DEV Community: Judy

Building the Smart Era Infrastructure in Michigan

Judy — Wed, 15 Jul 2026 01:00:27 +0000

This article is a deep-dive from JudyAI Lab — an AI engineering playbook series with 100+ published guides, 5,000+ weekly readers across 60+ countries, focused on the practical side of running AI agents, trading systems, and content pipelines in production.

📰 Key Takeaways

OpenAI announces a massive 1 GW data center in Michigan, part of the Stargate AI infrastructure expansion. This power capacity is enormous, making it one of the largest AI computing facilities to support model training and inference across OpenAI's lineup. The official statement outlines three key goals: expanding AI accessibility, creating local jobs, and contributing to the surrounding community. Michigan was chosen strategically—the state offers a mature manufacturing supply chain and power infrastructure, which will help accelerate construction. Stargate was initially announced by OpenAI together with multiple tech and financial institutions, aiming to build a series of hyperscale AI data centers across the US to secure America's infrastructure advantage in the global AI race. The Michigan facility is the latest development in Stargate's siting strategy, showing the program has moved from announcement to actual groundbreaking. The original summary covers only these directions—for construction timelines, exact investment figures, and partner details, see the original link.

💬 JudyAI Lab Perspective

OpenAI broke ground on a 1 GW data center in Michigan, and the Stargate program has officially moved from announcement to shovels-in-the-ground mode. This marks a decisive turning point in the AI infrastructure race—it's now about burning hardware.

One gigawatt of power capacity makes this one of the larger AI computing facilities out there. That number alone tells you the demand for model training and inference has far exceeded what general cloud services can handle. What caught our attention even more is the选址 logic: Michigan got picked because of its mature manufacturing supply chain and power infrastructure, not just policy incentives or cheap land. Here's the thing—building large-scale AI infrastructure is starting to look more like a traditional heavy industry's site decision. Power stability and supply chain maturity are the real barriers. From OpenAI announcing Stargate to actually breaking ground, the speed itself is sending a signal: whoever turns compute into physical builds fastest will have the edge in the inference services market down the road.

Next time you're evaluating which AI provider to integrate with, check out their data center footprint and power assurance capabilities—that directly determines your product's stability ceiling.

📅 Original Source Info

Published: 2026-06-01T12:00
Source Article: https://openai.com/index/stargate-michigan-data-center

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

The Real Truth About AI Taking Jobs: Who Actually Gets Eaten and Who Survives Behind 8,000 Resumes

Judy — Wed, 15 Jul 2026 01:00:08 +0000

8,000 Resumes, a Door That Won't Open

I read Nikkei Asia's June 30 report twice: top US university tech grads sent out 8,000 resumes and heard almost nothing back. The numbers TechCrunch pulled together the same week are even colder — before May 2026, US employers had explicitly tagged over 90,000 positions as "eliminated due to AI."

Put those two numbers together and, from the builder's seat, it isn't a surprise. MIT NANDA's 2025 State of AI in Business report punctures one contradiction: 95% of enterprise GenAI deployments return zero P&L, but that 5% that does see returns almost all use AI for repetitive entry-level work (data processing, code review, content editing), and the P&L gain shows up directly as headcount cuts. That maps straight onto the other side of Nikkei's 8,000-resume story — junior headcount is gone, so the resumes have nowhere to land.

On the builder side, I'm not surprised. The deeper I go, the clearer it gets why those 8,000 resumes hit a closed door.

The Three Types of Jobs AI Is Actually Eating

I'm not going to throw around scary generalities. From actually deploying agents, I can clearly name which positions are getting locked out of the interview list.

The first is entry-level technical documentation, code review, and data processing. This is what junior data analysts and entry-level engineers used to do every day: catch typos, standardize formats, generate summaries, run basic stats. Deploy an LLM agent now and it hits 70–80% quality, runs 24/7, and costs less than a junior's monthly salary. A lot of US tech companies have already cut these headcounts — not trimmed, eliminated. That 5% from the MIT NANDA report with real returns almost all use AI to replace this kind of repetitive work, and the P&L shows up straight in the headcount budget.

