137Labs Global

Introduction
Over the past two years, market discussions about artificial intelligence have focused primarily on whether model capabilities can continue to improve, whether generative AI can become a general-purpose technology comparable to the internet and smartphones, and how large language models may transform industries such as search, software, advertising, and enterprise services; however, as model performance has advanced, user adoption has accelerated, and global computing infrastructure has entered an intensive construction phase, investor attention has shifted increasingly toward commercial returns, with the central question now being whether the enormous amounts of capital that major technology companies are investing in AI can ultimately be converted into stable revenue, profits, and free cash flow.
In its report, Why AI Companies May Invest More than $500 Billion in 2026, Goldman Sachs noted that the consensus forecast for 2026 capital expenditure among leading AI hyperscalers had been raised from $465 billion to $527 billion, while broader estimates suggest that global AI infrastructure investment could continue to expand rapidly over the coming years. As capital moves beyond chips and servers into data centers, electricity generation, cooling systems, and network infrastructure, AI is no longer simply a cycle of software and semiconductor innovation, but is increasingly becoming a global restructuring of capital across energy, industry, finance, and infrastructure.
I. Why AI Capital Expenditure Continues to Rise
The reason major technology companies continue to increase AI investment despite pressure on free cash flow, concerns about valuation, and periods of weak share-price performance is that they do not view AI as an ordinary product investment, but as a strategic commitment that may determine leadership in the next decade of computing platforms, cloud services, and software ecosystems, which means their greatest concern is not whether they spend several billion dollars too much in a particular year, but whether they lose their competitive position as the next generation of technological infrastructure takes shape.
Microsoft, Amazon, Alphabet, and Meta are not facing a conventional cost-benefit decision, but a strategic contest in which any company that slows investment first risks seeing cloud customers move to competitors with greater computing capacity, developers migrate toward rival ecosystems, and its own capabilities in model training, inference, and product development gradually fall behind.
A substantial portion of current AI capital expenditure therefore has a defensive character. Even though technology companies have not yet proved that every dollar invested in AI will generate an attractive return, they generally believe that the long-term cost of failing to invest may be greater. Once all leading companies reach a similar conclusion, the industry enters a competitive structure resembling a prisoner’s dilemma, in which every participant would prefer its rivals to reduce spending, yet no market leader is willing to withdraw first.
At the same time, the market’s understanding of computing demand is also changing, because AI infrastructure investment initially focused mainly on training large models, whereas the emergence of AI agents is now making inference demand an increasingly important source of growth. A conventional chatbot may require only one or several model calls to answer a user’s question, but an AI agent performing a genuinely complex task may need to break down objectives, search for information, compare alternatives, invoke tools, revise its plan, and repeatedly verify its output, meaning that its consumption of tokens and computing resources may be several or even dozens of times greater than that of a standard question-and-answer interaction.
Goldman Sachs estimates that, driven by the adoption of AI agents by both consumers and enterprises, global token usage could increase twenty-fourfold between 2026 and 2030, reaching 120 quadrillion tokens per month. The logic behind this forecast is that AI may no longer remain a tool that users open occasionally, but could gradually become a persistent execution system embedded in office software, customer service, advertising, financial analysis, e-commerce, software development, and industrial processes.
If this transition occurs, the main driver of future computing demand will shift from a limited number of companies periodically training large models to hundreds of millions of users and enterprises continuously running intelligent agents, while inference computing will increasingly resemble the recurring consumption patterns of cloud computing, electricity, and telecommunications traffic. As a result, even if training efficiency improves or individual models are trained less frequently, total demand for computing resources across the economy may still grow rapidly.
Another Goldman Sachs study suggests that improvements in chip performance, model compression, and data-center architecture could reduce the unit cost of AI inference per token by 60% to 70% annually, but lower unit costs do not necessarily imply lower total industry spending, because technological history repeatedly shows that when a resource becomes cheaper, its range of uses and total consumption can increase rather than decline, and AI may follow the same pattern.
As inference costs continue to fall, applications that were previously uneconomic, including automated customer service, real-time video generation, personalized education, software development agents, and enterprise process automation, may become suitable for large-scale deployment. The key variable determining the future scale of AI capital expenditure is therefore not the price of an individual token, but whether growth in usage can continue to exceed the decline in unit cost.
II. Why the $527 Billion and $765 Billion Estimates Reflect Different Measurement Frameworks
