EU Drops Final Code of Practice for Foundation Models -Here’s What’s Inside (and Why Big Tech Is Sweating)

The European Commission’s new voluntary playbook for general-purpose AI kicks off a 12-month countdown to hard-law enforcement. We break down the must-know clauses, industry push-back, and global ripple effects.

Published: 11 July 2025 · 7-min read · by AI Trend Scout

TL;DR Brussels just released the final General-Purpose AI Code of Practice. It is “voluntary” for now but becomes legally binding on 2 August 2026 with fines up to €35 million or 7 % of global revenue. Transparency disclosures, copyright filters and safety stress-tests are now table-stakes for anyone shipping foundation models in Europe.

1. What happened?

The European Commission quietly published its long-awaited General-Purpose AI Code of Practice on 10 July 2025, giving model builders a one-year grace period to align with the AI Act’s next enforcement wave. Although the document is branded “voluntary,” it is effectively a dry-run for legally binding obligations that kick in on 2 August 2026—a timeline the Commission insists will not be delayed.

2. Why this matters

  • Global reach – Any model that ends up in EU products or services is covered.
  • Clock is ticking – One year of voluntary compliance, then real fines (up to €35 million or 7 %).
  • GDPR déjà-vu – The code is being pitched to G7 partners as a template.
  • Reg-tech boom incoming – Get ready for tools that automate model cards, dataset audits and watermarking.

3. What’s inside the Code?

  1. Transparency Pack
    Model Cards with architecture, training-data summaries, evaluation scores and energy footprints.
    Dataset provenance plus mandatory labelling of synthetic content.
  2. Copyright Guard-Rails
    • Track copyrighted works in training data or compensate rightsholders.
    • Provide an opt-out and quick takedown channel for creators.
  3. Safety & Systemic-Risk Controls
    • Compulsory red-team reports.
    • Alignment tests and kill-switch procedures for frontier-scale models.
  4. EU AI Office Oversight
    • A public registry of GPAI models plus annual stress-tests—real enforcement powers start in 2026.

4. Industry reaction: “Stop the clock!”

More than 40 heavyweight European brands – Airbus, Mercedes-Benz, Philips, even open-source darling Mistral—signed an open letter urging a two-year delay, calling the rules “unclear, overlapping and increasingly complex.” Brussels’ response: no grace period, no pause.

5. What happens next?

TimelineMilestone
Jul 2025Member States review the Code’s adequacy; Commission issues guidance.
2 Aug 2025AI Act “Wave 2” risk-based rules begin (high-risk systems & GPAI disclosures).
2 Aug 2026Code’s requirements become binding law; fines and market bans start.

6. The bigger picture

  • Open-source frameworks (e.g., Hugging Face) are racing to bundle “EU-ready” compliance kits.
  • VC term sheets now come with “AI-Act-ready” warranties—echoes of GDPR clauses in 2018.
  • US policymakers lose their favourite talking point (“heavy-hit regulation can’t be done”). Watch for renewed lobbying in Washington.
  • Start-ups may pivot to smaller, domain-specific models to dodge exhaustive reporting overhead.

🎯 Key takeaway for builders

If your model touches an EU user, you have 12 months to: document data, prove safety, and label everything—or budget for a compliance team larger than your research team.

Further reading

  • European Commission press release, General-Purpose AI Code of Practice now available (10 July 2025)
  • AP News, EU unveils AI code of practice to help businesses comply with bloc’s rules (11 July 2025)
  • TechXplore, More than 40 EU companies ask Brussels to delay rules (11 July 2025)

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Chai-2: The AI Model Turning Antibody Discovery into a Two Week Sprint

TL;DR

Chai Discovery has unveiled Chai-2, an all-atom generative foundation model that designs functional antibodies “in a single shot.” In lab tests it produced binders for 16 % of sequences on the first try, slashing discovery timelines from months to roughly two weeks.