The second is customer support and content editing. In TechCrunch's list of 90,000 eliminated positions, support engineers and content editors are the heaviest groups. These jobs used to be the dual-track growth ladder for mid-career juniors — go in for 1–2 years, learn the industry jargon and how to talk to customers, then move up into marketing, product, or customer success. Now AI handles tier-1 ticket triage, FAQ replies, and large-scale copy generation on its own, and that dual-track ladder is broken. This isn't about whether the news is good or bad — the org chart has already been redrawn.

The third is entry analyst roles — junior finance, data, and market research. Here's where it gets absurd: I see plenty of US tech companies using AI to screen resumes and run first-round interviews, blocking candidates with the reason "AI already replaced this role." So AI is acting as the HR gatekeeper while simultaneously killing the entry-level openings. That's the coldest loop behind the 8,000-resume story.

Five Patterns That Survive

A closed door doesn't mean total wipeout. From building an AI team, here are five positions I genuinely cannot find a way for an agent to carry alone.

One: AI trainers and prompt engineers. These are new roles, but the bar is way higher than "familiar with ChatGPT" on a resume. What you actually need is someone who can design evaluation criteria, break down agent failure modes, and maintain multi-agent collaboration. I spend 20–30 hours a week doing this myself — far more than writing code.

Two: systems integration. The part of building an AI team that AI can't replace is wiring different systems (content generation, databases, notifications, review gates) into a stable production line. That requires cross-team communication and judgment about when to let an agent run on its own and when to require human review. It's a rare mix of "understands tech, understands the business, understands people."

Three: product positioning and customer problem discovery. AI can spit out 100 solutions, but it can't decide on its own whether a problem is even worth solving. That judgment requires deep customer understanding — running customer interviews yourself, taking the product through its own failures.

Four: decision ethics and compliance. Anywhere money, law, healthcare, or privacy is involved, a human has to be in the loop — a named human with legal accountability. When building automated trading or decision systems, the common practice is to add emergency stops and human sign-off gates. Not because the tech can't do it, but because you can't hand responsibility to an agent.

Five: physical-world operations. Plumbing, F&B, logistics, on-site repairs — AI substitution moves way slower here than people imagine. I keep telling friends around me: don't underestimate the value of physical labor and on-site experience just because the news is loud.

Three Things Office Workers Should Do

First, treat AI as a colleague to train, not an enemy. Real-world skills are built like this: pick one concrete task (weekly report summary, customer reply categorization, data scraping), design a prompt, run it 10 times, study the failures, refine the prompt. After three months you'll understand real-world AI use better than anyone in your department who treats AI as just a "chat toy." That process itself is your differentiation.

Second, build cross-domain skills. Tech plus domain knowledge is always worth more than tech alone. If you're in marketing, tie AI to customer psychology. If you're in finance, tie AI to compliance. The surviving positions all sit at the intersection.

Third, document your workflow visibility. Write down seriously what you actually do each week, what your decision chain looks like, and what your output metrics are. Before your boss decides whether to replace you with AI, this is the record they'll see first. Positions whose workflows are invisible get cut first.

Closing

8,000 resumes isn't AI's fault — it's the "structural disappearance of entry-level roles." That structural gap won't be filled by complaining about AI. After building an AI team for over six months, the truth I see is this: AI amplifies the problems each position already had. Repetitive roles disappear fastest; integration, judgment, ethics, and physical roles get more valuable. The number 8,000 isn't a generation's failure — it's a complete reshuffle of the entry-level structure.

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Alphabet Plans to Raise $80 Billion for AI Infrastructure Expansion

Judy — Sat, 11 Jul 2026 01:00:29 +0000

📰 Key Highlights

Alphabet (Google's parent company) publicly states that demand from enterprise customers and consumers for its AI solutions and services continues to grow strongly, significantly exceeding the company's current supply capacity. To address this supply-demand gap, Alphabet plans to raise up to $80 billion, specifically allocated for expanding AI-related infrastructure. In its official statement, the company directly acknowledges the existence of production capacity bottlenecks in its AI service delivery capabilities and states that large-scale capital injection is needed to keep pace with market demand. However, the original summary does not reveal further details such as the specific structure of this financing, the allocation ratios for various AI construction projects, or the estimated completion timeline. For more details, please refer to the original article link.

💬 JudyAI Lab Perspective

Alphabet has openly admitted that its AI service capacity can't keep up with demand, and announced plans to raise $80 billion specifically for infrastructure expansion—this is the first time the biggest player in tech has directly quantified the supply-demand gap. That's something everyone working on AI products should take seriously.