To understand Goldman Sachs’ assessment of AI investment, it is necessary to distinguish between the statistical scope of the $527 billion and $765 billion figures. The former refers mainly to the 2026 consensus capital expenditure forecast for leading AI cloud companies such as Microsoft, Amazon, Alphabet, and Meta, including spending on servers, data centers, networking equipment, and other capital projects; the latter comes from Goldman Sachs’ broader AI infrastructure model, which includes not only direct investment by technology companies, but also the data-center buildings, electricity supply, cooling systems, and other infrastructure required to support those computing systems.
The $527 billion figure is therefore closer to the annual capital budgets of leading technology companies, while the $765 billion estimate more closely represents the annual construction cost of the entire AI infrastructure ecosystem, and the projected $7.6 trillion reflects potential cumulative investment between 2026 and 2031.
Although chips receive the greatest attention within AI infrastructure, they are only the starting point of the system, because a large-scale AI computing cluster also requires server racks, high-speed networking, optical components, liquid-cooling systems, uninterruptible power supplies, transformers, transmission lines, backup generation, and the data-center buildings needed to house and operate all of these assets. Every new deployment of AI accelerators therefore tends to require system-wide investment that may be several times greater than the value of the chips themselves.
Some of the baseline assumptions used in Goldman Sachs’ model include approximately 3,000 watts of package power for an AI accelerator, a data-center power usage effectiveness ratio of around 1.2, data-center construction costs of roughly $15 million per megawatt, and incremental power-infrastructure costs of approximately $2,500 per kilowatt. These parameters indicate that competition in AI is no longer limited to chip design and model development, but is increasingly becoming a broader contest involving land, electricity, financing, supply chains, engineering capacity, and long-term operating expertise.
III. Why Semiconductor Companies Are Already Profitable While the Broader AI Industry Has Yet to Prove Its Returns
So far, the clearest and most concentrated economic value in the AI industry has emerged among semiconductor companies and related equipment suppliers, because major technology companies must purchase chips, build data centers, and complete networking and power infrastructure before AI businesses generate stable revenue, while chip suppliers can recognize revenue and profit shortly after delivery. By contrast, cloud providers and model developers that purchase these chips must recover their investment over many years through cloud services, software subscriptions, improvements in advertising efficiency, or enterprise AI products.
The result is that profits across the AI value chain are currently concentrated among upstream chip and equipment suppliers, while cloud platforms, model companies, and enterprise customers remain in the investment and commercialization-validation stage, meaning that the industry as a whole has not yet formed a broad and stable cash-flow cycle.
Goldman Sachs has noted that although semiconductor companies are generating record levels of revenue and profit, many other participants in the AI ecosystem have not yet achieved returns commensurate with their investment, and a structure in which upstream suppliers make money first while downstream customers continue to spend cannot be sustained indefinitely. A healthy value chain must ultimately be supported by end customers that use AI to increase revenue, reduce costs, or improve efficiency, because only when profits gradually spread from chip manufacturers to cloud platforms, software companies, and end applications will customers be able to keep increasing purchases and drive the industry into a stable positive cycle.
The consumer market has delivered extremely rapid AI adoption, but user growth cannot be equated directly with commercial value. Research cited by Goldman Sachs suggests that generative AI reached an adoption rate of approximately 53% within three years of the launch of the first widely available product, significantly faster than the early adoption of personal computers or the internet, yet many users rely on free products, and even when some consumers pay subscription fees, that revenue may not be sufficient to cover model training, inference, electricity, data centers, and research and development.
The customers most capable of supporting trillions of dollars in AI infrastructure investment are therefore likely to be enterprises, because businesses have greater purchasing power and operate large-scale processes in customer service, sales, research and development, finance, and supply chains, although enterprise AI deployment is far more difficult than consumer use of a chatbot. Many companies initially believed that purchasing the most advanced model would automatically generate productivity gains, but in reality their internal data is often fragmented across different systems, with inconsistent formats, unclear permissions, and uneven quality. If inventory, membership, order, and recommendation data remain disconnected, even a highly capable model may struggle to produce stable and reliable business outcomes.
The key constraint on enterprise AI adoption is therefore no longer model capability alone, but whether companies can complete data governance, model orchestration, and business-process redesign. Enterprises are likely to use different models according to task complexity, cost, data security, and risk, while also establishing permission controls, human review, and output-tracing mechanisms, which means enterprise AI is not simply a matter of purchasing a software license, but a long-term project involving system redesign, compliance review, and organizational change.
