Produced by I Xiahai (fallsea)

Written by I Hu Buzhi

On January 8, 2026, as a hint of chill lingered in the Silicon Valley morning, a brief announcement from OpenAI sent ripples through the venture capital community: The company would acquire the core team of Convogo, an AI executive advisory tool, through an all-stock transaction while explicitly forgoing its intellectual property and technical assets. With Convogo's three co-founders—Matt Cooper, Evan Cater, and Mike Gillett—officially joining OpenAI's 'AI Cloud Business' division, the AI giant, now valued at $500 billion, has finalized nine mergers and acquisitions within a year.

This seemingly routine talent mobility announcement conceals a pivotal turning point in the AI industry's development. As the parameter race for GPT-5 nears its end and the technological gap between large models narrows, 'implementation-oriented talent' capable of transforming model capabilities into industry solutions is becoming a more scarce core asset than patents or products. OpenAI's 'buying talent, not products' acquisition model is not only a precise layout to complement its own business ecosystem but also quietly rewriting Silicon Valley's decade-long M&A rules, marking the beginning of an 'implementation capability rivalry' in the AI industry.

'The core issue we've identified is bridging the gap between the capability leap brought by each new model release and translating that capability into real-world results,' Convogo's team stated in the acquisition announcement, precisely hitting the pain point of the entire AI industry. OpenAI's talent acquisition strategy is a tailored solution to cross this gap. However, behind this 'efficiency tool' lie multiple risks, including innovation stifling, regulatory tightening, and ecological imbalance, which are reshaping the competitive landscape and survival logic of the AI industry.

Why Does OpenAI Insist on 'Buying Talent, Not Products?'

In the AI startup arena, Convogo is hardly a star company. Founded just two years ago, this startup lacks breakthrough large model technology or a user base in the millions, focusing instead on a highly niche vertical—providing automated leadership assessment and feedback report tools for executive coaches and HR teams. Yet, this 'small but beautiful' company became OpenAI's ninth acquisition target within a year and was integrated into its core AI cloud business division.

The answer lies in the core value of Convogo's team: it possesses the scarcest 'transformation capability' in AI implementation, which is precisely OpenAI's most urgent weakness.

Convogo's product logic precisely addresses the pain points of AI implementation in professional service scenarios. In the executive coaching and talent development fields, professionals spend significant time organizing interview records, 360-degree feedback, and survey data to distill core issues and generate structured assessment reports. Convogo's core function automates these repetitive tasks—using AI to identify themes, extract supporting quotes, and compress report work that originally took days into just a few hours.

However, Convogo's core competitiveness has never been these technical functions themselves. OpenAI's GPT models already possess text analysis and generation capabilities. What is truly scarce is Convogo's 'implementation methodology' accumulated in vertical scenarios: How to understand the professional needs of executive coaches? How to translate vague 'leadership assessment' standards into AI-executable algorithmic logic? How to balance automation and human intervention to ensure report professionalism and accuracy? These 'tacit knowledge' embedded in team collaboration experience are the key to bridging the gap between 'model capabilities' and 'real-world results.'

Convogo's inspiration itself is highly representative—founder Matt Cooper's mother, an executive coach, once complained to him that 'report writing consumes too much coaching time.' This authentic industry pain point has kept the team focused on 'building purpose-driven, well-formed applications for professionals' rather than pursuing generalized technical capabilities. This deep insight into industry needs is precisely what technology giants like OpenAI lack.

For OpenAI, the core goal of its AI cloud business is to enable enterprise clients to 'use large models effectively,' not just 'access them.' Currently, Microsoft Azure OpenAI and Google Cloud AI have already established first-mover advantages in the enterprise service market. To break through, OpenAI must address its weakness in 'industry-specific solutions.' The vertical scenario implementation experience brought by the Convogo team provides OpenAI's AI cloud business with a 'reusable industry template'—ranging from leadership assessments in HR to risk reports in finance and case analyses in healthcare. This 'model + workflow' integration capability is irreplaceable by technical parameters alone.

Although specific revenue figures are undisclosed, Convogo has accumulated 'thousands of coach users' and established partnerships with 'world-class leadership development institutions.' This means the team understands not only technical products but also the client communication logic, delivery standards, and trust-building paths in the professional services industry.

