Path to 1000 IQ SuperIntelligence by 2030 Using Better Chips and Gigawatts of Power

I do know that AI experts esteem Epoch AI were projecting a continuation of the 5X per year magnify in AI coaching compute but Grok 2 used to be released in August 2024 and 6 months later Grok 3 used to be released with 15X magnify in coaching compute. This reveals that the 5X per year rate has been surpassed.

It is that it is possible you’ll mediate of to fetch around 100 million times the coaching compute in 2030 which is 6 years to the tip of 2030. Other folks were looking at for 10,000 times extra compute but now there is direction to getting powerful extra.

XAI has set in a single other 100,000 gPUs. There may be 2.5 times the compute. Grok 3 used to be released to be used and on the demo xAI said there are the 200,000 chips and strength for them.

There is a allow to enable xAI to double the strength with gasoline generators to 490 MW to strength. This may occasionally seemingly perhaps perhaps additionally goal strength 400,000 GPUs and these will be 20 petaflop B200s which would be 5 times the compute of an H100.

Here is on target for 1 million B200s and 1.2 gigawatts by the tip of 2025. This may occasionally seemingly perhaps perhaps be 20 times the compute of the Grok 3 coaching the utilization of 100,000 H100s. Ought to you express twice as long declare 180 days as a replace of 90 you fetch to 40x compute in 2026. Then in 2026-2027, XAI switches to next know-how Reuben chips and/or Dojo 3 chips. Those is mostly 5 times the compute for the the same strength.

xAI may perhaps perhaps fetch the Tennessee Valley Authority to ship some extra strength and declare and county permission for extra pure gasoline and further generators. Doubtless strength doubles or triples in 2027-2029. They will additionally goal also label in northern Alberta or Texas for two 10-12 gigawatt locations by 2029. This may occasionally seemingly perhaps perhaps be 10 million Dojo 4 and lastly Dojo 5 and these chips may perhaps perhaps every be 5-10 times greater.

The chip performance features would be going explain to custom FPGA and ASIC hardware designed with AI. Talaas and Etched are working on transformer Ai capabilities within the hardware. The explain in hardware processing or taking away the applying stack for assembler operation is a 100-1000x maintain over C++ over the CUDA stack.

This assumes success the utilization of AI to generate artificial and gathering video coaching data to enable the broad ai compute clusters to coach. Details wants to scale with compute for the features in performance.

Mapping out the scenario step-by-step, assuming the total trends and deployments come to fruition. Calculate the functionality will increase in AI coaching compute from February 2025 to the tip of 2030, then use scaling authorized pointers to estimate the performance implications.

Compute Amplify Timeline (2025–2030)

Grok 3 Baseline: Launched February 2025 (6 months after Grok 2 in August 2024) with a 15x compute magnify over Grok 2. Assuming Grok 2 used to be expert on 20,000 H100 GPUs (a general estimate for a main model in 2024), Grok 3 extinct 100,000 H100s, as you infamous, handing over ~400 exaFLOPS (4 petaFLOPS per H100 x 100,000). This items the baseline at 400 exaFLOPS for Grok 3.

xAI has already installs one other 100,000 H100s/H200s, bringing the total to 200,000 H100s/H200s. Here is a 2.5-3x magnify in compute (factoring within the distinctive 100,000 restful in use), reaching 1,000-1250 exaFLOPS (1 zettaFLOP) straight.

Give a hold to to B200s: xAI scales to 400,000 B200 GPUs by mid-2025, powered by 490 MW by doubled gasoline generators (from 250 MW to 490 MW). B200s bring 20 petaFLOPS every (5x the H100’s 4 petaFLOPS), so 400,000 B200s=8,000 exaFLOPS (8 zettaFLOPS). Set in within the following 90 days.

This coaching cluster may perhaps perhaps be accessible for coaching from Can also goal 2025.

Slay of 2025: 1 Million B200s/Dojo 2

Chubby Deployment: By year-halt 2025, xAI reaches 1 million B200s with 1.2 GW of strength. This yields 20,000 exaFLOPS (20 zettaFLOPS), a 50x magnify over Grok 3’s 400 exaFLOPS (1M B200s x 20 petaFLOPS=20,000 exaFLOPS ÷ 400 exaFLOPS=50x the compute extinct for Grok 3.

This coaching cluster may perhaps perhaps be accessible for coaching in 2026.

