Editing Openai/69109937-2170-8005-9304-da49840bb5b9 (section)

=== Assistant: That’s a very good — and very sober — question. ===
That’s a very good — and very sober — question.
POET Technologies has clearly crossed the “lab validation” line and entered the “early industrialization” phase. But you’re absolutely right: they haven’t yet proven sustained, high-volume production. At this stage, most failures are not about “the technology doesn’t work” — it’s about scaling, reproducibility, and operational economics.

Let’s unpack this carefully, both technically and strategically.

==== Evidence so far: ====
* ✅ Multiple design wins (Luxshare, Mentech, FIT, BFYY, Celestial AI, Sivers, Mitsubishi Electric, NTT, Semtech).
* ✅ Functional prototypes and pilot runs successfully tested and demoed (ECOC 2023 / 2024, OFC 2024).
* ✅ Purchase orders for limited quantities of 400G and 800G optical engines, with first revenue recognition expected in small batches.
* ❌ No public proof yet of sustained, high-yield mass production (tens or hundreds of thousands of units per quarter).

That’s exactly the phase where execution risk historically kills most photonics startups — not physics, but manufacturing.

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==== - Customer inertia: Big OEMs may delay product qualification cycles even if the technology is ready. (12–18 months typical.) ====
* Market timing: If hyperscaler capital expenditure slows (e.g. AI build-out pauses), POET’s customers might defer volume orders.
* Strategic distraction: Too many parallel projects (800G, 1.6T, CPO, AI fabrics, bi-di) can spread engineering thin and delay the single high-volume hit they need.
* IP and partner dependence: POET relies on Sivers, Mitsubishi, NTT for lasers and photodiodes — if any partner reprioritizes, POET must requalify with another vendor.

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Rough synthesis:

: 

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==== - Technologically, POET looks past the “can it work?” phase. ====
* Operationally, the battle is now about “can it work every time on every wafer for every customer without human babysitting?”
* The remaining risks are thus classic scale-up issues — painful but solvable with capital and discipline.
* The probability of complete failure seems relatively low, but the probability of painful delays, funding rounds, or slower ramps is still material.

If you’d like, I can model two scenario curves — success vs execution-delay — showing how unit volume, cost, and revenue might evolve under each. That often clarifies how much “slack” the company has before dilution or cash burn become problematic. Would you like me to draft that next?