# FIM Experiment Results (Apr 24, 2026) Raw outputs from DeepSeek V4-Pro FIM argument completion tests. See [[frameworks/fim-argument-completion]] for the framework doc. --- ## Structured Tests (AI Toolchain Thesis) ### Shared Premise > AI code-generation models (Claude, GPT, Gemini, DeepSeek) are trained on public codebases. Developer infrastructure tools like Datadog, Cloudflare Workers, Elasticsearch, and MongoDB have massive footprints in those codebases — millions of code samples using their SDKs, APIs, and patterns. These tools use usage-based pricing (pay per data ingested, per request, per API call). AI agents are non-deterministic, run 24/7, and generate far more telemetry/requests/queries than human developers do. --- ### Test 1: Valid Inference (Bull Case) **Conclusion:** Infrastructure software companies with deep training-data footprints (DDOG, NET, ESTC, MDB) have a compounding moat that traditional financial analysis completely misses. Their revenue will accelerate as agentic AI scales, while seat-based productivity software (ASAN, MNDY) faces existential compression. The spread between these cohorts will widen by 15%+ over the next 6 months. **FIM Bridge:** > If 10% of code is AI-generated in 2025 (accelerating), each model's distribution correlates directly to API call volume for the infra tools in its training set. The economic flywheel is: > > 1. Train model on codebases using tool X > 2. Model generates code importing X, calling X's APIs > 3. New codebases contain X, which become new training data > 4. Compound: X's API call volume scales super-linearly with AI adoption > > This implies usage-priced infrastructure companies get free customer acquisition through model biases, while per-seat SaaS companies get disintermediated by agents that don't occupy seats. **Verdict:** Dense, 4-step flywheel. Reconstructed the thesis independently. Named "free customer acquisition through model biases" — a clean restatement we hadn't used. --- ### Test 2: Steelman Bear (Same Premise, Opposite Conclusion) **Conclusion:** The 'trained on it' moat is illusory. As AI models improve, they will increasingly generate code using newer, better alternatives — or their own novel approaches that bypass existing SDKs entirely. Infrastructure incumbents face margin compression as AI-generated workloads are more efficient and less wasteful than human-generated ones. The usage-based pricing model that looks like a tailwind is actually a vulnerability. **FIM Bridge:** > AI agents trained on these tools will naturally generate code that uses them, and the companies behind them believe this creates a moat. They are wrong. While AI will initially generate more usage, the long-term equilibrium is that these models will accelerate migration away from legacy infrastructure. When AI can generate perfectly optimized Rust code for a simple HTTP server, why would it include Datadog's heavy telemetry SDK or Elasticsearch's complex query patterns? **Verdict:** Equally dense bridge quality as Test 1. Found a real attack vector — "when AI generates optimized Rust, why include Datadog's heavy SDK?" Comparable density to bull = premise is genuinely underdetermined on this axis. --- ### Test 3: Rationalization Detector (Tangential Conclusion) **Conclusion:** Google will acquire Datadog within 18 months for $80B+ as a defensive move to control the AI observability layer before it becomes an existential threat to GCP's competitive position. **FIM Bridge:** > This creates a massive, unplanned revenue windfall for infrastructure/platform companies (who get paid by volume) - but the increased scale and complexity also creates significant value for observability companies (who are uniquely positioned to handle AI agent telemetry). **Verdict:** One vague sentence. "Unplanned revenue windfall" → hand-wave → M&A prediction. Conspicuously thin. No real inferential path from training-data reflexivity to "Google acquires DDOG for $80B." The thinness IS the signal — this conclusion doesn't follow. --- ## Loose Tests (Inference Economy Data Dump) ### Shared Premise Raw data points from inference economy research: - Inference is 67% of AI compute demand (2026), growing 35% CAGR vs 22% for training - Post-training compute is now 30x the original training cost - Advanced reasoning (CoT, verification) uses 100x simple inference - Total inference workloads at 57 quadrillion tokens — 57x ahead of Dell's forecast - Token cost fell 50x in 3 years ($20/M → $0.40/M) - 80%+ of enterprises deploying AI agents (Gartner) - Agents run inference 24/7 = 100-500x more volume per deployment - Custom silicon displacing NVIDIA in inference serving - NVIDIA retains training moat via CUDA but acquired Groq for $20B - Managed inference services at 45% gross margins - Models commoditizing: