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Jeff Dean leaves Google after 27 years – Why Google is consolidating its AI leadership in Silicon Valley

On August 6th, Google announced a major personnel restructuring that consolidated the command structure of its AI organization in Mountain View. Demis Hassabis became Chairman of Google DeepMind and Chief Scientist of Alphabet, Kolay Kavquall will oversee all AI research and operations, and Jeff Dean, employee number 30, will leave after 27 years to start his own AI startup. This article explains these changes in the context of the unresolved geographical fragmentation that arose before the 2023 integration of Google Brain and DeepMind.

Jeff Dean leaves Google after 27 years – Why Google is consolidating its AI leadership in Silicon Valley
(Photo: illustrative)

Jeff Dean Leaves After 27 Years – Why Google is Consolidating its AI Command in Silicon Valley

On August 6th, it was revealed that Google had significantly restructured its AI organization's command structure. DeepMind co-founder Demis Hassabis stepped down from day-to-day operations to become Chairman of Google DeepMind and Chief Scientist at Alphabet, while Jeff Dean, a Google employee number 30 and a longtime veteran, left after 27 years to launch his own AI startup. As an engineer, I want to examine the organizational structure issues behind this personnel restructuring.

An Organization with "Two Capitals"

The key to understanding this restructuring lies in a merger Google undertook in 2023. At that time, Google merged two powerful AI research organizations, "Google Brain" based in Mountain View, Silicon Valley, and "DeepMind" based in London, which had previously operated independently, under the single banner of "Google DeepMind." However, even though the organizational chart showed a unified structure, in reality, researchers continued to work separately at two locations across the Atlantic Ocean. Hassabis led the integrated organization from London, while Dean remained in Silicon Valley—both reported directly to CEO Sundar Pichai. This meant that even with a unified organizational chart, two de facto "capitals" remained.

A "Shift in Center of Gravity" to Mountain View

With this restructuring, Kolay Kavkull will now oversee all AI research and operations. He moved from London to Mountain View within the past year and has a long history of steadily climbing the leadership ladder within DeepMind, having founded the company's deep learning team. He was also promoted to Google's "Chief AI Architect" last summer.

Furthermore, Sebastian Borgaard, who led key initiatives in the field of AI coding, also moved from the UK to California. Furthermore, several other individuals have moved completely away from the London headquarters. This series of moves can be seen as a clear indication of Google's deliberate decision to shift its organizational focus to Mountain View.

Evaluation from a Deep Learning Pioneer

Yann LeCun, Kavúll's doctoral advisor and a renowned pioneer in deep learning, describes him as "an excellent engineer who has cultivated outstanding research management skills throughout his career at DeepMind." LeCun further adds that Kavúll's early research helped spread the recognition in the industry that AI could fundamentally change the way language is processed.

The fact that someone with a solid technical track record climbed the organizational ladder relatively quietly and reached the top at this time can be interpreted as an indication that Google's AI organization is shifting its focus from "flashiness" to "execution."

A Former DeepMind Employee Speaks: "The Final Outcome of the Integration of Brain and DeepMind"

Regarding this restructuring, a former employee who spent over 10 years at Google DeepMind commented, "This is the final outcome of the integration of Brain and DeepMind." The initial concept was simple—"one company, one AI army, and reduced internal conflict." However, the reality was far more complex.

Hassabis led the integrated organization from London, while Dean remained in Silicon Valley. While there was a single command structure on the organizational chart, in reality, there were two "capitals." This restructuring is seen as finally resolving this long-simmering geographical tension.

In the Context of Countering Anthropic and OpenAI

This organizational restructuring is driven by intensifying competition with rivals Anthropic and OpenAI. According to Bloomberg, Google is consolidating its AI leadership in Mountain View to accelerate decision-making and secure a stronger position in the competition with the other two companies.

A geographically dispersed decision-making hierarchy creates communication overhead and slows down decision-making—this is something that engineers with experience leading distributed teams in software development will intuitively understand. In the accelerating race to develop frontier models, Google's choice to centralize decision-making is a logical decision, both from an organizational and technological standpoint.

What Engineers Should Consider

Jeff Dean's departure has more significance than simply the departure of a prominent engineer. He was Google employee number 30 and embodied the company's technological culture for many years. The news that he is launching his own AI startup after 27 years is likely to be seen as a symbolic event in the Silicon Valley AI talent market, representing a shift in talent from large corporations to startups.

For those leading technology organizations, the Google case offers a subtle but important lesson: a "consolidated organizational chart" and a "decision-making process that actually works" do not always coincide. The fact that it took three years from the organizational chart's integration to finally unify the geographical center of gravity illustrates just how time-consuming organizational restructuring can be in large corporations.

GoogleDeepMind組織再編AI/ML人材

The obligation to identify as "talking to an AI" has begun—Article 50 of the EU AI Act questions the technical aspects of transparency.