Goldman Sachs estimates that only around 12% of knowledge workers may use AI agents by 2030, with the figure potentially rising to 37% by 2040, suggesting that infrastructure investment may advance significantly faster than the realization of enterprise commercialization returns.
IV. Is AI Investment Becoming a New Bubble?
Comparing the current AI boom with the internet bubble of the late 1990s has become one of the most common market frameworks, because both periods have involved rapid increases in capital expenditure, highly optimistic assumptions about future demand, companies building infrastructure in advance out of fear of missing a technological revolution, and valuations that depend heavily on revenue and profit expected many years into the future. Nevertheless, concluding from these similarities alone that AI must repeat the dot-com collapse would also be overly simplistic.
Today, the main AI investors are global technology companies with substantial operating cash flow, mature business models, and strong balance sheets, whereas many telecommunications and internet companies during the dot-com era relied heavily on leverage and had not yet established stable revenue or profits. The current AI investment cycle may therefore be less likely to end in a broad systemic collapse, although that does not eliminate the possibility of capital misallocation and structural overcapacity.
The first risk is that investment may move faster than commercialization. Data centers, chips, and power facilities can be built within several years, whereas enterprise AI revenue, process redesign, and organizational transformation may require much longer to mature. If supply is created well in advance of effective paying demand, returns on capital may remain under pressure for an extended period.
The second risk concerns the uncertain economic life of AI chips. Goldman Sachs has identified accelerator lifespan as one of the most important variables determining cumulative AI investment, because AI accelerators are generally expected to operate for four to six years, while newer generations may replace them rapidly with higher performance and lower unit costs. By comparison, data-center buildings may be depreciated over approximately twenty years, while power infrastructure can remain in use for twenty-five years or longer. If chips require frequent replacement, technology companies will face not only recurring investment requirements, but also greater depreciation pressure and a higher risk of equipment obsolescence.
The third risk lies in electricity and engineering constraints, because even after companies purchase large volumes of chips, those assets cannot operate efficiently if data centers fail to obtain power connections on schedule, or if transformers, transmission lines, generation equipment, and cooling systems are not built at the same pace. The main bottleneck in AI development may therefore shift gradually from chip shortages to electricity, land, regulatory approvals, and construction capacity.
Beyond industrial investment risk, capital markets also face the danger of excessive valuation expansion. Goldman Sachs research showed that its basket of AI infrastructure stocks at one point generated an average year-to-date return of approximately 44%, while consensus earnings-per-share forecasts for those companies over the following two years increased by only around 9%, indicating that a substantial portion of share-price appreciation came from multiple expansion rather than corresponding upgrades to profit forecasts.
For valuations to continue rising, companies would need to deliver earnings well above expectations, receive even higher valuation multiples, or demonstrate stronger market power and pricing ability. If capital-expenditure growth slows or downstream customers reduce orders, companies whose share prices have risen much faster than their earnings expectations may face more severe valuation compression.
V. AI Investing Is Moving from Thematic Trading to Fundamental Differentiation
Goldman Sachs found that the average share-price correlation among major publicly listed AI hyperscalers fell from roughly 80% to 20%, indicating that the market no longer treats all AI companies as part of a single trading theme, but is increasingly distinguishing among them according to capital expenditure, financing structure, revenue realization, and cash-flow quality.
During the early phase of the AI rally, investors were mainly concerned with whether a company belonged to the AI value chain, but as valuations and investment levels rose, the market began asking more demanding questions, including whether capital expenditure had already produced revenue, whether a company depended on debt financing, whether customer demand was stable, and whether its business model had a clear path to profit realization. AI investing is therefore moving from a broad thematic trade toward more rigorous fundamental selection.
Future market analysis is likely to focus heavily on the relationship between capital expenditure and AI revenue, because rising investment is not necessarily a problem if revenue and profit expand at the same or a faster rate. Investors will also monitor free cash flow, since a company can continue to report profit growth while free cash flow declines if capital expenditure rises more quickly. Financing structure will become increasingly important as well, because funding investment through operating cash flow creates a very different risk profile from relying on debt to build data centers.
Computing utilization will also determine whether data-center investment can generate adequate returns, because the number of installed servers does not itself represent effective demand. Only when computing assets maintain consistently high utilization can companies cover depreciation, electricity, and maintenance costs, while businesses with cloud platforms, developer ecosystems, proprietary data, and stable enterprise relationships are more likely to convert infrastructure advantages into recurring revenue and customer lock-in.