In the deep waters of AI commercialization, 'professional service capabilities' are becoming the core of differentiated competition. When enterprise clients procure AI tools, they are no longer satisfied with 'technical feasibility' but demand 'measurable effectiveness and controllable risks.' The Convogo team has directly confronted these practical issues during service delivery: How to explain AI analysis logic to clients? How to handle data privacy and compliance issues? How to iterate product experiences based on client feedback? These frontline practical experiences hold far more value than laboratory technical parameters.

OpenAI's AI cloud business urgently needs this 'professional service gene.' Previously, its enterprise-version GPT models were suspended by multiple enterprise clients due to issues like 'inability to adapt to specific workflows' and 'data security concerns.' The Convogo team's compliance experience in HR—such as handling executive privacy data and meeting enterprise data localization requirements—can provide direct references for OpenAI, reducing client education costs and compliance risks.

Another key logic behind OpenAI's choice to 'buy talent, not products' is risk avoidance. If OpenAI acquired Convogo's products and intellectual property, it would need to undertake subsequent client support, service commitments, and data compliance responsibilities—such as safeguarding existing users' data security, fulfilling uncompleted service contracts, and addressing potential product disputes. These hidden costs and risks are 'unnecessary burdens' for OpenAI, which is eager to advance its AI cloud business.

By acquiring only the team, these risks are completely stripped away. According to the transaction arrangement, Convogo's existing products will gradually cease operations, with user needs migrated with assistance from the original team. This 'clean acquisition' model allows the team to focus on new business ventures while enabling OpenAI to avoid the embarrassment of 'acquiring and inheriting debts.' The all-stock transaction form further reduces financial pressure—for OpenAI, valued at $500 billion, stock payments are more cost-effective than cash acquisitions and can bind the core team's long-term interests.

OpenAI's Capability Puzzle Logic

The acquisition of the Convogo team is not an isolated case but a clear continuation of OpenAI's M&A strategy. Reviewing its nine acquisitions over the past year, a dual-track approach of 'product integration' and 'talent absorption' has gradually emerged. These two paths, while seemingly different, point to the same strategic goal: maintaining leadership in large model technology while rapidly addressing capability gaps in productization, commercialization, and scenario implementation.

OpenAI's M&A strategy can be clearly divided into two categories, forming precise capability complementarity.

The first category is 'product integration' acquisitions, with the core goal of 'technology + talent' bundling to rapidly complement product infrastructure. The most typical case is the $1.1 billion acquisition of product testing company Statsig in September 2025, which not only incorporated its core tools like A/B testing and feature toggles into OpenAI's ecosystem but also appointed CEO Vijaye Raji as CTO of the Applications Division to directly lead product engineering and execution. Another major deal was the $6.5 billion acquisition of AI hardware company io in May 2025, bringing Apple's former Chief Design Officer Jony Ive's team on board to advance next-generation AI hardware (headsets, wearables, etc.) development. These acquisitions aim to acquire mature technical assets and product systems to accelerate infrastructure construction for core businesses.

The second category is 'talent absorption' acquisitions, adopting the 'buying talent, not products' acqui-hire model, as seen with Convogo and Software Applications (Sky team). In October 2025, OpenAI acquired Software Applications, founded by former Apple engineers, without acquiring its Mac-end natural language interface product Sky but instead integrating all 12 core members into the ChatGPT team to strengthen desktop AI experience research and development. The core goal of these transactions is to acquire vertical scenario implementation experience, product methodologies, and collaboration models to rapidly strengthen capabilities in AI cloud, consumer applications, and other businesses.

The synergy between these two models clearly demonstrates OpenAI's strategic intent: with large model technology as the core, it builds infrastructure through product integration acquisitions and fills industry scenario gaps through talent absorption acquisitions, ultimately achieving a full closed loop (closed loop) of 'technology-product-scenario-commercialization.' As of 2025, OpenAI has rapidly covered multiple key areas, including enterprise services, hardware devices, desktop applications, and professional services, through this strategy, with its valuation soaring from $100 billion in 2024 to $500 billion.

