AI starts cracking “open” math: GPT-5.2 and Gemini’s AlphaEvolve help solve

Frontier AI is starting to unlock pieces of “open” math. That matters for Morocco’s labs, startups, and classrooms today. It signals progress in rigorous reasoning, not just fluent text.

Moroccan teams can use this shift to raise quality and speed in research and engineering. The same tools behind formal proofs can validate models, optimize operations, and reduce risk.

Key takeaways

• TechCrunch reports frontier models now make measurable progress on Erdős problems.

• Verification matters: formal proof tools reduce hand‑waving and catch errors fast.

• Morocco can apply these methods to logistics, finance, public services, and education.

• Constraints remain: compute access, data governance, skills, and procurement.

• Start small, verify results, and build bilingual workflows in Arabic, French, and English.

The news: frontier models push into “open” math

TechCrunch highlights a weekend experiment by engineer Neel Somani. He prompted OpenAI’s newest model, referred to as GPT‑5.2, on a hard math problem. The model produced a full argument after about 15 minutes. Somani then formalized it using Harmonic’s tools, and the proof checked out.

The report says the model’s reasoning looked unusually mathematical. It referenced known lemmas, theorems, and even found a related 2013 MathOverflow post by Noam Elkies. TechCrunch notes the final proof differed in important ways and addressed a version of a question linked to Paul Erdős. That makes it harder to dismiss as just retrieval or paraphrase.

TechCrunch also points to a broader pattern. A Gemini‑powered system called AlphaEvolve reportedly posted “autonomous” results earlier. Since Christmas, TechCrunch cites a shift on the Erdős list: 15 problems moved from open to solved, with 11 crediting AI help. Mathematician Terence Tao is tracking “meaningful autonomous progress” cases on GitHub.

For Morocco, the signal is practical. Verifiable reasoning is entering mainstream tooling. Local labs and startups can piggyback on these methods without massive budgets.

TechCrunch stresses credibility as the adoption trigger. It cites Harmonic’s founder saying respected researchers publicly acknowledge using tools like Aristotle or ChatGPT. In Morocco, reputational dynamics also matter. Professors, engineers, and regulators will move faster when verification is routine.

Why the long tail matters, and Morocco’s opportunity

Tao has suggested that AI may excel at the “long tail” of neglected problems. Many such problems have simple solutions that humans never prioritized. AI can apply systematic search and formal checks at scale. That favors breadth and cleanup over flashy one‑offs.

Morocco has similar long tails in practice. Routing small fleets, cleaning messy datasets, and checking compliance rules all hide low‑hanging value. Tools that formalize reasoning can chip away at these backlogs. They can help teams deliver reliable, auditable results.

The core idea is simple. Stronger models generate candidate solutions. Proof assistants and automated checkers then verify each step. Moroccan teams can adopt that workflow for operations, modeling, and code.

Morocco context

Compute access is uneven across Morocco. Some teams rely on cloud credits or shared clusters. Others face bandwidth limits or strict procurement cycles. This affects training, inference costs, and iteration speed.

Language is a daily constraint. Data and documents span Arabic, French, and sometimes Tamazight. Many technical resources are in English. Any Moroccan rollout must support bilingual or trilingual workflows.

Data governance is essential. Sensitive public and financial datasets need careful handling. Cloud region choices, vendor contracts, and audit trails become key. Teams should align with local data protection rules and sector norms.

Skills are improving but remain scarce. Advanced math, MLOps, and formal methods are niche skills. Assumption: universities and institutes are expanding AI curricula and research groups. Partnerships and remote mentorship can help bridge gaps.

Procurement can slow pilots. Contracts often favor established vendors and long cycles. Lightweight proofs‑of‑concept with clear metrics can unlock approvals. Morocco’s SMEs benefit from small, well‑scoped projects that show measurable returns.

Use cases in Morocco

1) Public procurement and policy checks

Large contracts mix legal, technical, and financial requirements. AI can map clauses, find conflicts, and propose fixes. Formalization helps convert rules into testable checks. This suits Morocco’s multilingual documents and varied tender formats.

2) Logistics and delivery routing

Grocers, wholesalers, and e‑commerce players juggle dense urban routes and regional hubs. AI can search long‑tail routing tweaks that humans miss. Formal constraints ensure delivery windows, load limits, and road rules hold. Moroccan SMEs can start with a subset of depots.

3) Agriculture scheduling and input optimization

Cooperatives face water, fertilizer, and labor trade‑offs. AI can test many feasible plans, then verify constraints like quotas and safety. Simple sensors and historical logs are enough to start. Reports and prompts should support Arabic and French.

4) Banking risk and fraud investigation

Models can surface unusual transaction paths and policy edge cases. Formal rule checks reduce false positives and explain decisions. Human analysts remain in the loop. This approach suits Morocco’s regulated financial environment.