VI. The Next Winners May Extend Beyond Semiconductor Companies
Goldman Sachs believes that after semiconductor companies captured the first major share of AI profits, the next stage of value creation may gradually shift toward hyperscale cloud providers, AI platforms, and companies that benefit from productivity gains, because the market already understands the capital-expenditure burden facing technology giants, but may underestimate the profit potential associated with rising AI cloud revenue and declining unit costs.
As inference efficiency improves and usage expands, cloud platforms may be able to achieve both revenue growth and lower unit costs, gradually developing economies of scale similar to those of traditional cloud computing. This suggests that platform companies with substantial computing resources, established customer bases, and broad software ecosystems may generate more stable returns as AI commercialization matures.
At the same time, the orchestration layer connecting enterprise data, business processes, and different models may become a new center of value creation. Platforms capable of providing data integration, model routing, cost control, permission management, and compliance auditing could assume an infrastructure role comparable to databases and middleware in the cloud-computing era, while earning stable revenue through high switching costs.
The economic value generated by AI may also accrue not only to AI suppliers, but to companies that use AI to improve their core operations. When an advertising company uses AI to improve conversion rates, a logistics company applies AI to reduce transportation costs, or a software company shortens development cycles through AI-assisted coding, the resulting value may appear directly in the income statements of those users. The next phase of AI investing should therefore focus not only on companies selling AI, but also on businesses capable of using AI to redistribute industry profits, expand market share, or improve cost structures.
AI infrastructure investment will also create opportunities for many industries outside traditional technology. Goldman Sachs estimates that major technology companies could spend a cumulative $5.3 trillion on related capital expenditure between 2025 and 2030, up from an earlier forecast of $4.5 trillion, and a construction program of this scale cannot be financed entirely from technology companies’ internal cash flow.
Data-center developers, power companies, equipment suppliers, infrastructure funds, real-estate capital, and credit markets will all become important participants. As of September 2025, global infrastructure funds managed more than $1.7 trillion in assets and held approximately $400 billion in unallocated capital, while related assets under management could exceed $3 trillion by 2030, suggesting that AI may become an important growth driver for private markets, infrastructure finance, and corporate bond issuance over the coming years.
VII. How to Determine Whether AI Capital Expenditure Will Ultimately Succeed
Assessing whether the current AI capital-expenditure cycle is sustainable requires more than examining chip sales, server orders, or data-center construction, because the analysis must consider demand, revenue, unit economics, returns on capital, and the distribution of profits across the value chain.
Investors must first determine whether AI user growth, enterprise adoption, and token usage continue to expand, while also assessing whether cloud platforms, model companies, and software providers can convert usage into paid revenue, and whether the income generated by each inference task or AI application is sufficient to cover chip depreciation, electricity, networking, maintenance, and research and development. Only when genuine demand, commercial revenue, and viable unit economics exist simultaneously can infrastructure investment receive durable support.
Investors must also evaluate whether the incremental operating profit generated by AI exceeds the cost of the capital required to build the supporting infrastructure, because revenue growth does not necessarily create value if each dollar of AI revenue requires more than one dollar of incremental investment.
Ultimately, the maturity of the AI industry will depend on whether the entire value chain forms a stable commercial cycle rather than on whether chip suppliers alone continue to earn high profits. If model companies, cloud platforms, and enterprise customers can use AI to generate sustainable revenue and improve cash flow, current capital expenditure may be converted into long-term economic value; if downstream companies remain dependent on continuous investment without producing adequate returns, the sustainability of the entire cycle will come into question.
Conclusion
Goldman Sachs’ forecast of more than $500 billion in AI capital expenditure reveals not only how much technology companies may continue to spend, but also that the global economy is entering a new investment cycle driven jointly by computing capacity, data centers, and power infrastructure. Historical experience suggests that major technological revolutions often produce genuine productivity gains and temporary capital misallocation at the same time, as railways, the internet, and fiber-optic networks all changed the world while imposing heavy losses on investors whose projects lacked viable commercial returns. The central uncertainty surrounding AI is therefore no longer whether the technology will continue to advance, but which companies will be able to convert technological advantages into durable revenue, profits, and cash flow.
From this perspective, $500 billion may represent both the beginning of a new cycle and the first dividing line in the market’s assessment of AI’s commercial viability, while the ultimate success or failure of this investment wave will depend not on the absolute size of capital expenditure, but on whether those investments can generate long-term economic value sufficient to cover their cost of capital.