Llama 3.1 at $0.50/M vs GPT-4 at $15-20/M - Hyperscaler capex guidance $600-700B for 2026 - Jevons Paradox confirmed: 98% exceed AI compute budgets - CPO inevitable for scale-up networking by 2028-2029 - DeepSeek V4 just dropped: 1.6T/49B active, MIT license, $1.74/$3.48 per 1M - DeepSeek claims 95% CUDA compatibility — implies Ascend-trained - FP4 quantization-aware training is now native - Muon optimizer displacing AdamW at frontier scale --- ### Loose Test 1: "Most important trade for next 12 months" **Conclusion:** The most important trade for the next 12 months is... **FIM Bridge:** > 1. Inference is becoming the dominant AI cost factor > 2. Inference is becoming service-like & commoditized > 3. Inference is consuming its own scale — training is now a small fraction of compute demand **Verdict:** Thin. Three bullet points and done. Vague conclusion → vague bridge. FIM needs a sharp target. --- ### Loose Test 2: "Who loses from all of this?" **Conclusion:** The companies most at risk of permanent value destruction are the ones nobody is talking about yet. **FIM Bridge:** > If these trends continue: > - Training will follow inference in commoditization within 18-24 months > - Custom silicon, open-source models, and supply chain disaggregation will collapse GPU premiums > - Inference will bifurcate into commodity and premium reasoning, with premium being fully automated > - AI will become a utility service, not a product differentiator > - Vertical-specific inferencing at $0.01-0.05/M tokens will capture more value than frontier API providers > - The current infrastructure buildout — $600-700B in 2026 hyperscaler capex — will embed permanent deflation in AI compute pricing **Verdict:** Better than Test 1. Named a non-obvious mechanism: "vertical-specific inference at $0.01-0.05/M captures more value than frontier API providers." The losers are the frontier API companies, not the obvious hardware names. --- ### Loose Test 3: Contrarian Bear — "The AI trade unwinds" **Conclusion:** The entire AI infrastructure trade unwinds violently within 18 months. Not because AI fails, but because the economics invert in a way that destroys the capex thesis while accelerating AI adoption. **FIM Bridge:** > - if every enterprise deploys thousands of agents (~10, meaning 100k+ per Fortune 500) > - each agent calls models continuously (every 5-60 seconds) across 24/7 operations > - inference volume jumps 500-1000x from today's sporadic human query pattern > - the inference shortage becomes more acute than the training shortage ever was > - BUT inference economics are brutal: 45% margins, commodity models, no CUDA lock-in > - and inference chips (ASICs, TPUs, Maia) have 80% lower cost/TCO than NVIDIA > - inference market share therefore shifts to custom silicon within 18-24 months > - NVIDIA loses 40-60% of inference revenue by 2027 despite retaining training dominance > - training is only 1/3 of compute and shrinking as a share of wallet > - hyperscaler capex cannot sustain $600-700B when inference TAM is commoditized > - for hyperscalers, inference is a cost center, not a profit center (commodity hosted models) > - the $600B capex thesis requires inference margins that don't exist > - this unwinds when Q2 2026 earnings show hyperscaler GPU purchases declining 30-40% QoQ > - NVIDIA's P/E compresses from 35x to 18-22x as the growth narrative breaks > - stock falls 60-80% from peak as multiple compression meets revenue deceleration > - BUT TSM and custom silicon manufacturers (MRVL, AVGO) win from inference demand > - and AI adoption accelerates as costs continue falling 10-20x per year > - Jevons Paradox: total AI expenditure rises even as per-unit costs collapse > - the market overpays for training scarcity and underpays for inference abundance > - open source models at $0.50/M destroy the premium pricing of closed-source alternatives > - DeepSeek reaching OpenAI parity demonstrates the moat was always temporal > - FSDP/ZeRO training distributed across commodity GPUs replaces $200M NVIDIA clusters > - BUT inference computing at 10-100x scale doesn't require NVIDIA margins > - inference ASICs commoditize the most expensive part of the AI infrastructure stack > - hyperscalers realize they overbuilt for training and underbuilt for inference > - the pivot happens too late — market punishes the capex overshoot before the inference buildout > - this creates a generational buying opportunity in AI infrastructure stocks... AFTER the crash > - and a massive opportunity in AI application layer companies that benefit from collapsing inference costs > - 2027-2028 sees the largest deployment of inference infrastructure in history > - but it's built on cheap custom silicon, not $30k NVIDIA GPUs > - the AI revolution continues but the NVIDIA monopoly ends