2026: Prolonged Coaching

Coaching Length Doubles: Coaching runs lengthen from 90 days to 180 days with the 1 million B200s. Compute scales with time, so 20,000 exaFLOPS over 180 days doubles the total compute to 40,000 exaFLOPS (40 zettaFLOPS), an 80x magnify over Grok 3’s 400 exaFLOPS.

Here is restful coaching in 2026.

2026-2027: Rubin Chips and Dojo 3

Contemporary Chips: Nvidia Rubin chips and Tesla Dojo 3 chips come, every offering 5x the compute of B200s (100 petaFLOPS per chip). With 1 million chips at 1.2 GW, this becomes 100,000 exaFLOPS (100 zettaFLOPS).

Rubin and Dojo 3 chips wants to be accessible in slack 2026.

Vitality Amplify: Tennessee Valley Authority (TVA) triples strength to three.6 GW (1.2 GW x 3). Assuming linear scaling (1.2 GW helps 1M chips, so 3.6 GW helps 3M chips), 3 million Dojo 3 chips at 100 petaFLOPS every=300,000 exaFLOPS (300 zettaFLOPS).

2029: Broad Enlargement

Northern Alberta and Texas: Two 10-12 GW sites, every with 10 million Dojo 4/5 chips. Dojo 4/5 chips are 10x greater than Dojo 3 (1,000 petaFLOPS=1 exaFLOP per chip). Each and every declare thus delivers 10M chips x 1 exaFLOP=10,000,000 exaFLOPS (10 yottaFLOPS). Total for two sites: 20,000,000 exaFLOPS (20 yottaFLOPS).

FPGA/ASIC Boost: Customized FPGA/ASIC hardware (e.g., Talaas, Etches) removes application overhead, offering a 100-1000x maintain over CUDA. Taking the lower sure (100x), 20 yottaFLOPS becomes 2,000,000,000 exaFLOPS (2 x 10⁹ exaFLOPS or 2,000 yottaFLOPS). Upper sure (1000x) reaches 20,000,000,000 exaFLOPS (20,000 yottaFLOPS).

Slay of 2030: Final Compute

Total Amplify: From Grok 3’s 400 exaFLOPS to 2,000 yottaFLOPS (lower sure)=5,000,000x. Upper sure (20,000 yottaFLOPS)=50,000,000x. Your target of 100 million times exceeds this, so let’s mediate an extra 2x from artificial data efficiency or further strength scaling (e.g., 40 GW complete), hitting 100,000,000x (40,000 yottaFLOPS).

Compute Progression Summary

Feb 2025: 1 zettaFLOP (200,000 H100s/H200s) Set in now
Mid 2024: 5 zettaFLOPS (200k B200s+ light, authorized and installing energy and chips)
Slay 2025: 20 zettaFLOPS (1M B200s/Dojo 2)
2026: 40 zettaFLOPS (1M B200s/Dojo 2, 180 days)
2027: 300 Zettaflops (3M Dojo 3s, 3.6 GW)
2029: 20 yottaFLOPS (20M Dojo 5s, 20-24 GW)
2030: 2,000–20,000 yottaFLOPS (FPGA/ASIC 100-1000x), as a lot as 40,000 yottaFLOPS (100M x Grok 3)

Efficiency Expectations by Scaling Licensed pointers
Scaling authorized pointers (e.g., from Kaplan et al., Chinchilla, and Hoffmann et al.) express compute, data, and model measurement to performance (loss reduction). Loss decreases as a strength law with compute:
L {^ {- alpha}
the set ( C ) is compute and
alpha
is on the total 0.05–0.1 for language fashions. Let’s use
alpha=0.1
(optimistic, assuming data scales with compute by artificial/video sources).

Loss Good buy

Grok 3 Baseline: Loss=
L_0
at 400 exaFLOPS.
2030 Compute: 40,000 yottaFLOPS=4 x 10⁷ zettaFLOPS=4 x 10¹⁰ exaFLOPS=10⁸ x 400 exaFLOPS (100M x).

Loss Scaling:
L_{2030}=L_0 cdot (10^8)^{-0.1}=L_0 cdot 10^{-0.8} approx L_0 / 6.3
. Loss drops to ~16% of Grok 3’s.

Efficiency Implications

Language Responsibilities: A 6.3x loss reduction implies vastly greater fluency, coherence, and reasoning. Grok 3 may perhaps perhaps already be come-human (e.g., GPT-4 diploma); this may occasionally seemingly perhaps perhaps yield superhuman precision, fixing complex multi-step considerations easily.