The driving force behind this demand boom isn't some technological breakthrough—it's that adoption rates on both the enterprise and consumer sides have already outpaced what suppliers expected. For teams like us who are planning or developing AI products, this signal points to a core reality: infrastructure availability and stability will become the most critical design constraint over the next one to two years—not feature design, but whether compute power can reliably kick in when it matters. Alphabet's massive capital plan also suggests that pricing and supply conditions for cloud AI services could shift in the near term—existing cost assumptions may not hold.

Now's a good time to rethink your AI service dependency structure: if your main supplier has limited capacity, do you have backup options or alternative inference paths?

📅 Original Article Info

Published: 2026-06-01T22:55
Original Source: https://techcrunch.com/2026/06/01/alphabet-plans-to-raise-80-billion-to-pay-for-ai-buildout/

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Driving Global Youth Cybersecurity and Digital Opportunity Assurance Through International Leadership

Judy — Sat, 11 Jul 2026 01:00:08 +0000

📰 Key Takeaways

OpenAI recently called on the world to jointly address youth AI safety issues, proposing the establishment of an international specialized agency dedicated to coordinating standards and regulatory frameworks for minors' use of AI. The core concern of this initiative is that there is currently no unified standard across countries for regulating youths' access to AI systems, making potential risks difficult to manage systematically. OpenAI believes that corporate self-regulation or individual country legislation alone is insufficient to address this cross-border challenge — an international coordination mechanism is needed to ensure safety while guaranteeing that younger generations still have fair access to the learning and development opportunities that AI brings. The proposal emphasizes that the goal is not to restrict youths' use of AI, but to design systems that make these opportunities more secure and risks more predictable. Since the original summary was concise, specific details such as the organizational structure, member composition, and implementation timeline were not disclosed. For detailed content, please refer to the original article link.

💬 JudyAI Lab Perspective

� Commentary To Be Added (to be added by Hermes during the finalize_commentary stage; must be fact-based and not extrapolate information)

📅 Source Information

Published: 2026-06-02T07:00
Source Article: https://openai.com/index/advancing-youth-safety-and-opportunity-through-global-leadership

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

NVIDIA Cosmos 3 Open Sources First Full-Modality Physical AI Reasoning and Action Model

Judy — Wed, 08 Jul 2026 01:00:27 +0000

📰 Key Highlights

NVIDIA releases Cosmos 3, an open full-modality World Foundation Model designed for "Physical AI", featuring integrated image generation, physical reasoning, and action output in a single architecture, replacing the previous separate deployment of Cosmos Predict, Transfer, Reason, Policy, and other independent models.

Cosmos 3 uses a Mixture-of-Transformers (MoT) backbone, operating through two parallel processing streams: autoregressive (AR) sequence for reasoning and understanding, and diffusion (DM) sequence for iterative denoising generation. While using independent parameters, both interact through shared attention mechanisms, capable of handling multiple modalities including text, images, video, audio, and motion simultaneously.

The model launches in two versions: Cosmos 3 Nano with 8B reasoner + 8B generator, targeting workstation-class hardware (like RTX PRO 6000); Cosmos 3 Super expands to 32B + 32B, targeting NVIDIA Hopper and Blackwell high-end GPUs, suitable for large-scale synthetic data generation and research. Application scenarios cover robotics manipulation, autonomous driving, warehouse safety, and intelligent spaces. The model is now available on Hugging Face, integrated into Diffusers' Cosmos3OmniPipeline, with six synthetic training datasets open-sourced covering robotics, physical simulation, driving, warehouse, spatial reasoning, and human motion.

💬 JudyAI Lab Viewpoint

⏳ Commentary To be added (by Hermes during finalize_commentary stage — must be fact-driven, no information fabrication)

📅 Source Information

Release Time: 2026-06-01T04:44

- Source: https://huggingface.co/blog/nvidia/cosmos-3-for-physical-ai

🔗 延伸閱讀

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

JetBrains Releases Mellum2: 12B Parameter Mixture-of-Experts Architecture Developer-Focused Model

Judy — Wed, 08 Jul 2026 01:00:07 +0000

📰 Key Takeaways

JetBrains released Mellum2 on June 1, 2026—a 12-billion parameter open-source model based on Mixture-of-Experts (MoE) architecture, but it only activates 2.5 billion active parameters per inference, making inference over twice as fast as models of equivalent scale, significantly reducing deployment costs, released under Apache 2.0 license.