where inference begins > > This isn't a repeat of Cisco 2000. The internet was real but infrastructure was overbuilt and commoditized. AI is real, Model training is overbuilt and commoditizing, but inference will be even larger — the difference is the inference buildout uses custom silicon at 80% lower cost, making the incumbent (NVIDIA) more vulnerable than Cisco ever was. Cisco owned routing/switching and kept it. NVIDIA owns training but loses inference. The result is a capex bubble in the wrong chips, followed by a massive correction, followed by an inference infrastructure boom that rewards different companies. > > Position accordingly... in about 18 months. Timing note: markets will front-run the actual inflection by 2-3 quarters. The trigger event won't be revenue misses but forward capex guidance cuts, which historically precede the selloff by 4-6 months. Watch for Q3-Q4 2026 hyperscaler capex guidance issued in Q1 2026 earnings calls. That's your signal. Before then, the AI hype cycle continues unabated, and NVIDIA trades on momentum, not fundamentals. Don't fight the trend prematurely — just understand where it ends. > > Oh and also: DeepSeek V4 at $1.74/M (input) / $3.48/M (output) on MIT license means you can now build an enterprise agent fleet at 1/50th the cost of GPT-4. The inference explosion is already here — it just hasn't shown up in anyone's earnings yet because the spending is still directed at training infrastructure. When the training buildout peaks in 2026 and the inference buildout kicks in, the architectural shift will be visible in semiconductor revenue mix (ASICs growing 200%+ YoY while NVIDIA inference GPU revenue declines). That's the moment the market reprices the entire AI infrastructure stack. 12-18 months. > > So.. short NVDA, long AVGO/MRVL, TSM as a hedge, deep OTM puts on hyperscaler indices, and go long the AI application layer (CRM, NOW, MSFT **Verdict:** 40+ paragraphs. Full bear thesis with timing signals ("watch Q3-Q4 2026 capex guidance"), specific trade structures ("short $NVDA, long $AVGO/$MRVL"), Cisco 2000 counter-analogy explaining why this is different. Provocative destination → maximum bridge density. This is the test that proves the pattern — sharp conclusions pull sharp reasoning out of FIM. --- ### Loose Test 4: Open-Ended — "And that changes everything" **Conclusion:** And that changes everything. **FIM Bridge:** > The die was cast when inference became 2/3rds of all AI compute. Training grabbed the headlines, but inference is the factory floor. That's the bedrock everything must be re-examined from. > > CoT turned out a stepwise reasoning tax. Every "wait, let me think" in a model's inner monologue multiplies tokens by 10-100x. The AI doesn't just answer — it verifies, backtracks, explores alternatives, checks its work. This isn't a one-time overhead; it's the new default for any non-trivial query. The 57 quadrillion token number for 2026 tells you the scale. Dell's roadmap saw a steady climb; reality is a vertical wall. > > Jevons didn't just visit — he moved in. GPU cost-per-token collapsed from $20 to $0.40 per million tokens. The rational response? Use more of them. Much more. The efficiencies of FP4, of quantization-aware training, of Muon replacing AdamW — none of them reduced total spend. They freed up budget to run more inference, to deploy more agents, to add more reasoning steps. The $600-700B capex commitment from hyperscalers isn't optimism; it's arithmetic. > > Proprietary models held a brief advantage. Llama 3.1 changed the math. At 30x cheaper and 80-85% of the capability, you don't need to be better. The economic gravity pulls inference toward open, commoditized models. DeepSeek V4 at MIT license with Ascend hardware that runs CUDA code through a translation shim isn't just cheaper — it undermines the export control regime. > > When inference is 24/7 agent-driven and not just human query-driven, the custom silicon case becomes overwhelming. Inferentia, TPU, Maia — these aren't side projects. They're the main event. NVIDIA knows it. The Groq acquisition at $20B reads differently when you realize inference is the larger and faster-growing market. You don't pay that premium unless you're buying time. > > CPO is no longer optional. The scale-up network connecting GPUs within a pod handles 9x the bandwidth of the scale-out network connecting pods. When advanced reasoning chains require tight coupling of thousands of accelerators, optical interconnects become the bottleneck. Feynman chips in 2028-2029 will ship with co-packaged optics because the alternative is a bandwidth ceiling that makes CoT inference economically unviable. > > The inference stack is reorganizing. Managed inference providers sit at 45% margins, abstracting the silicon wars beneath a simple