The EU AI Act's transparency requirement (Article 50) came into effect on August 2nd. This article explains, from a technical perspective, four different regulations—the obligation to disclose chatbots, deepfakes, machine-readable marking, and AI-generated text; two technically different types of labeling (visible marking and machine-readable marking); a grace period until December 2026; a code of conduct signed by over 180 companies; and penalties of up to €15 million.

The Obligation to Disclose "You Are Talking to an AI" Begins: The Technical Substances of Transparency Under Article 50 of the EU AI Act

On August 2nd, Article 50 of the EU's Artificial Intelligence Regulation (AI Act), concerning transparency obligations, came into effect. Chatbots must clearly indicate to users that they are AI, and AI-generated and processed content must be marked in a machine-readable format. As a journalist with a background in AI research, I want to carefully examine the technical aspects of this regulation and what it will actually change.

A Regulation with Four Different Obligations Coexisting

Article 50 is not a single rule, but rather a combination of four different obligations depending on the type of system covered.

The first is the "obligation to disclose that it is an AI" for AI systems that directly interact with people (chatbots, voice assistants, AI agents, digital avatars, etc.). Unless it is self-evident, users must be informed that they are interacting with an AI.

The second requirement is a "disclosure obligation" for deepfakes (images, videos, and audio generated and processed by AI to resemble real people, places, or events).

The third requirement is a "machine-readable marking obligation" for AI-generated and processed content, requiring the incorporation of technical mechanisms to enable platforms and automated tools to detect the source of the content.

The fourth requirement is a disclosure obligation when publishing AI-generated text on matters of public interest that has not undergone human review or editing supervision.

"Visible Disclosure" and "Machine-Readable Marks" are Different Things

Understanding these rules accurately requires understanding the difference between two types of labeling. One is "visible disclosure"—a form of display directly seen by humans, such as a chatbot explicitly stating "I am AI" or on-screen annotations in videos. The other is "invisible marking"—machine-readable identification information embedded within a file, used by platforms and tools to automatically detect AI-derived content.

The regulations, which come into effect on August 2nd, primarily mandate "visible markings." However, for "machine-readable markings," a grace period of December 2nd, 2026, is provided for generative AI systems already on the market before August 2nd. Standardizing these "invisible marks" is key to enabling large-scale content detection, and technical specifications are still under development.

Scope Depends on Whether Products are Provided to the EU

Another important feature of these regulations is their broad scope. The AI ​​Act applies to all providers, implementers, importers, and distributors of AI systems in the EU market, or whose AI output is used within the EU. This means that many companies worldwide, including US companies serving EU users, are subject to these regulations, regardless of their headquarters location.

The penalties for violations are also significant. Companies could face fines of up to €15 million (approximately $16 million) or 3% of their global annual revenue, whichever is higher.

Over 180 Companies Sign the Code of Conduct

At the same time as the implementation of the new regulations, the European Commission published a list of organizations that have signed the "Code of Conduct on Transparency of AI-Generated Content." To date, over 180 organizations have signed. By operating in accordance with this code of conduct, companies can obtain a certain "presumption" from regulators that they are fulfilling their marking and detection obligations.

The code of conduct also includes a set of icons for identifying AI-generated content, aiming to make it easier for users to distinguish AI-generated content through a unified visual representation.

"Past Content" Not Subject to Retroactive Application

In practice, a point of concern for many companies is that content generated and published before August 2nd is not required to be retroactively labeled. However, there is a somewhat complicated provision that even if the content was generated before August 2nd, if it was published after August 2nd, it will be subject to the labeling obligation.

The distinction between using the "generation date" and the "publication date" as the basis for judgment is likely to be a practically sensitive issue for companies operating content management systems.

Points to Note from a Researcher's Perspective

The more fundamental technical challenge posed by this rule is that the goal of "making AI-generated content detectable" is not as simple as it sounds. Machine-readable marking of text is known to be technically more difficult than watermarking of images and audio, and there remains the question of how well the marking will be maintained after AI-generated text has been edited by humans.

This rule merely mandates "display," and does not guarantee the "accuracy of the detection technology itself." How standardization bodies and the research community solidify the specific technical specifications for this machine-readable marking, and how effective that will lead to detection, will remain key technical issues to watch as the grace period ends in December 2026.

EU AI Act政策AI規制透明性AI/ML

OpenAI, the "frontrunner," remains unchanged in valuation, while Intel, the "supporting player," saved the earnings report—SoftBank's earnings report reveals the reality of AI investment.

SoftBank Group announced its first-quarter results for fiscal year 2026 on August 6, reporting a net profit of 347.3 billion yen, exceeding market expectations. While the 850 billion yen valuation gain from a 216% rise in Intel shares drove the results, the company also accounted for the fact that there was no valuation gain or loss on OpenAI (fair value of $89.6 billion), a 65% decrease in Vision Fund investment income, cumulative OpenAI investments reaching $44.6 billion, and a doubling of financial costs due to increased Arm personnel and the acquisition of Ampere.