Han pasado tres días desde que el gigante surcoreano de la memoria, SK Hynix, hizo su debut en la lista de EE. UU. Este artículo repasa el evento desde cuatro ángulos clave: los detalles de la cotización y las fortalezas empresariales de SK Hynix, el apoyo derivado del aumento del capex en IA para el sector de la memoria, el desglose de Goldman Sachs de la cadena de valor de la IA y el valor estratégico de SK Hynix con ideas de cara al futuro.
SK Hynix fijó el precio de sus ADR en 149 dólares. Las operaciones comenzaron bajo SKHYV y más tarde se cambiaron a SKHY. La empresa recaudó un récord de 26.500 millones de dólares en la mayor OPI de EE. UU. de una compañía extranjera. Sus ADR se dispararon aproximadamente un 13% el primer día, impulsando la capitalización bursátil por encima de 1,2 billones de dólares. Los fondos se destinan principalmente a ampliar las fábricas de obleas en Corea y a adquirir equipos avanzados de litografía EUV para acelerar la expansión de capacidad de HBM. Como proveedor clave de NVIDIA y otros aceleradores de IA de alta gama, sus productos ya ocupan una posición crítica en las cadenas de suministro globales de centros de datos.

I. El ciclo de las tasas de interés que creó un nuevo mercado
Si nos remontamos a 2021, el concepto de activos del mundo real, o RWA por sus siglas en inglés, aún era en gran medida teórico. Se refería al proceso de representar activos de la economía tradicional en blockchains y permitir que se emitieran, mantuvieran y transfirieran en forma tokenizada. En ese momento, sin embargo, la idea todavía no había evolucionado hasta convertirse en un mercado comercial convincente. Por un lado, la Reserva Federal había mantenido las tasas de interés cerca de cero durante años, mientras que los rendimientos de las letras del Tesoro de EE. UU. a corto plazo permanecían por debajo del 1%, dejando muy pocos ingresos de bajo riesgo en las finanzas tradicionales que fueran lo bastante atractivos como para migrar a la cadena. Por otro lado, la industria cripto se expandía rápidamente, con DeFi, NFTs, GameFi y otros sectores especulativos generando retornos dramáticos. Los inversionistas estaban mucho más interesados en activos que pudieran subir diez veces o incluso cien veces que en productos que ofrecieran apenas unos cuantos puntos porcentuales de ingreso anual. En esas condiciones, los RWA parecían más una dirección tecnológica que un negocio financiero viable.