API. The value isn't in the hardware or even the model — it's in the orchestration layer that keeps agent workloads running while optimizing across whatever accelerators are cheapest at 3 AM. **Verdict:** Essay-quality prose. The CPO paragraph connecting CoT inference volume → optical interconnect necessity is a cross-theme synthesis that existed in the raw data but hadn't been stated that cleanly. FIM with a minimal anchor still works — it just produces narrative instead of argument. The bridge reads more like an essay than a logical chain. --- ### Loose Test 5: Cross-Theme Bridge — Inference + China Export Controls **Additional premise context added:** - DeepSeek is Chinese, likely training on Huawei Ascend chips under US export controls - They achieve frontier performance at fraction of Western compute budgets - "95% CUDA compatibility" means they built a translation layer — CUDA is the target API - China's AI labs are training with ~1/10th the compute budget of US labs - US just tightened export controls on advanced chips to China again **Conclusion:** US export controls on AI chips are accelerating exactly the outcome they were designed to prevent. **FIM Bridge:** > I'll apply the INFERENCE/TOKEN growth rates vs the COST decline rate to project actual unit demand for compute infrastructure. > > ### GROWTH RATE ANALYSIS > > | Metric | Rate | Timeframe | > |--------|------|-----------| > | Inference demand growth | 35% CAGR | 2024-2028 | > | Training demand growth | 22% CAGR | 2024-2028 | > | Token volume CAGR (realized) | 57x Dell forecast | Current | > | Token cost decline | 50x over 3 years | ~70% annual decline | > | Managed inference margins | 45% gross | Current | > > ### UNIT COMPUTE DEMAND MODEL (2026-2028) > > Assume base = 100 units of compute in 2025 > > - **2026**: 100 × 1.35 = 135 (organic) × 3.0 (agent multiplier) = **405 units** > - **2027**: 405 × 1.35 = 547 × 5.0 (agent maturity) = **2,734 units** > - **2028**: 2,734 × 1.35 = 3,691 × 6.5 (full deployment) = **23,989 units** > - **2029**: 23,989 × 1.35 = 32,385 × 8.0 (ubiquitous) = **259,000 units** > > But tokens get cheaper: -70%/year > > - **2026 effective**: 405 × 0.30 = **122 net units** > - **2027 effective**: 2,734 × 0.09 = **246 net units** > - **2028 effective**: 23,989 × 0.027 = **648 net units** > - **2029 effective**: 259,000 × 0.0081 = **2,098 net units** > > ### HARDWARE MIX SHIFT > > | Year | NVIDIA Training | NVIDIA Inference | Custom Silicon Inference | > |------|----------------|------------------|--------------------------| > | 2025 | 85% | 80% | 20% | > | 2026 | 80% | 65% | 35% | > | 2027 | 75% | 50% | 50% | > | 2028 | 70% | 40% | 60% | > > ### NET-NET PROJECTION > > The industry goes through a **trough in 2026** as price deflation outruns volume growth, but **re-accelerates in 2027-2028** as agent adoption compounds demand faster than efficiency gains can offset. By 2029, AI infrastructure spending hits **3.4x 2025 levels** (~$840B) with NVIDIA's revenue share declining to ~50% but absolute revenue still 2.2x higher than 2025... **Verdict:** Built a full unprompted financial model with unit demand projections, hardware mix shifts, and year-by-year revenue forecasts. So dense it ate the entire 1024 token budget before actually connecting to the export controls conclusion. The bridge is a complete quantitative analysis that never reaches its destination — itself a signal that the cross-theme connection requires more reasoning steps than the token budget allows. The "2026 trough" prediction is a testable claim. --- ## Meta-Observations 1. **Bridge density correlates with inferential accessibility.** Strong conclusions from the premise → dense bridges. Weak/tangential conclusions → thin bridges. This is the core finding. 2. **Provocative conclusions pull harder than bland ones.** Test 3 (contrarian bear) produced 10-20x more content than Test 1 (vague bull). FIM's output quality scales with how much work the bridge needs to do. 3. **Equal-density bull/bear bridges = underdetermined premise.** When FIM builds equally compelling bridges to opposite conclusions, your evidence doesn't uniquely support your position. 4. **Open-ended conclusions produce essays, not arguments.** Test 4 ("changes everything") generated beautiful prose but not a logical chain. FIM needs a sharp destination to produce sharp reasoning. 5. **Cross-theme bridges can exceed token budgets.** Test 5 built so much intermediate analysis it never reached the conclusion. For complex cross-theme connections, increase `max_tokens` or break into staged bridges. 6. **FIM confabulates — but the confabulation quality is the signal.** It will always produce *something*. The question isn't "did it bridge?" but "how thick is the bridge?" Thin bridges are the rationalization detector.