OpenAI's frequent adoption of talent acquisition models is essentially driven by the talent supply-demand imbalance in the AI industry. According to a January 2026 report by the AI Workforce Alliance, AI-related roles in G7 countries account for seven of the top ten fastest-growing occupations, with AI risk and governance specialists growing at 234% annually, NLP engineers at 186%, and AI/machine learning engineers at 145%. This explosive growth has led to a critical skills gap—less than 30% of demand is being met, especially for implementation-related skills like large model adaptation, RAG systems, and prompt engineering, where talent reserves are nearly nonexistent.

Traditional headhunting recruitment models can no longer address this dilemma. Top teams' collaboration modes require long-term run in (collaboration), and poaching core members individually cannot replicate their overall capabilities. Meanwhile, market-validated startup teams not only possess ready-made collaboration mechanisms but also bring industry resources and implementation experience, making them the most efficient talent acquisition channels. For OpenAI, exchanging capital for time by rapidly absorbing core teams through acquisitions is the optimal solution to address capability gaps.

Data shows that OpenAI's talent acquisition costs are significantly lower than those for product integration acquisitions. Transaction amounts for talent acquisitions like Convogo and the Sky team are undisclosed but are presumed to be small all-stock deals, while the average cost for product integration acquisitions exceeds $3 billion. This cost difference makes talent acquisitions a 'cost-effective choice' for OpenAI to rapidly expand its capability boundaries.

The AI Talent War Enters the 'Acqui-Hire' Era

OpenAI's acquisitions are not isolated acts but a microcosm of the global AI industry's talent competition. As large model technologies gradually become infrastructure, the industry's competitive focus has shifted from 'who can build better models' to 'who can implement scenarios faster.' Teams with 'technology + industry' composite capabilities have become the core targets for giants. Meta, Google, NVIDIA, and other giants have already joined this 'acqui-hire' wave, pushing industry competition to new heights.

Silicon Valley's 'talent acquisition' model has evolved over a decade, shifting from 'win-win' to 'hollowing out.' When Facebook acquired Instagram in 2012, it retained the 13-person team and allowed the product to operate independently, ultimately achieving a multi-win outcome. However, today's talent acquisitions resemble 'selective absorption'—giants pay hefty fees to take core teams while leaving shell companies and ordinary employees with unvested options behind.

Meta's operations are highly representative: in July 2025, it acquired AI voice startup PlayAI, absorbing only the core team into its AI roles and audio content creation business without acquiring product assets. That same year, it acquired a 49% stake in ScaleAI for over $14 billion, with the core goal of bringing founder Alexandr Wang on board to form a 'superintelligence' team. Google acquired AI programming startup Windsurf's core talent for $2.4 billion in July 2025, integrating CEO Varun Mohan and others into DeepMind to avoid competition with OpenAI. NVIDIA went even further, acquiring 90% of potential competitor Groq's core team through a $20 billion 'technology licensing + talent joining' deal, directly stifling technological disruption.

The logic behind this 'hollowing-out acquisition' is simple: for giants, core teams' implementation capabilities are more valuable than startups' products, while also eliminating potential competitors. For startup core members, joining giants provides more abundant funding, computational power support, and broader business scenarios. However, for ordinary employees and early investors, this means unemployment and diminished investment returns. When Google acquired Character.AI's core team in 2025, it took only 30 core members, leaving 220 ordinary employees with just 18 months of operating funds, sparking industry controversy.

For AI start-ups, accepting the acquisition model of 'buying talent but not products' is often a reluctant decision made after weighing the options. Under the current industry landscape, giants have formed monopolistic barriers with their advantages in computing power, data, and capital, leaving limited room for start-ups to grow independently.

Start-ups like Convogo operating in vertical scenarios face dual survival pressures: on one hand, large model giants may introduce similar functionalities, quickly squeezing the market with their technological advantages and traffic resources; on the other hand, the limited user base in vertical scenarios makes it difficult to sustain ongoing R&D investment and commercial expansion. According to a report by the AI Workforce Alliance, over 90% of AI start-ups fail or are acquired within three years of establishment due to their inability to break through the 'technology implementation bottleneck.' At this point, being acquired by a giant not only allows founders and core teams to receive generous rewards (for example, OpenAI's all-stock transaction is equivalent to binding core members to the growth dividends of a $500 billion valuation) but also enables investors to recoup their principal, making it the least risky exit option.