5) Manufacturing quality and maintenance

Plant teams track incidents, manuals, and supplier specs, often in French. AI can build verifiable checklists and compare against standards. Proof‑style reasoning helps ensure each step meets requirements. Start with a single line or subsystem.

6) Education and research training

Math and CS departments can use Lean to teach rigor. Students learn to turn arguments into checkable proofs. That skill transfers to software verification and data analysis. Assumption: extracurricular clubs can run small Lean and Python labs.

Skills, tools, and infrastructure for Moroccan teams

Start with the tools TechCrunch mentions and their open cousins. Lean, developed in 2013 at Microsoft Research, is widely used for formal proofs. Harmonic and its formalization‑oriented model, Aristotle, illustrate an emerging workflow. Alternatives exist, but the pattern is stable: propose, formalize, verify.

Pair Lean with Python notebooks and basic MLOps. Use version control, unit tests, and continuous integration. Add prompt trackers and evaluation sets. Keep artifacts bilingual when possible.

Evaluate models with clear metrics. Track accuracy, verification success rates, latency, and cost per task. Sample manually in Arabic and French. Document failure modes and escalation paths.

Balance cloud and on‑premise options. Cloud offers quick starts but needs careful data governance. On‑premise reduces data exposure but adds maintenance. Many Moroccan teams will combine both.

Invest in people. Train one or two “formalization champions” per team. Encourage cross‑functional reviews between domain experts and AI engineers. Knowledge sharing compounds quickly in small ecosystems.

Risks & governance

AI that “sounds right” can still be wrong. Formalization reduces risk but does not remove it. Moroccan organizations should build layered checks. Humans must remain accountable for high‑impact decisions.

Key risk areas for Morocco:

• Privacy and data residency: avoid exporting sensitive data without safeguards. Align with local rules and sector norms.

• Bias and fairness: test across languages and dialects. Watch for unequal error rates.

• Hallucinations and overreach: require verification and citations for claims. Prefer tools with audit logs.

• Academic integrity: clarify acceptable AI use in coursework and research. Require disclosure of assistance.

• Procurement and vendor lock‑in: negotiate exit clauses and data portability. Avoid single‑source dependencies.

• Cybersecurity: protect model prompts, keys, and datasets. Audit third‑party integrations.

Set governance early. Define roles, risk tiers, and escalation. Use lightweight model cards and data sheets. Keep a record of what was decided and why.

What to do next

Here is a pragmatic 30/90‑day plan for Morocco. Adjust scope to your budget and compliance needs. Mark assumptions explicitly when needed.

Startups and SMEs

• 30 days:

• Pick one process with measurable value, like routing or contract review.

• Prototype with a managed model and Lean or an equivalent checker.

• Build a bilingual prompt and evaluation set.

• Write a one‑page governance note and access rules.

• 90 days:

• Integrate verification into CI for that process.

• Add cost and latency dashboards.

• Run an A/B test against current methods.

• Draft a second use case if ROI holds.

Public sector teams

• 30 days:

• Choose a low‑risk pilot, such as document deduplication or rule cross‑checks.

• Form a triad: domain owner, AI engineer, and legal/compliance reviewer.

• Define data handling and redaction steps for Arabic and French texts.

• 90 days:

• Publish pilot metrics and a lessons‑learned memo.

• Prepare a small procurement template that favors open standards.

• Assumption: consult with oversight bodies on data practices.

Universities and students

• 30 days:

• Run an intro workshop on Lean and prompt evaluation.

• Assign one formalized proof per student in a relevant course.

• Encourage bilingual documentation for all exercises.

• 90 days:

• Launch a reading group on formal methods and applied optimization.

• Partner with a local SME for a verified AI capstone.

• Create a disclosure policy for AI assistance in coursework.

Investors and donors

• 30 days:

• Fund compute vouchers tied to verified, open evaluation reports.

• Support shared datasets in Arabic and French with clear licenses.

• 90 days:

• Back toolchains that improve verification and observability.

• Sponsor internships that pair formal methods with real operations.

Outlook for Morocco

The pattern is clear. Better models plus formal verification are moving problems from “open” to “solved.” TechCrunch frames January 2026 as a visible inflection. The effect is measurable and verifiable.

Morocco can ride this wave without chasing hype. Focus on small, auditable wins. Support bilingual workflows and conservative data practices. Build capacity in verification, not just prompting.

This shift is not about AI “doing math like humans.” It is about pairing automated search with tools that check every step. That mindset travels well across Morocco’s sectors and languages.

The next 12 months will reward teams that measure first and scale second. Morocco’s advantage is practical creativity and multilingual agility. Use those strengths with rigorous verification. The results will compound, one checked step at a time.