Barry Sternlicht Says U.S. Crypto Regulations Are Blocking Starwood’s Real Estate Tokenization Plans

TheNewsCryptoPublicado a 2026-02-19Actualizado a 2026-02-19

Resumen

Barry Sternlicht, founder and CEO of Starwood Capital, stated that his firm is prepared to tokenize real estate but is being blocked by unclear U.S. crypto regulations. Speaking at the World Liberty Forum, he described tokenization—converting physical asset ownership into digital tokens on a blockchain—as a "fantastic" innovation that represents the future of finance. It enables fractional ownership, improves liquidity, increases transparency, and allows smaller investors to participate. However, Sternlicht emphasized that regulatory uncertainty is the major obstacle preventing large firms from entering the market. Deloitte estimates that up to $4 trillion in real estate could be tokenized by 2035, signaling significant potential growth if clear regulations are established.

Barry Sternlicht, who was the founder and CEO of Starwood Capital, says his company is ready to tokenize real-world assets, but U.S. regulations are blocking it. His company manages more than $125 billion in assets. He shared his plans on RWA and made comments on the current U.S. rules on crypto at the World Liberty Forum in Florida.

What Tokenization means

Tokenization is a process of turning the ownership of physical assets into digital tokens in the blockchain. Instead of buying an entire building, an investor could buy a small portion in a digital share that can be traded more easily. Supporters say tokenization for the large firms could make it easier to raise money and allow small investors to participate. It also reduces the paperwork and improves transparency.

Currently, the U.S. has not fully adopted tokenization in real estate. Consulting firm Deloitte estimates that $4 trillion worth of real estate could be tokenized by 2035, and Proppy has also announced a plan to expand its blockchain-based real estate services. In 2024, less than $0.3 trillion of real estate was tokenized, which signals that the market could grow significantly over the next decade.

According to Sternlicht, he described tokenization as “fantastic” and said it represents the future of finance. He compared tokenization to artificial intelligence but said it is still at an early stage of development. He said the biggest challenge is the lack of clear U.S. regulation around digital assets and tokenized securities, which creates uncertainty for large financial firms.

Sternlich’s statement shows that the U.S. regulations are the major issue that blocks large firms from entering the real estate tokenization. If the U.S. lawmakers provide clear rules, then tokenized RWA could become a major market in the upcoming years.

Highlighted Crypto News:

CoinFello Unveils AI Smart Contract Agent at ETHDenver with BuffiBot Preview

TagsBlockchainCryptocurrencyUS crypto

Criptos en tendencia

Preguntas relacionadas

QWhat is Barry Sternlicht's main concern regarding real estate tokenization in the U.S.?

ABarry Sternlicht's main concern is that the lack of clear U.S. regulations around digital assets and tokenized securities is blocking large financial firms, including his company Starwood Capital, from entering the real estate tokenization market.

QHow does tokenization work for real estate assets?

ATokenization is the process of converting ownership of physical real estate assets into digital tokens on a blockchain. This allows investors to buy small digital shares of a property rather than the entire building, making it easier to trade, raise funds, and enable participation from small investors while reducing paperwork and improving transparency.

QWhat is the estimated potential value of tokenized real estate by 2035 according to Deloitte?

AConsulting firm Deloitte estimates that up to $4 trillion worth of real estate could be tokenized by 2035.

QHow much real estate was tokenized in 2024, and what does this indicate?

AIn 2024, less than $0.3 trillion of real estate was tokenized, which indicates significant growth potential for the market over the next decade.

QWhat did Sternlicht compare tokenization to, and what stage of development did he say it is in?

ASternlicht compared tokenization to artificial intelligence (AI) but stated that it is still at an early stage of development.

Lecturas Relacionadas

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbitHace 54 min(s)

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbitHace 54 min(s)

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbitHace 1 hora(s)

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbitHace 1 hora(s)

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbitHace 3 hora(s)

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbitHace 3 hora(s)

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbitHace 3 hora(s)

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbitHace 3 hora(s)

Trading

Spot
Futuros

Artículos destacados

Cómo comprar COMP

¡Bienvenido a HTX.com! Hemos hecho que comprar Compound (COMP) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Compound (COMP) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Compound (COMP)Después de comprar tu Compound (COMP), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Compound (COMP)Tradear fácilmente con Compound (COMP) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

212 Vistas totalesPublicado en 2024.12.13Actualizado en 2026.06.02

Cómo comprar COMP

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de COMP (COMP).

活动图片