'Nowadays, the goal of starting a business is not to go public but to catch the eye of a giant.' The sentiment expressed by a CEO of an AI start-up reflects the prevailing mindset in the industry. More and more start-up teams now set 'being acquired by a giant' as their core objective from the outset, deliberately refining their 'implementation capabilities that meet the needs of giants' rather than building independent commercial closures. The spread of this mindset is quietly altering the innovation ecosystem of the AI industry.

Industry Concerns Behind Talent Acquisitions

While the 'buying talent but not products' model appears efficient, it is quietly rewriting the competitive rules of the AI industry, harboring multiple risks such as innovation stifling, cultural clashes, and regulatory tightening. These risks not only concern the development of individual companies but may also impact the long-term trajectory of the entire industry.

When giants 'co-opt' potential competitors through talent acquisitions, disruptive innovation in the industry may be suppressed. Breakthroughs in the AI industry often stem from cross-border attempts by start-ups—for example, OpenAI's early GPT model and DeepMind's AlphaGo both originated from bold explorations by start-up teams. However, today, these most innovative teams are being acquired by giants in their early growth stages, leading to market competition becoming increasingly homogeneous.

Take the AI chip sector as an example: Groq's LPU chip posed a direct threat to NVIDIA's GPUs in terms of inference speed and energy consumption. However, with the core team being acquired by NVIDIA, this potential technological disruption was nipped in the bud. In the AI application layer, more and more vertical scenario start-ups are choosing to 'wait for acquisition' rather than invest resources in technological innovation, resulting in a continuous decline in industry innovation vitality. The AI Workforce Alliance warns, 'If the talent acquisition model continues to proliferate, disruptive innovation in the AI industry may decrease by 60% before 2030.'

The success of talent acquisitions hinges on the cultural integration between the team and the acquirer. However, cultural differences between giants and start-up teams often become the biggest obstacle to integration. OpenAI is known for its research-driven culture, with engineering teams focusing more on technological breakthroughs; in contrast, start-up teams like Convogo possess an agile commercial implementation gene, paying more attention to user needs and market feedback. These differences in value orientation may lead to teams 'not fitting in.'

Historical experience shows a high failure rate in cultural integration by giants. After Google acquired DeepMind in 2014, it took five years to resolve conflicts in research directions and decision-making mechanisms between the two entities. After Meta acquired Scale AI, strategic disagreements repeatedly arose between the superintelligence project led by Alexandr Wang and the foundational research team led by Yann LeCun, causing delays in project advancement. For OpenAI, how to quickly integrate the Convogo team into its AI cloud business and balance technological ideals with commercial goals will be the core challenge in subsequent integration.

The current talent acquisition model is operating in a gray area of antitrust regulation. By using forms such as 'technology licensing + talent joining,' giants avoid the 'concentration of undertakings' reporting threshold in traditional mergers and acquisitions while substantially achieving control over potential competitors. This 'regulatory evasion' tactic has drawn attention from antitrust authorities in the EU and the US.

In July 2024, the EU, along with the US, UK, and other countries, issued a joint statement explicitly focusing on monitoring 'killer acquisitions' by tech giants—that is, acquisitions aimed at eliminating potential competitors—in the AI start-up sector. In January 2026, the EU Court of Justice ruled in the Illumina/Grail case, restricting member states' review rights over 'below-threshold mergers' while promoting the introduction of an 'intervention rights' system—allowing regulatory authorities to review below-threshold transactions with competitive impacts. France, Italy, and other countries have begun implementing relevant reforms, planning to establish a standards-based 'intervention rights' mechanism by the end of 2025, bringing talent acquisitions under regulatory scrutiny.

The US Federal Trade Commission (FTC) is also considering new regulations that may include transactions involving 'core team transfers + substantial business termination' in antitrust reviews. Sources within regulatory agencies have revealed that Microsoft's acquisition of Inflection's core team is under review, and if deemed a 'de facto merger,' it may face penalties such as divestiture or fines. Additionally, data compliance risks cannot be ignored—Convogo has accumulated a large amount of executive privacy information from corporate clients during its operations. Although OpenAI did not acquire its intellectual property, the joining of the core team may pose potential data leakage risks.