El 8 de julio de 2026, Binance Research publicó este informe en profundidad titulado Stablecoins: Transforming The Financial Landscape. El informe examina de manera sistemática cómo las stablecoins han evolucionado desde simples puentes en el ecosistema cripto hasta convertirse en infraestructura esencial que está transformando el sistema financiero global.
Este artículo ofrece un desglose detallado de las ideas clave del informe, los datos críticos y las tendencias emergentes, ayudando a los lectores a comprender los cambios estructurales que se están produciendo en las stablecoins y sus profundas implicaciones para las personas, las instituciones y el panorama financiero en general.

Solo una semana después de su lanzamiento en mainnet público el 1 de julio, la Liquidez Total Bloqueada (TVL) de Robinhood Chain superó la marca de los 100 millones de dólares, con un pico cercano a los 106 millones y un aumento en 24 horas de hasta 159% en un momento dado. Este crecimiento explosivo, impulsado por protocolos de préstamos DeFi y amplificado por la liquidez de trading de monedas meme, colocó rápidamente a la nueva cadena de Capa 2 en el centro de la atención.
El 8 de julio, una moneda meme en cadena vio cómo su capitalización bursátil superaba brevemente los 110 millones de dólares antes de retroceder a alrededor de 104 millones. Entregó ganancias de más de 13,9x en 24 horas (incluso más altas en momentos puntuales), con un volumen diario de operaciones que alcanzó cientos de millones de dólares. El volumen total DEX de Robinhood Chain se disparó por encima de los 500 millones de dólares en 24 horas, convirtiéndola en uno de los temas más candentes del mercado.

La publicación de las actas de la reunión de junio del FOMC de la Reserva Federal ha desviado la atención de los inversores de la pregunta conocida de "¿cuándo comenzarán los recortes de tipos?" hacia un asunto más fundamental: ¿qué está impulsando la inflación en la próxima etapa de la economía estadounidense?
Si bien los responsables de la política monetaria acordaron por unanimidad mantener los tipos de interés sin cambios, las actas revelan una preocupación creciente de que las presiones inflacionarias están evolucionando en lugar de desaparecer. Más allá de los salarios y la demanda de los consumidores, los funcionarios de la Reserva Federal señalaron cada vez más tres fuentes emergentes de una inflación persistente: la inversión en inteligencia artificial, el aumento de los precios de la energía y los aranceles más altos.

En julio de 2026, Robinhood presentó una de las expansiones de producto más ambiciosas de su historia en su evento "The World is Flat" en Londres. La compañía lanzó oficialmente Robinhood Chain, su blockchain de Capa 2 construida sobre Arbitrum Orbit, junto con una serie de nuevos productos, como acciones tokenizadas, préstamos descentralizados, agentes de trading impulsados por IA, futuros perpetuos y una estrategia de expansión global acelerada.
A primera vista, puede parecer otra plataforma financiera que lanza su propia blockchain. Pero al mirar más de cerca, Robinhood está intentando algo mucho más grande: transformarse de un bróker en línea en la capa de infraestructura para la próxima generación de las finanzas globales.

Introducción
A principios de julio, los informes de que Meta estaba construyendo un negocio de computación en la nube y se preparaba para vender capacidad de cómputo de IA a clientes externos desencadenaron una reacción inusualmente aguda en todo el sector de la infraestructura de IA. La respuesta del mercado fue marcadamente asimétrica: las acciones de Meta se dispararon, mientras que las empresas de alquiler de cómputo de IA, como CoreWeave y Nebius, sufrieron pérdidas significativas. Al mismo tiempo, casi todo el ecosistema de hardware de IA —incluyendo AMD, Micron, SanDisk, ASML, TSMC, Samsung Electronics y SK hynix— quedó bajo una presión vendedora generalizada. En apariencia, esto parecía no ser más que otra empresa tecnológica que se expandía a una nueva línea de negocio. Sin embargo, en realidad, lo que el mercado estaba valorando no era si Meta pretendía comercializar sus recursos de GPU, sino si una de las suposiciones fundamentales que ha sustentado a la industria de la IA durante los últimos dos años empezaba a cambiar.

