If this ever became widespread, people would game the "verified real" checkmark using the analog gap.

1. Print AI photo 2. Point fancy expensive camera at printed photo 3. Take a "real" photo

Then you'd see more sensors and even more expensive cameras. Then you'd see miniatured virtual-production volumes.

So, this is not a solution. It's just an arms race disguised as one.

This would make deepfakes more expensive because you would have to tightly control the chroma and illuminance of the display.

Something a calibration tool surely can do I assume.

Fakes were always possible. Just labor intensive or costly.

Can you photograph a photo so it’s not really noticeable?

Yes to arbitrarily good degree with hobby grade tooling. The previous gen photo tech (b&w negative film, colour negative film) used device called enlarger† which is pretty much taking a photo of a photo.

† https://en.wikipedia.org/wiki/Enlarger

I've been able to get some truly great digital copy of old analog printed photo using Google snapseed and a sub 1k€ smartphone and that was almost a decade ago, so I'm pretty sure anyone dedicated can.

The OP rediscovered that frontier models prefer to reason over logical scaffolding for complex tasks. They excel at technical work with many constraints as they can perfectly maintain the references, flow graph, and evidence states while they works through your conformance gates.

Many people here might disagree; but they're holding it wrong. Those folks should ask:

1. Am I using free tier tokens? 2. Am I working on trivial software? 3. Am I expecting models to adapt to my ways of thinking?

Anyone affect by any of these three mistakes will maintain an impenetrable filter of perpetual ignorance about model capabilities. Since the OP came with receipts, I'm reproducing an example graph below.

From a plan in my active research project:

  Phase 0   resolve -> vendor -> pin             [USER+agent]  -> G0 CORPUS-RESOLVED   [PASS]

  Track B   B1 Abusalah              [WRITTEN]   [agent]       -> G1B Q-BOUNDARY
            B2 Guan-Riazanov-Yuan    [WRITTEN]                  (KILL-BOUNDARY risk)
            B3 Mahmoody-Smith-Wu     [WRITTEN]  + O-MSW-VERIFIER-COST closed (t = 588, MEASURED)
                                     -> G1B PASSED: BOUNDARY-ESTABLISHED

  Track A   05 Binius                [WRITTEN]   [agent]       -> G1 AUDITS-COMPLETE [PASS] 2026-08-28
            07 FRI-Binius 2024/504   [WRITTEN] — vendored by USER; calculated Q-FLOOR point obtained
            06 HOBBIT                [WRITTEN] — O-QUEUE-M escalated to 179 Phase 2
            01 Wesolowski            [WRITTEN] — hinge splits; C_overlap = 0, C_tail = 57.50% computed
            02 Tight VDFs            [WRITTEN] — no omega; O(log T) proof threads; black-box RO base impossible
            03 LaBRADOR (light)      [WRITTEN] — calculated, not measured; translation remains open
            04 LatticeFold+          -- deferred to pre-141

  Track C   [PASS] C0a contract -> C0b exact simulator -> G0C TRANSLATION-READY
            C2 Alwen-Serbinenko [TECHNIQUE-ONLY] -> C1 SoW [PASS] ANCESTRY-FOUND
                                                -> C4 Seeded PoW [FORM-ONLY]
                                                                 -> G1C [RED] WORK-LEMMA-MISSING
                                                                          [WARN] ANCESTRY-CLASSICAL
                                                                 -> L0 [PASS] SCOPE-FROZEN
                                                                      |- E0 response [PASS] PRESENT
                                                                      `- L1-L2 local [PASS] CARRIER-QUOTIENT
                                                                           -> J0 [PASS] TECHNIQUE-FIT CONFIRMED
                                                                                -> admission decision [PASS] CANONICAL RESULT
                                                                                     -> A0 [AMBER] REFERENCE-TESTED
                                                                                          -> A1 [PASS] ROOT-LEAF FEASIBILITY LEAD
                                                                                               -> semantic equivalence + epoch [USER]
            C3, C5  -- deferred to pre-141

  Phase 2   Q-TAIL / Q-FLOOR synthesis           [agent]       -> [PASS] G2: TAIL-MECHANISM-FOUND · FLOOR-OPEN
  Phase 3   reference ledger for 141              [agent]       -> CLOSED; awaits fresh USER authorization

AI will not begin to benefit student learning unless educators pivot into a strict bifurcation pattern around access.

For example, alternating weeks with and without the permission to use AI assistance. This is also the latent solution for corporate de-skilling that no one seems to be talking about yet.

However, it won't happen (in both cases) because we're too early. Domain experts like the OP are still relinquishing their expertise to unlock productivity gains. Once that bottoms out there will be no longer be demand for people like OP to betray their peer groups.

Learning with AI assistance be as frictionless and productive as I suppose it can, but de-skilling and job consolidation (role expansion) will continue.

I've been using my own variant docset driven development since late last year. It emerged organically for me by adapting to the limits of the agents.

- Prefix-numbered sets of documents, keeping them clustered visually and easily referenced by humans and agents. (124.5 = docset 125, phase 5) - Each doc gets a suffix. Most of the time STRATEGY is the first doc. Sometimes NOTES, DISCUSSION, or HANDOFF though. - Once the strategy doc is comprehensive, the multi-phase checklist PLAN doc can emerge. - During execution, the agent drives the PLAN while purposefully expanding lightly scaffolded phases before entry. - As the user, I drive a PROGRESS doc during the PLAN execution. This tracks the progression of my own questions and the important work summaries that I need for tracking the current work trajectory and for historic analysis over past decision-making. - Any concern that exceed the scope of the active docset numbers calls for a new one. - A "docs/archive" folder is kept where I sweep docsets into numbered eras.

I have aliases and templates too, but DDD is so simple that it's overkill.

Any modern reasoning agent can take the plain explanation above and understand you effortlessly when you say "open a new docset strategy" or "read docset 125 and proceed".