Mellum2 isn't positioned as a replacement for frontier large models, but rather as a "focused model" in multi-model collaboration systems, handling high-frequency lightweight tasks including prompt classification, tool selection, context compression and summarization for RAG pipelines, sub-agent planning validation, and code completion. The model processes only text and code modalities, deliberately excluding multimodal capabilities to keep the architecture lean—particularly suitable for enterprises deploying in private environments to handle internal code and confidential data.

Across multiple benchmarks including code generation, reasoning, science, and math, Mellum2 achieves competitive performance among open-source models of similar scale. The technical report has also been published on arXiv (ID 2605.31268), and model weights are available for download on HuggingFace.

💬 JudyAI Lab Perspective

The Mellum2 release from JetBrains is worth paying attention to—not because it's taking on frontier large models, but because it clearly demonstrates the "good enough is best" design philosophy: 12 billion parameters but only 2.5 billion activated, inference twice as fast, costs significantly reduced.

This case reflects a clear trend we've observed: in multi-model collaboration architectures, every node doesn't need to use flagship models. Mellum2's design choices are highly instructive—it processes only text and code, deliberately drops multimodal capabilities, and concentrates performance on several high-frequency tasks that don't require deep reasoning: prompt classification, tool selection, context compression for RAG pipelines, sub-agent planning validation, and code completion. For enterprises wanting to handle internal code or confidential data in private environments, the Apache 2.0 license plus lightweight deployment costs make this type of model a quite pragmatic choice.

If you're designing a multi-model collaboration system, what you can do now is: list out each task node, identify which positions "don't need the strongest model," and try replacing them with focused models like Mellum2—this might be the most direct starting point for cutting down inference costs.

📎 Source Information

Published: 2026-06-01T15:45
Original Source: https://huggingface.co/blog/JetBrains/mellum2-launch

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

How Google Built Google I/O 2026 Developer Conference with Gemini

Judy — Sat, 04 Jul 2026 01:00:28 +0000

📰 Key Summary

This passage is the alt text description for a collage image on the Google I/O 2026 related reporting page. The content only involves visual elements, including the Antigravity Coffee Co. flash event, colorful jellyfish images, Timmy TPU video screenshots, and decorative icons such as the repeated 'AI' text appearing three times on the left side of the page along with twinkling star shapes. It does not contain any substantive information about Google AI technology, product announcements, or event agendas. Since the original summary lacks expandable mechanisms, numbers, or details, please refer to the original link for full content.

💬 JudyAI Lab Perspective

The substantive information in this original summary is insufficient to support commentary writing.

According to Absolute Rules 1 and 2, I can only quote facts from the original summary and must not fabricate extensions. The entire content here is alt text for a collage image—describing coffee flash event visuals, jellyfish images, and decorative 'AI' text—without any technical details, numbers, mechanisms, or industry dynamics可供分析的技術細節、數字、機制或產業動態.

If I were to force out a 300-word commentary, I'd have to supplement it with additional facts about Google I/O 2026, directly violating Rules 1 and 2.

Recommended Approach:

Verify whether the RSS pipeline grabbed the correct article content (rather than image alt text)
If the original link contains substantial content, please provide the text summary from that page
If this piece genuinely has no substance, recommend skipping commentary generation to avoid producing low-quality or fabricated content

📅 Original Information

Publication Time: 2026-06-01T16:00
Original Source: https://blog.google/innovation-and-ai/technology/ai/io-2026-google-ai/

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

The Global AI Hardware Gamble: Korea $550B + Japan $6B + Qualcomm Challenges NVIDIA - What This Means for Investors and Builders

Judy — Sat, 04 Jul 2026 01:00:08 +0000

Over the past week, the AI hardware news I've been tracking adds up to more than $610 billion in capital deployed globally — in just seven days.

Not valuations. Not market cap. Actual capital expenditure commitments. Korea $550B, Japan $6B, Qualcomm's new accelerator, Kawasaki Heavy Industries' $1B AI infrastructure bond — this round of moves has already surpassed the wildest half-year of the 2000 dot-com bubble in scale. But this time the money isn't flowing into web pages. It's flowing into chips, memory, and power. Watching all of this over the past few days, I've been thinking: for investors and for builders like us making products on top of AI, what does this gamble actually mean?