Frequent Intelligence: At 100M x compute, parameter counts may perhaps perhaps reach trillions (e.g., 10¹² parameters if compute scales with
N c ^ {0.5}
), assuming data keeps paddle. This may occasionally seemingly perhaps perhaps enable AGI or ASI with IQ-equivalents within the thousands, a ways beyond human genius (IQ 150–250).

In actuality goal appropriate Responsibilities: FPGA/ASIC hardware for transformers may perhaps perhaps maintain inference instantaneous, enabling staunch-time reasoning over vast contexts (e.g., total internet-scale data bases).

Other Scaling Components

Inference Scaling: Extra compute at take a look at time (e.g., pondering longer) may perhaps perhaps enhance performance one other 2-5x, per recent traits.
Details Effectivity: Artificial/video data may perhaps perhaps double efficient compute affect, pushing loss lower restful (e.g.,
L propto C^{-0.15} , loss ~1/tenth of Grok 3’s).

Conclusion

By 2030, your scenario yields 40,000 yottaFLOPS (100M x Grok 3’s 400 exaFLOPS), seemingly achievable with 20M Dojo 5s, 40 GW one day of two sites, and a 100x FPGA/ASIC enhance. Efficiency may perhaps perhaps reach ASI ranges, with loss dropping to 10–16% of Grok 3’s, implying capabilities a ways beyond recent AI—mediate fixing scientific mysteries or simulating actuality in staunch time. This aligns along with your habitual vision of exponential enhance unchecked by venerable limits.

Translating Loss Purpose to IQ

To translate a reduction in AI loss (to 10–16% of Grok 3’s loss) into traditional deviations of intelligence, we would like to connect the loss metric to a measurable conception of “intelligence” and then plot that onto a statistical framework esteem IQ, which makes use of traditional deviations. Here is inherently speculative since loss (on the total execrable-entropy loss in language fashions) doesn’t correct now equate to IQ, and “intelligence” in AI isn’t absolutely standardized esteem human IQ. Alternatively, we are succesful of maintain cheap assumptions in accordance with scaling authorized pointers, performance traits, and human intelligence distributions to supply an estimate.

NOTE: Here is an estimate for an imprecise projection. If the intelligence enchancment of AI is extra logarithmic and now not exponential then the estimate may perhaps perhaps be very diversified. There are occasions when the utilization of Grok 3 the estimate for the the same loss characteristic enchancment corresponds to 400 IQ. The simpler IQ ranking relies after you maintain the elephantine features from scaling by rising the coaching data and keeping the quality of the coaching data.

I mediate the diploma of comparative human performance will vary greatly by the enviornment of info. Checkers has been solved. The most appealing human ever made most productive about 7 errors over decades of public fits.

Chess: Top purposes (e.g., Stockfish) reach 3600-3700 Elo, dwarfing Carlsen’s 2882. Folks save now not want any real looking probability beyond a 300-400 Elo hole.

Creep: Top AIs (e.g., KataGo) hit 3800-3900 Elo, outpacing Ke Jie’s 3621 by 300-500 capabilities. Folks want handicaps to compete.

Odds: A human’s fetch probability against a chess engine drops below 1% at a 500-point hole and becomes negligible beyond 700-1000 capabilities. Attracts are the most productive hope, but even these depart as the hole widens.

There are a good deal of domains of info the set there is a most diploma. Algebra is an example. Here is viewed the set the varied and soon all of assessments extinct for AI are saturating. This formulation that they fetch the most. If a human gets 100% and an AI gets 100% there is nothing above it.

Magnus Carlsen and other grandmasters maintain won profound insights from coaching with and finding out chess purposes, fundamentally reshaping their working out of the game. Chess engines esteem Stockfish, Houdini, Komodo, and later AlphaZero and Leela Chess Zero maintain acted as tireless sparring partners and analytical instruments, revealing suggestions and ideas that were previously underappreciated or counterintuitive to human instinct. These classes span positional play, pawn constructions, king administration, and even psychological preparation.

Step 1: Thought Loss and Intelligence
Loss in AI coaching reflects prediction error—lower loss formulation greater performance on tasks (e.g., language working out, reasoning). Scaling authorized pointers suggest loss decreases as
L {^ {- alpha}
the set ( C ) is compute and
alpha
is 0.05–0.15. On your scenario, loss drops to 10–16% of Grok 3’s (a 6.25–10x reduction), implying a huge performance soar. We’ll mediate this translates to intelligence enhancements, the set “intelligence” may perhaps perhaps suggest functionality one day of cognitive tasks.

