of AI models, even small startups could quickly join the industry's front ranks with excellent researchers and strong execution. Companies founded in the early 2010s that are PI IP LAW's clients — SUALAB, MakinaRocks, VUNO, and Nota — grew along exactly this route.
Things are different now that Transformer-based language models are the mainstream. Transformer models inherently demand far more resources for training and inference than CNNs. The industrialized AI business environment concentrates capital and talent in a handful of companies, and it is becoming ever harder for underdogs to produce results that surprise the industry. Already, the AI ecosystem is being restructured into a few leading companies capable of building foundation models, and smaller companies that create markets in vertical domains by leveraging those models.
In AI, as in telecommunications, the few technology leaders capable of building foundation models need an ecosystem through which to recoup the price of their technology. Telecommunications required standardization because of the technology's inherently two-way nature. AI has no such two-way nature — but it does carry the need for verification to ensure AI produces reliable results, and for regulation and control to keep AI within human oversight. It is no coincidence that the SAIL Foundation names patents on verification, testing, and security of foundation models, or that Hassabis's X post invokes standardization alongside model verification and regulation.
At present, in the contest for hegemony over AI models, the leading companies are releasing their models commercially at relatively low prices while shouldering massive infrastructure investment. As the AI industry matures, that investment must someday be recovered. One natural and proven method available to the frontier companies that built the foundation models is royalty revenue through SEPs.
The very fact that the other party practices the standard technology becomes synonymous with the fact that they fall within the scope of my patent rights.
The Task for Korean Companies — Preparing to Secure Future AI SEPs
Standardization in AI will proceed field by field. Following the fields defined by the SAIL Foundation, the architecture of foundation models, training methods, output verification, and methodologies for foundation model security and safety will be standardized by the few companies capable of developing such models — and the governments of the countries that host them will back this through policy. Even if the precise shape differs from the FINRA-style model Hassabis proposed, it is self-evident that a small number of companies holding leading AI technology — and the small number of countries hosting them — will take the lead and entrench their positions.
From what I have seen in the industry, Korea unfortunately does not yet have a company that would qualify for the frontier labs Hassabis envisions — all the more so since the AI mainstream shifted to Transformer-based models, despite the large-scale investment and support being mobilized to close the gap.
If AI's industrialization advances and a few leading companies build a standardization ecosystem to recoup their investments, Korea could end up handing back a substantial share of the fruits of its AI innovation as SEP royalties — or find itself shut out of overseas markets altogether by a technology stack disconnected from the standards. If the leaders of the AI ecosystem use SEPs as the vehicle for recouping their technology investments, Korean companies can prepare by preemptively securing patents in the technology fields likely to become SEPs.
In particular, technologies for the verification, security, and safety of foundation (frontier) models are a field where Korean companies can build asymmetric patent capability. Designing a foundation model architecture, training it, and testing its performance requires accumulated expertise and enormous resources. But verifying a model's outputs, and AI security and safety technologies, can be designed relatively independently of the model architecture — an area open to attempt even without frontier-grade model design capability.
Moreover, the direction of foundation model verification, security, and safety will inevitably move in step with government regulation — and information about that regulation is open to everyone and carries predictability. The technology and know-how needed to build frontier-grade foundation models are hard to acquire; information on the state and outlook of AI regulation in leading AI nations is comparatively easy to obtain. Indeed, the EU AI Act is now in its implementation phase, mandating 1) watermarking of generative AI content and 2) adversarial testing plus incident tracking and reporting for models posing systemic risk; Korea's AI Framework Act was amended effective July 21, 2026. Leveraging the trajectory of such legislation — and the data disclosed through the legislative process — to secure regulation-related IP can be an alternative route by which Korean companies access overseas markets otherwise closed off by standardization and SEPs.
In addition, Korean companies striving to become future frontier labs — and startups with the technical capability to improve performance by modifying publicly available frontier model architectures — should invest more resources in securing model architecture patents. The fact that frontier labs lead the technology does not mean every company designs AI architectures from scratch. A company with an add-on architecture that improves an existing model's accuracy, or the efficiency of its training or inference, or a design variation on a particular layer, should invest in securing architecture patents through active overseas filings in leading AI jurisdictions such as the U.S. and Europe.
Finally, vertical AI companies should avoid over-limiting the domain when securing rights to their technology, preparing from the initial filing to extend their rights toward generalized technology. Alternatively, a two-track strategy is worth considering: secure domain-limited rights tailored to one's own business field in the initial filing, then pursue broader rights through mechanisms such as U.S. continuation applications. This requires that, at the initial filing stage, the patent specification contain a sufficiently generalized description of the technology to support the later expansion of rights.
I began my career in the 2000s as a patent attorney handling telecommunications standard technologies, and in the 2010s became the head of a patent firm working primarily with AI. Now, in the 2020s, I find myself able to watch the AI industry mature through a perspective of my own. We have spoken about the possibility of standardization in AI since the 2010s, but given the industry's maturity at the time, our argument was perhaps somewhat ahead of its moment. The AI field has now reached the point where an industrial ecosystem is about to take shape, and the recent movements all point in the same direction. I hope this article serves as a prompt for AI companies to respond quickly to what is coming.