The Real Story Behind AI Training Bottlenecks: From GPU Scarcity → Memory Scarcity → Power Scarcity

Honestly, everyone watches AI through the lens of models, but the real bottleneck was never the models — it's been the hardware.

From 2023 to 2025, the bottleneck shifted from GPU scarcity to memory scarcity, and is now pushing toward power scarcity. When GPUs were tight, everyone scrambled for H100s and NVIDIA raked it in — but the part that actually throttled the H100 wasn't the GPU core, it was the HBM high-bandwidth memory. On the B200, the HBM3E stacked on top has its capacity locked up entirely by NVIDIA at SK Hynix, while Samsung is chasing hard but its yields can't keep up.

That's why South Korea just committed $518B to build 4 memory fabs plus $52B for the central regions, totaling $550B (TechCrunch). This isn't just about filling upstream capacity — the key is that Samsung + SK Hynix are trying to flip themselves from being NVIDIA's downstream suppliers into becoming the dominant players in AI hardware.

Why did downstream hardware investment kick off so late? Because for the past two years people were still watching and waiting to see if "this AI hype cycle would cool down again." By 2026, GPT-6, Claude 4, and Gemini 3 are all live, inference costs have come down, user numbers are real — only then did people dare to bet on 10+ year capacity. Japan is also putting in $6B to back SoftBank-led AI model development (Nikkei), and Kawasaki Heavy Industries issued a $1B bond to dive into AI infrastructure. East Asia is pushing "AI sovereignty" as a national-level agenda.

Qualcomm's Challenge: What Bypassing HBM Actually Means

Then there's Qualcomm — and this move is interesting.

Qualcomm's new AI accelerator series announced in early July leads with "HBM-independent" (Qualcomm), going with an LPDDR + on-chip SRAM combo. Sounds like a compromise, but it's actually a completely different path.

If this path works out, the implications for the ecosystem run three layers deep.

Layer one: part of NVIDIA's tech moat gets bypassed. The H100/B200 doesn't just rely on CUDA — it's the entire locked-in combo of CUDA + HBM + NVLink. With HBM bypassed, the logic of "you must buy NVIDIA for AI inference" loses a corner.

Layer two: more subtle implications for TSMC. NVIDIA's B200 is on TSMC 4nm, Qualcomm's new accelerator is also on TSMC, and Samsung/SK Hynix's HBM packaging has to go through TSMC for CoWoS. No matter who wins, TSMC takes orders this round — and that's why I think it's a better medium-to-long-term bet than NVIDIA.

Layer three: the most tangible impact is for people building AI applications. If Qualcomm can push inference costs down to a third of NVIDIA's at the same tier, there's a good chance the OpenAI/Anthropic API costs we're paying now drop by half within six months — and every product idea shelved because "the API is too expensive" will get pulled back out and rebuilt.

4 Practical Judgments for Investors and Builders

So I've boiled it down to four judgments.

One: don't chase NVIDIA's short-term highs. The lesson from last round's $600B+ market cap evaporation in a single day hasn't been fully digested yet. The valuation has already priced in three years of flawless execution — miss any milestone, and the pullback won't be small.

Two: TSMC benefits on both sides. Building HBM packaging for Korea, B200 for NVIDIA, the new accelerator for Qualcomm — whoever fights whom, TSMC's still collecting the orders.

Three: AI application developers don't need to wait for the hardware reshuffle. The product you're building right now on top of OpenAI/Anthropic APIs — whoever swaps in underneath is transparent to you. Migration costs are near zero. The people who wait are the ones who miss the window.

Four: over the next six months, watch two signals — power (US state-level data center power permits, Japan/Korea nuclear restart progress) and the HBM4 production timeline (2027 Q2 is the key milestone).

Closing: Capital Flows Out of Bubbles, Infrastructure Stays

This is the largest single-sector global capital deployment since the dot-com bust. There's some bubble in it, but the underlying demand is real. Money will flow out of the bubble, but when it does, the infrastructure will genuinely remain — unlike back in 2000 when all that was left was dark fiber no one used. What this round leaves behind is the foundation AI will run on for the next decade. Every product I'm building is a bet on that assumption.