Ayer, un nuevo anuncio de stablecoin se impuso rápidamente en las conversaciones en toda la comunidad cripto y entre los inversores de renta variable de EE. UU. Más de 140 empresas e instituciones presentaron conjuntamente Open USD (OUSD), mientras que las acciones de Circle (CRCL) cayeron de inmediato aproximadamente un 17,5%. Al mismo tiempo, el último reajuste del índice Russell provocó una presión adicional de venta por parte de fondos pasivos. Este evento va más allá del lanzamiento de un único producto. Señala la integración acelerada de las stablecoins desde herramientas nativas de cripto hacia la infraestructura de pagos financieros convencional, al tiempo que impulsa al mercado a replantearse el impacto competitivo real de los gigantes financieros tradicionales que entran en el sector. A continuación, se presenta un desglose completo del evento, de OUSD y de sus implicaciones desde múltiples perspectivas.

I.¿Por qué Grayscale ha vuelto a fijarse en Solana?
En los últimos años, dos palabras casi siempre han definido a Solana: el rendimiento y las memecoins.
Como una de las principales blockchains de Capa 1 del ciclo de mercado anterior, Solana ganó protagonismo gracias a su alto rendimiento, sus bajos costos de transacción y su finalización casi instantánea. Al mismo tiempo, proyectos del ecosistema como BONK, dogwifhat (WIF) y Pump.fun convirtieron a Solana en el epicentro del auge de las memecoins. Sin embargo, esta percepción también ha eclipsado una transformación más profunda que se está produciendo en toda la red.

A finales de junio de 2026, Chainalysis presentó un nuevo marco llamado Blockchain Tracing Ontology, con el objetivo de establecer una forma más estandarizada y transparente de describir la inteligencia sobre blockchain. En lugar de lanzar otro producto de analítica o una herramienta de investigación, la empresa intenta algo mucho más fundamental: redefinir cómo se estructuran, interpretan y comunican los datos de trazado en blockchain.
Aunque el marco aún se encuentra en una etapa de propuesta, ya ha provocado un importante debate en toda la industria de activos digitales. En su esencia, plantea una pregunta simple pero de gran alcance: ¿la inteligencia sobre blockchain necesita un lenguaje común?

El 26 de junio de 2026, OpenAI presentó oficialmente la familia GPT-5.6, dando a conocer tres modelos distintos: Sol, Terra y Luna. A diferencia de lanzamientos anteriores centrados en un único modelo insignia, GPT-5.6 representa un cambio significativo en la estrategia de producto de OpenAI. En lugar de ofrecer un solo modelo «mejor», la empresa ahora presenta una cartera completa de modelos diseñada para atender tres prioridades diferentes: máxima inteligencia, rendimiento equilibrado y eficiencia de costos de alta capacidad de procesamiento.
Según OpenAI, la serie GPT-5.6 mejora significativamente las capacidades en ingeniería de software, operaciones informáticas, trabajo profesional del conocimiento, investigación científica y ciberseguridad. En el lanzamiento, los modelos están disponibles únicamente a través de una vista previa limitada mediante la API y Codex para un pequeño grupo de socios de confianza, y se espera una disponibilidad más amplia en ChatGPT en una etapa posterior.

El 22 de junio de 2026, trade.xyz lanzó oficialmente el contrato perpetuo ZHIPU-USDC en el mercado Hyperliquid HIP-3. El contrato ofrece hasta 10x de apalancamiento y permite trading 24/7.
Este marca el segundo activo listado en Hong Kong por trade.xyz. El primero fue MINIMAX (Xi Yu Technology / MiniMax Group, HK:0100), que se activó el 18 de junio de 2026.
Resumen Rápido
Este artículo comienza con el trasfondo de la empresa ZHIPU y el avance tecnológico GLM-5.2, luego detalla la mecánica del contrato trade.xyz y su rendimiento inicial, lo compara con MINIMAX, analiza múltiples factores impulsores, explora el ecosistema Hyperliquid HIP-3, y finalmente mira hacia el potencial a largo plazo de la fijación de precios en cadena para diversos activos.