OpenAI, the "Frontrunner," Remains Flat in Valuation, While Intel, the "Supporting Player," Saves the Earnings—SoftBank's Earnings Reflect the Reality of AI Investment

On August 6th, SoftBank Group announced its financial results for the first quarter of fiscal year 2026 (April-June). While net profit fell 18% year-on-year to 347.3 billion yen (approximately $2.2 billion), it significantly exceeded market expectations. What's interesting from an accountant's perspective is that this "exceeding expectations" was driven not by OpenAI, the company's "frontrunner" in AI investment, but by Intel, which had been positioned as a supporting player.

The "Unexpected Key Player" Supporting the Earnings

Investment gains for the quarter totaled 1.86 trillion yen, nearly tripling from 487 billion yen in the same period last year. The biggest factor was the 1.33 trillion yen (approximately $8.5 billion) in valuation gains from holding Intel shares. SoftBank acquired approximately 2% of Intel shares at around $23 per share in 2025. Intel shares have surged by approximately 216% year-to-date—more than three times their initial value—resulting in an unrealized gain of $8.5 billion in this quarter alone.

On the other hand, regarding OpenAI, which the company has positioned as the "core of its AI strategy," the valuation gain/loss for this quarter was reported to be zero, meaning there was no change in its valuation. OpenAI's fair value remains unchanged at $89.6 billion as of the end of March 2026. The fact that investment in semiconductors, a "mature technology," contributed more significantly to this quarter's results than investment in OpenAI, which is considered a symbol of the AI ​​industry, is highly suggestive from an accounting perspective.

Vision Fund Investment Profits Down 65%

Another figure to note is the investment profits of the Vision Fund business. The business alone recorded investment profits of 255.7 billion yen (approximately $1.6 billion), a 65% decrease compared to the same period last year. According to the company's explanation, the decline in the valuation of its portfolio companies was the cause.

For SoftBank as a whole, the sharp rise in Intel's stock price, which could be considered a temporary factor, boosted the overall earnings. This structure is a typical pattern to be aware of when evaluating the earnings of AI-related investment companies—when you break down the earnings figures that are presented as "results of AI investment," it is often the case that the main cause is not the investment return on the AI ​​company itself, but rather the stock price fluctuations in the adjacent semiconductor field.

Cumulative investment in OpenAI reaches $44.6 billion

According to the earnings report, SoftBank invested an additional $10 billion each in OpenAI in April and July. This is part of a total additional investment plan of $30 billion, and as a result, SoftBank's cumulative investment in OpenAI has reached $44.6 billion. Once the next investment scheduled for October is completed, the cumulative investment is expected to reach $64.6 billion, and its shareholding ratio will be approximately 13%.

From an accounting perspective, the key point to note here is the combination of the sheer size of this cumulative investment and the aforementioned "zero unrealized gains/losses" result. The fact that the valuation remains unchanged at the quarterly level despite accumulating a massive investment of $44.6 billion means that, depending on the timing of the investment, it could be in a "flat" state with neither unrealized gains nor losses. The actual return on this massive investment will largely depend on whether OpenAI conducts an IPO in the future and how its valuation is determined at that time.

Side Effects: Increased Financial Costs

SoftBank's aggressive AI investment strategy also comes with cost side effects. The company's financial costs have reportedly effectively doubled due to personnel expansion costs at Arm Holdings, costs related to the acquisition of Ampere Computing, and the impact of exchange rate losses. The use of bridge loans to finance the investment in OpenAI is also considered a contributing factor to the increase in financial costs.

In other words, SoftBank is facing a dual structure: while its overall earnings figures are boosted by the windfall profits from the surge in Intel's stock price, the borrowing costs for continuing AI investments are steadily accumulating.

Points to Note from an Accountant's Perspective

CEO Masayoshi Son has consistently positioned AI as the company's core strategy, and the strong quarterly earnings can be seen as providing him with the financial capacity to further aggressively pursue AI-related investments. Net asset value (NAV) has also reached a record high of 72.3 trillion yen, expanding the company's valuation base as an investment firm.

However, from an investment decision-making perspective, it would be premature to interpret a headline stating "AI investment firm's earnings were good" as evidence that investment in AI companies itself is progressing smoothly. As in this case, when the factors driving up earnings are broken down, it's not uncommon to find that the price movements of semiconductor stocks surrounding the AI ​​boom are the main cause. When investing in privately held AI companies like OpenAI, it will be necessary to continuously monitor how the actual unrealized gains and losses are moving, and how that valuation will be verified in a future IPO.

SoftBank決算OpenAIIntelファイナンスAI投資
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