AI Mergers and Acquisitions Enter a 'Talent Methodology' Paradigm

The case of OpenAI acquiring the Convogo team marks a shift in the M&A logic of the AI industry from 'asset-oriented' to 'talent and methodology-oriented.' Over the next few years, this trend will continue to deepen, reshaping the industry's competitive landscape and ecological rules.

With the popularization of large model technologies, mere technological patents will no longer be the core targets of M&A. Teams possessing 'model + industry' composite capabilities and mature implementation methodologies will become the primary acquisition targets for giants. Especially in vertical scenarios such as enterprise services, healthcare, finance, and industrial sectors, implementation-oriented teams with industry resources and compliance experience will see their valuations continue to rise.

For OpenAI, subsequent talent acquisitions may focus on expanding scene coverage for its AI cloud business—extending from the HR field to financial risk control, medical diagnostics, industrial manufacturing, and more industries to build a full-scenario solution of 'large models + industry workflows.' Competitors like Meta and Google will also follow suit, making targeted talent acquisitions around their core businesses. The AI Workforce Alliance predicts that from 2026 to 2030, global talent acquisition transactions in the AI industry will grow at an annual rate of 35%, with the proportion of such transactions in overall M&A deals rising from the current 28% to over 50%.

Talent acquisitions will become an important exit path for AI start-ups, forming a tripartite pattern (translated as 'landscape' here to maintain context) alongside IPOs and independent financing. For start-ups in vertical scenarios, if they cannot achieve scaling profitability in the short term, being acquired by a giant as a whole will become the optimal choice. This trend will influence start-ups' financing and development strategies: early-stage projects may focus more on teams' collaborative capabilities and implementation experience rather than pure technological innovation; investors will also pay more attention to the potential for teams to be acquired by giants, focusing on connecting them with giant resources in post-investment management.

Meanwhile, start-ups with unique technological barriers still have the opportunity to grow into independent giants. For example, in niche areas such as AI security and edge AI, if they can develop irreplaceable core technologies, they can avoid being 'co-opted.' However, the proportion of such companies will continue to decline, further highlighting the 'Matthew effect' in the AI industry.

Global antitrust authorities will strengthen regulatory oversight of 'talent acquisitions.' The EU may lead the way in revising the 'Concentration of Undertakings Review Regulation' to include transactions involving 'core team transfers + substantial business termination' in the review scope; the US FTC may also introduce new regulations requiring giants to disclose specific terms and potential impacts of such talent acquisitions. Enhanced regulation will force giants to adjust their acquisition strategies, possibly shifting from 'exhaustive acquisitions' to 'cooperative absorption'—retaining parts of the start-up's business and allowing teams to operate independently within certain limits to acquire core capabilities while avoiding antitrust risks.

Additionally, data compliance regulation will further tighten. The EU's GDPR has already imposed strict requirements on AI data processing, and future regulations may specifically address data transfers in talent acquisitions; major markets such as the US and China will also strengthen control over data security in AI talent flows, requiring companies to establish robust data isolation mechanisms.

Conclusion

The case of OpenAI acquiring the Convogo team, while seemingly a routine talent movement, is actually a landmark event in the development stage of the AI industry. It signifies that industry competition has shifted from an 'arms race of technological parameters' to a 'comprehensive competition of implementation capabilities,' with talent, especially composite teams with scene implementation experience, becoming the core asset in this competition.

For giants, talent acquisitions are an efficient strategy to exchange capital for time, quickly filling capability gaps and eliminating potential competitors. However, the excessive proliferation of this model may lead to a decline in industry innovation vitality and market competition tending toward monopoly. Finding a balance between talent competition and innovation protection will be a challenge for all participants.

For start-ups, being acquired by a giant is no longer a symbol of failure but a rational exit option. However, this does not mean start-ups can only passively wait for 'co-optation'—those with unique technological barriers that are difficult for giants to replicate still have the opportunity to grow into independent industry giants.

The ultimate outcome of the AI industry will not be a monopolistic landscape formed through talent acquisitions by giants but a dynamic balance between technological innovation and commercial implementation capabilities. When model technologies become infrastructure, implementation-oriented talent and teams that can truly solve industry pain points will ultimately receive recognition for their value. OpenAI's talent acquisition battle is just a microcosm of this prolonged competition. In the future, only those companies that can master core technologies while precisely meeting industry demands will ultimately prevail in the AI era.