El 22 de junio de 2026, Marvell Technology se convirtió oficialmente en un componente del índice S&P 500. A primera vista, esto puede parecer un evento rutinario de reequilibrio del índice. Sin embargo, al verlo a través de la lente más amplia del ciclo de inversión en infraestructura de IA, la revalorización de la industria de semiconductores en EE. UU., y la creciente influencia de los flujos de inversión pasiva, la inclusión de Marvell representa algo mucho más significativo. Sirve como un reconocimiento formal de la exitosa transformación de la compañía de ser un proveedor tradicional de semiconductores de comunicaciones a convertirse en un jugador crítico en el ecosistema de infraestructura de IA. Para Marvell, unirse al S&P 500 no solo le otorga estatus de blue-chip a los ojos de los inversores globales, sino que también eleva las expectativas en cuanto a crecimiento, rentabilidad y ejecución a largo plazo. Como tal, este hito es tanto un logro como el comienzo de una fase más exigente en su trayectoria corporativa.

A primera vista, la reunión de política del Banco Central de junio de 2026 parecía tranquila. El Comité Federal de Mercado Abierto (FOMC) decidió mantener la tasa de fondos federales sin cambios en 3.50%–3.75%, un movimiento que fue ampliamente anticipado por los mercados y en gran medida ya estaba descontado antes del anuncio.
Sin embargo, centrarse únicamente en la decisión de la tasa de interés corre el riesgo de perder el mensaje más importante de la reunión. Aunque la Fed optó por no subir las tasas esta vez, sus proyecciones económicas actualizadas, cambios en el gráfico de puntos, lenguaje de política revisado y la debutante aparición del nuevo presidente de la Fed, Kevin Warsh, enviaron colectivamente una señal mucho más significativa: la conversación en política monetaria ha cambiado de "¿cuándo comenzarán los recortes de tasas?" hacia "¿podrían ser necesarias más subidas de tasas para contener la inflación?"

Introducción
En la última década, Notion se ha convertido en una de las empresas más interesantes para estudiar en el panorama global de SaaS. No se construyó a través de una sola característica innovadora, un hack de crecimiento efímero, o una agresiva máquina de ventas empresariales. En cambio, Notion creció a través de un sistema de crecimiento complejo pero altamente orgánico, evolucionando de una herramienta de productividad de nicho a una plataforma global para la gestión del conocimiento, colaboración en equipo y diseño de flujos de trabajo. Muchos productos adquieren usuarios tempranos a través de la novedad, pero a medida que el interés de los usuarios se desvanece, las alternativas se multiplican y los costos de adquisición aumentan, rápidamente alcanzan un techo de crecimiento. Lo que hace que Notion sea diferente es que su crecimiento nunca se basó en un solo canal. Conectó la experiencia del producto, ecosistemas de plantillas, comunidades de usuarios, distribución de contenido y necesidades de colaboración en equipo en una sola red que se refuerza mutuamente.

Introducción
En junio de 2026, el Banco de Japón elevó su tasa de política al 1%, marcando la primera vez desde 1995 que la tasa de referencia de Japón alcanzó este nivel. En términos absolutos, una tasa de política del 1% no es nada extraordinario entre las principales economías. La tasa de fondos federales de EE. UU. se mantiene por encima del 4%, y las tasas de política en gran parte de Europa son aún significativamente más altas que las de Japón. Visto puramente como un número, el aumento de tasas de Japón no parece lo suficientemente significativo como para atraer una atención global tan amplia. Sin embargo, los mercados financieros rara vez se enfocan solo en el nivel de las tasas de interés; se centran en lo que esas tasas señalan sobre la dirección de la política y el ciclo económico más amplio. Para una economía que ha pasado décadas en un entorno de tasas cero e incluso negativas, el movimiento de tasas negativas a 1% representa un cambio profundo en el marco monetario que ha sostenido la economía de Japón durante casi treinta años.