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

SoftBank Announces Up to €75 Billion Investment in France for Large-Scale Data Center Construction

Judy — Wed, 01 Jul 2026 01:00:28 +0000

📰 Key Takeaways

SoftBank announced plans to invest up to €75 billion in France for building and operating large-scale data centers. The official statement revealed the goal is to develop and operate up to 5 gigawatts (GW) of additional data center capacity within France, demonstrating SoftBank's strong commitment to European AI infrastructure. 5 GW serves as the core technical figure in this announcement, providing an important basis for evaluating both the investment scale and actual computing power impact. All in all, while this investment program is substantial, the publicly available summary currently reveals only two key figures—the investment cap and capacity target. For details on phased timelines, construction locations, and partner specifics, please refer to the original article link.

💬 JudyAI Lab Perspective

SoftBank's announcement of plans to invest up to €75 billion in France to build up to 5 GW of data center capacity stands out as the most prominent single investment commitment in Europe's AI infrastructure to date—all AI builders should keep a close eye on this.

From this announcement, we can glean a clear signal: massive capital is rapidly flowing into AI computing power infrastructure, and the geographic landscape is no longer limited to the North American market. For AI builders, Europe's future computing power supply and cost structure may see structural changes as a result. Equally worth noting is that this announcement only disclosed the investment cap and the 5 GW capacity target—details on construction timelines, locations, and partners remain unreleased. This reminds us that when evaluating large-scale infrastructure investments, we need to consciously distinguish between "announced intent" and "actual implementation," avoiding premature equivalency between announcement figures and usable computing power.

When facing similar big-ticket AI investment announcements, it's advised to first clarify which figures are confirmed technical numbers versus pending execution details, then assess their actual impact on your product roadmap.

📅 Original Article Info

Publication Time: 2026-05-30T21:45
Source: https://techcrunch.com/2026/05/30/softbank-says-it-will-invest-up-to-e75-billion-to-build-french-data-centers/

References

- SoftBank plans up to €75 billion investment in French AI centers - The Japan Times

🔗 延伸閱讀

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Making Sense of the Debate Over AI Psychosis

Judy — Wed, 01 Jul 2026 01:00:08 +0000

📰 Key Takeaways

TechCrunch's Equity Podcast latest episode dives into a thought-provoking question: Are tech company CEOs more prone to falling into so-called "AI psychosis"—a term that's recently caught the industry's attention to describe how some execs develop unrealistic, almost faith-based judgment biases due to over-investment in or trust of AI systems? The show discusses whether this is tied to CEOs' decision-making positions, information echo chambers, and heavy reliance on AI for business gains, or if it's just media sensationalism. Since the original summary only provides the podcast discussion topic without specific data or case details, check the original link for detailed arguments and guest perspectives.

💬 JudyAI Lab Perspective

This podcast episode raises a red flag worth paying attention to: When a CEO's judgment of AI shifts from strategic consideration to near-faith-based reliance, the distortion risk for tech decisions doesn't just stay as a personal bias—it seeps down into the entire organization's judgment framework.

The discussion points to several structural factors: CEOs' decision-making positions naturally create information echo chambers, their heavy dependence on AI for business gains, plus the constant external hype around AI capabilities—all three叠加 make it easy for judgment to go off track. For those of us building and using AI tools in real-world scenarios, this phenomenon reminds us of one thing: The output AI systems give is still fundamentally a statistical result, not a factual arbiter. When decision-makers start equating AI's suggestions with objective truth instead of treating them as reference signals that can be questioned, that's where the blind spot forms. This isn't just an extreme case for execs—anyone who frequently uses AI in their workflow could unknowingly accumulate similar cognitive biases—the difference is just scale and impact scope.

My advice: After using AI for any key decision, spend a few minutes reversely questioning that conclusion: If AI gave you the opposite answer, how would you respond? This habit can effectively reduce blind acceptance of AI output.

📅 Source Info

Published: 2026-05-31T15:30
Original Source: https://techcrunch.com/2026/05/31/making-sense-of-the-debate-over-ai-psychosis/

References

- Special Report: AI-Induced Psychosis: A New Frontier in Mental Health | Psychiatric News

🔗 延伸閱讀

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

What happens when companies become too AI-pilled?