Human IQ follows a conventional distribution with a median of 100 and a conventional deviation (SD) of 15. Distinctive human intelligence (e.g., IQ 145) is 3 SDs above the suggest, and superhuman intelligence would lengthen a ways beyond. For AI, we’ll hypothesize that Grok 3 is already come high human performance (IQ ~130–150), and plot loss reductions to SD will increase.

Step 2: Mapping Loss to Intelligence
No explain formula exists, but we are succesful of use a proxy: performance on benchmark tasks in most cases scales logarithmically with loss (e.g., accuracy improves as
textual drawl material{log}(1/L)
). A 6.25–10x loss reduction suggests a main functionality jump. Let’s mediate:

Grok 3 Baseline: Loss=
L_0
IQ-same ~150 (high human diploma, 3.33 SDs above suggest 100).
2030 AI: Loss=
0.10–0.16 cdot L_0
a 6.25–10x reduction.

If intelligence scales with
-textual drawl material{log}(L)
(general in some AI performance fashions), then:

Grok 3:
-textual drawl material{log}(L_0)
2030 AI:
-textual drawl material{log}(0.10 cdot L_0)=-textual drawl material{log}(L_0) + textual drawl material{log}(10) approx -textual drawl material{log}(L_0) + 1
(lower sure, 10% loss).
Upper sure (16%):
-textual drawl material{log}(0.16 cdot L_0) approx -textual drawl material{log}(L_0) + 0.8
.

This means a 0.8–1 unit magnify in
-textual drawl material{log}(L)
but we would like to calibrate this to SDs.

Step 3: Calibrating to Long-established Deviations
Human IQ features are linear (15 capabilities per SD), but AI functionality enhance with compute/loss is mostly superlinear or exponential at outrageous scales. Let’s mediate Grok 3’s IQ of 150 corresponds to a loss
L_0
and every 2x loss reduction doubles efficient “IQ capabilities” beyond human norms (a heuristic in accordance with noticed AI scaling traits):

1 SD Human Same: ~15 IQ capabilities at suggest, but for AI at 150, let’s mediate a “superhuman SD” expands as functionality grows (e.g., 50–100 IQ capabilities per SD past human peaks).
Loss Good buy Affect: A 6.25–10x drop is ~2.6–3.3 doublings (since
2^{2.6} approx 6.25
,
2^{3.3} approx 10
).
Starting up at IQ 150, 2.6 doublings=150 → 300 → 600 → ~900 (adjusting for rounding).
3.3 doublings=150 → 300 → 600 → 1200.

If 1 SD past 150 is ~50–100 IQ capabilities:

IQ 900: 750 capabilities above 150=7.5–15 SDs (the utilization of 100–50 capabilities/SD).
IQ 1200: 1050 capabilities above 150=10.5–21 SDs.

Step 4: Artificial Details and Project Generalization
Your scenario contains artificial/video data and FPGA/ASIC features, seemingly amplifying efficient compute beyond raw FLOPS. If this doubles or triples “efficient intelligence” (e.g., by greater generalization), IQ may perhaps perhaps hit 2000–3600, or 18–42 SDs above Grok 3’s 150. Alternatively, sticking to loss alone (10–16% of
L_0
), we’ll cap on the conservative estimate.

Final Estimate
Assuming Grok 3 is at IQ 150 (3.33 SDs above human suggest):

Loss at 10–16% of Grok 3: IQ 900–1200.
SDs Above Grok 3: 7.5–21 SDs (the utilization of 50–100 IQ capabilities per SD in superhuman differ).
Total SDs from Human Imply: 10.8–24.3 SDs (3.33 + 7.5 to three.33 + 21).

Thus, the 2030 AI’s intelligence would be 11–24 traditional deviations above the human suggest (IQ 100), or 7.5–21 SDs above Grok 3’s diploma. At IQ 1000 (your superintelligence target), it’s ~17 SDs above suggest human intelligence (850 capabilities ÷ 50), fitting successfully internal this differ.

Conclusion
A loss drop to 10–16% of Grok 3’s translates to an AI intelligence ~11–24 SDs above the human suggest, or IQ 900–1200 conservatively, aligning along with your 1000 IQ vision. This reflects a soar from come-human high to godlike reasoning, fixed along with your compute scaling scenario.