💊Drug Discovery Gets a New Lab Partner

👋 Welcome! Today, we’re covering a range of topics from Google’s imagination of video games to how pharmaceutical companies are gearing AI to create complex medications. Get ready for a full spectrum of insights that will keep you informed and engaged!

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AI & TECH

Veo 3 could mark the dawn of playable world models – Google DeepMind’s CEO Demis Hassabis hinted that Veo 3—its latest text-to-video model—might evolve beyond cinematic storytelling into interactive game worlds. Google already runs a playable-world prototype, Genie 2, and may one day blend Veo’s visuals, Genie’s simulation, and Gemini’s intelligence to power next-gen video games.

Meet the robot tattoo master – Blackdot, an Austin-based startup, has introduced Aero (“Artist Enabled Robotic Operator”), an AI-powered tattooing device now available at Bang Bang Studio in New York City. Using computer vision, microscopic imaging, and skin-depth sensors, Aero converts tattoos into ultra-precise micro-dots—aiming to reduce pain and boost consistency.

Baidu unveils AI video generator – Baidu launched a new generative AI video platform and major AI-powered upgrades to its search engine. The video tool enables users to produce short clips from text prompts, showcasing China's push to rival global AI giants.

CAREER & WORK

AI-powered VTubers are earning millions – Virtual YouTube personas—often called “VTubers”—powered by artificial intelligence are generating multi-million dollar revenues from gaming and entertainment content. Take Bloo, a 3D avatar with around 2.5 million subscribers and over 700 million views, who’s pulling in seven‑figure earnings through ad revenue, sponsorships, and fan support.

Microsoft to cut 4% of workforce – Microsoft has announced a workforce reduction of nearly 4%, laying off around 9000 employees, as it shifts resources and retools operations to support heavy investments in AI infrastructure and development.

U.S. private payrolls drop – U.S. private payrolls fell by 33,000 in June—the first decline since March 2023—amid growing economic uncertainty, according to ADP. This surprised economists who forecasted a 95,000 job gain. Despite the drop, layoffs remain low, indicating a still-resilient labor market.

ECONOMY & FINANCE

U.S. and Vietnam reach preliminary trade deal – President Trump announced a framework deal on July 2, 2025, replacing a proposed 46 % tariff with a 20 % levy on Vietnamese goods while Vietnam offers zero‑tariff access for U.S. exports. This marks a pivotal turning point for deals with apac region, especially China.

Japan Yen out of steam? – As yen value continues to rise, many investors are considering trimming yen‑long bets amid Bank of Japan caution and widening U.S.‑Japan rate gap. Despite long‑term hopes for appreciation as Japan exits ultra‑low rates, short‑term pressures—including stalled U.S. trade talks and political uncertainty—make holding the yen costly.

Crypto looking for more legitimacy – Ripple Labs has applied to the U.S. Office of the Comptroller of the Currency for a national banking license, following Circle’s similar efforts. The move would bring its dollar‑backed stable coin under federal supervision and allow access to a Federal Reserve master account, boosting transparency and regulatory legitimacy.

VC & FUNDING

Figma eyes $1.5B IPO – Figma is reportedly advancing toward a blockbuster IPO that could raise up to $1.5 billion with strengthened financial footing. The design software company is said to be working with underwriters as it weighs timing and valuation, potentially marking one of the biggest tech listings of 2025.

Argon AI set to revolutionize pharma research – Argon AI, a ChatGPT‑style AI workspace tailored to pharma and life‑sciences, has closed a $5.5 million seed funding round led by Crosslink Capital and Wireframe Ventures, with support from Y Combinator and Pioneer Fund. The New York–based startup uses a proprietary data pipeline and “golden datasets” to ensure reliable outputs for drug development.

BIG THINK

AI in Pharma: Drug Discovery Breakthrough or Overhyped Promise?

AI is rapidly transforming the pharmaceutical industry, from molecule design to clinical trials, and even regulatory processes.

Drugmaker AstraZeneca is committing to AI-enabled drug discovery for chronic illness, signing a $5.2 billion partnership to leverage machine learning platforms that generate novel molecular candidates—aimed at reducing typical development timelines (source). Such large-scale deals mark a significant shift toward AI-first R&D strategies.

Smaller startups are also accelerating progress. A recently released dataset of 5.2 million synthetic molecular structures—generated using NVIDIA‑powered AI—assists in training models to predict drug‑target interactions (source). Public availability of such data could democratize discovery and empower researchers outside big pharma.

Meanwhile, regulators aren’t standing still. The FDA is piloting a secure AI tool named "Elsa" to summarize clinical protocol and adverse event documents, aiming to halve review timelines without training on proprietary data (source). If fully adopted, this could ease bottlenecks in drug approvals and improve patient safety.

Despite the enthusiasm, skeptics warn against overpromising AI’s role. One skeptic noted that while a new AI-designed molecule for pulmonary fibrosis completed early trials, success was modest—highlighting that real-world outcomes can fall short of theoretical predictions. Complex diseases remain formidable targets, even with computational acceleration.

AI itself introduces risks: bias in datasets may skew molecule prioritization, while overreliance on simulated binding models may overlook toxicity. Experts emphasize that human validation and wet-lab testing remain essential safeguards, cautioning against viewing AI as a replacement rather than a research amplifier.

Actionable Insights:

Blend AI with Human Expertise: Use AI for generating candidate molecules and streamlining review—but always integrate expert-led lab validation and safety checks before advancing compounds.
Ensure Data Transparency and Bias Monitoring: Require AI training datasets to be diverse and auditable, with mechanisms to flag bias or toxicological blind spots. Publish model validation outcomes alongside clinical trial results.