Judy — Sat, 27 Jun 2026 01:00:26 +0000

📰 TL;DR

Box founder Aaron Levie recently called out what he calls "AI psychosis" — the phenomenon where executives who greenlight "AI can replace this job" are often the ones who know the least about what that job actually entails. He's warning that this decision-making blind spot is spreading across tech — decision-makers, overconfident in AI's potential, are rushing to implement mass layoffs without truly understanding workflows, role nuances, or the human judgment required.

In a concrete case, collaboration platform ClickUp just announced cutting 22% of its workforce, explicitly stating it will use AI Agents to take over those functions. This wave of layoffs has pushed 2026's total tech layoffs to nearly match all of 2025 — before even reaching the mid-year point — showing AI-driven workforce reduction is accelerating.

Levie's core argument isn't against AI adoption, but rather a warning: companies rushing to replace human labor with AI lack deep understanding of the work itself. When decision-makers aren't familiar with the actual complexity of the roles being replaced, they tend to overestimate AI's real coverage capability, ultimately hurting organizational efficiency. This "over-AI'd" thinking is becoming a new management risk in Silicon Valley. Full interview available at the source link.

💬 JudyAI Lab Take

The "AI psychosis" Levie pointed out is a red flag worth every AI implementer paying attention to: the execs loudest about "AI can replace this job" are often the ones most unfamiliar with that role — that's the real decision blind spot.

ClickUp's 22% headcount reduction replacing roles with AI Agents has already pushed 2026 tech layoffs near matching all of 2025 — before midyear. There's a wake-up call for the AI builder community here: the环节 where automation design fails most isn't tech selection, but insufficient understanding of the "work being automated." When designing Agent flows or workflows, skipping deep interviews with actual executors easily leads to overestimating AI's coverage — automating visible steps while missing a lot of implicit human judgment and exception handling. Levie's critique is essentially a requirements analysis problem: not understanding the true complexity of a job means more AI investment can lead to bigger organizational efficiency losses.

Before planning any AI replacement plan, talk to the people actually doing that job first. Ask them "what do you know that nobody else knows?" — that's often exactly where AI falls shortest.

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

OpenAI Strengthens Societal Resilience with Rosalind Biodefense

Judy — Sat, 27 Jun 2026 01:00:06 +0000

📰 Key Highlights

OpenAI officially launches the "Rosalind Biodefense" program, expanding access to the biology-specific model GPT-Rosalind to specific groups. This open access adopts a "trusted access" mechanism, with eligibility limited to two groups: vetted developers and US government partners advancing biodefense work. In terms of application targets, the program focuses on three core areas: biodefense capability building, public health strengthening, and pandemic preparedness. The overall positioning aims to enhance societal resilience through cutting-edge AI technology. The design of "trusted access" means that GPT-Rosalind is not a publicly available general-purpose model. OpenAI has adopted strict user eligibility control strategies for sensitive AI applications involving national security and public health, rather than opening them directly to the public. Since the original summary does not provide details such as model technical specifications, specific partner institutions, or eligibility screening criteria, please refer to the original link for more details.

💬 JudyAI Lab Perspective

OpenAI's introduction of a "trusted access" mechanism for biodefense—limiting access to high-risk AI models to vetted developers and government partners—is a clear signal that AI governance is moving from "open sharing" toward "controlled layering."

This case reflects: when AI capabilities touch sensitive domains like national security and public health, the design logic of "who can use it" itself becomes the core of the product. Instead of making it publicly available, OpenAI has chosen to build a user circle through eligibility screening, meaning that the access architecture itself becomes a governance tool, not just a commercial threshold.

For AI builders, this reveals a thought-provoking product design question: beyond powerful features, does your system also have the capability for "differentiated access layers"? Competition in highly sensitive applications may not just be about model performance—it may become a competition over trust infrastructure. Whether governments and high-standard institutions are willing to accept your eligibility screening process will become the core ticket to enter these markets.

If you're planning or developing AI applications in highly sensitive fields, you should start designing your "access layering strategy" now, clearly defining eligibility thresholds for different user groups—rather than waiting for problems to emerge before adding controls.

📅 Source Information

Published: 2026-05-29T03:00
Original Source: https://openai.com/index/strengthening-societal-resilience-with-rosalind-biodefense

References

- OpenAI Bets on AI for Biodefense | StartupHub.ai

🔗 延伸閱讀

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.