SEO Metadata

  • Title (tag): Chai-2 Shatters Antibody Design Records with 16 % Zero-Shot Hit Rate
  • Meta Description: Chai Discovery’s Chai-2 AI model delivers a game-changing 16 % hit rate in de novo antibody design—100× better than traditional screens—promising faster, cheaper biologic drug discovery.
  • Keywords: Chai-2, zero-shot antibody design, generative AI biotech, de novo antibodies, AI drug discovery, protein generative model, Chai Discovery, all-atom foundation model

Chai-2: The AI Model Turning Antibody Discovery into a Two-Week Sprint

Published July 6 2025

TL;DR

Chai Discovery has unveiled Chai-2, an all-atom generative foundation model that designs functional antibodies “in a single shot.” In lab tests it produced binders for 16 % of sequences on the first try, slashing discovery timelines from months to roughly two weeks. (marktechpost.com, chaidiscovery.com)


Why This Story Matters

  • 100× leap in hit rate over conventional computational pipelines (0.1 %)—a step-change comparable to AlphaFold’s impact on structure prediction. (biopharmatrend.com)
  • Faster therapeutic pipelines: viable leads in days unlock rapid response to emerging pathogens and hard targets.
  • Shift to “programmable biology”: designing at the atomic level, not hunting in wet-lab haystacks.

The Breakthrough in Numbers

MetricChai-2Prior in-silico design
Experimental hit rate (antibodies)16 % of first-round designs~0.1 %
Targets with ≥1 hit50 % of 52 novel antigens<5 %
Miniprotein binder hit rate68 % (5 targets)n/a

All assays run in a 24-well plate; 20 designs per target. (chaidiscovery.com)


Under the Hood — How Chai-2 Works

  1. Multimodal Architecture
    Blends a large-scale language model (sequence) with a diffusion-style 3-D generative component that reasons over full atomic coordinates.
  2. All-Atom Training
    Trained end-to-end on antibody–antigen complexes plus miniprotein scaffolds; no multiple-sequence alignments needed, cutting compute. (biopharmatrend.com)
  3. Scaffold-Free CDR Design
    Generates completely new complementarity-determining regions (CDRs) conditioned on an epitope map—no template libraries.
  4. In-Silico Ranking → Instant Wet-Lab
    A fast docking head scores thousands of sequences; top 20 are synthesized and screened in a single ELISA pass.
  5. Two-Week Cycle
    Compute → synthesis → assay → hit confirmation in ~14 days, enabling iterative model refinement.

How It Beats Existing Methods

  • Library Size: 20 sequences vs. millions in phage/yeast display.
  • Generalization: Produced binders to TNF-α, a notoriously flat epitope, showing ability to tackle so-called “undruggables.” (biopharmatrend.com)
  • Modalities: Designs scFv, VHH nanobodies, and miniproteins from the same backbone.

Early Reactions

“Double-digit zero-shot hit rates blow past what we thought possible. It’s the first credible path to on-demand biologics.” — Independent biotech VC (LinkedIn stream, July 5) (linkedin.com)

Investors who backed Chai’s $30 M seed—including OpenAI and Thrive Capital—see it as a foundation model for molecular engineering. (biopharmatrend.com)


Caveats & Next Steps

LimitationChai team’s plan
Assays in scFv/VHH only—affinity may shift in full IgG formatReformat top hits, test stability & pharmacokinetics
Partial developability profiling (aggregation, viscosity)Integrate manufacturability predictors into generation loop
CDR loop flexibility still trickyImprove backbone sampling & fine-tune with cryo-EM data

The company is selectively opening access under a Responsible Deployment policy to mitigate dual-use bio-risk. (chaidiscovery.com)


Big-Picture Impact & Ethics

  • Pandemic readiness: Software-based antibody generation could compress months of scramble into days.
  • Biosecurity risk: The same tech could design harmful binders; controlled access and auditing are crucial.
  • Economic shift: Contract research orgs may pivot from high-throughput screening to high-throughput computation.

Bottom Line

If AlphaFold cracked protein folding, Chai-2 may crack protein creation. With a 16 % zero-shot hit rate in hand, programmable biologics just jumped from speculative to tangible—and every drug-discovery team will be paying attention.

Want more? Ping me for a deep-dive Q&A with Chai’s founders or a visual explainer of the generative pipeline.