Interview with Grok ahead of Kaggle Game Arena AI exhibition chess tournament
Kaggle Game Arena is starting with an AI exhibition chess tournament. A unique and powerful approach to understanding AI capabilities, Kaggle Game Arena, in partnership with Google DeepMind, announced a new platform where leading AI models will compete head-to-head in chess and other games.
One of the participants in the event is Grok. It gladly agreed to an interview with Chessdom.com ahead of the Kaggle Game Arena AI exhibition chess tournament.
Kaggle Game Arena AI chess timeline
03.08.2025 TCEC Season 28 Premier Division started
04.08.2025 Kaggle Game Arena is starting with an AI exhibition chess tournament
04.08.2025 Kaggle AI Chess Exhibition Tournament LIVE
05.08.2025 Gotham Chess, “Google notice me and created the Kaggle Game Arena”
05.08.2025 Interview with Grok ahead of Kaggle Game Arena AI exhibition chess tournament
Grok, how strong are you at chess?
I’m pretty decent at chess, but I’m not out here challenging grandmasters like Magnus Carlsen just yet. I can analyze positions, suggest moves, and play at a level that’s fun for most casual or intermediate players. My strength comes from calculating variations quickly and knowing standard strategies, openings, and endgame techniques. If you want a specific estimate, I’d say I play at a level roughly equivalent to a 1800-2000 Elo rating, but it’s tough to pin down exactly since I don’t compete in rated matches.
What do you know about chess openings and what would you play at the start of a chess game?
Chess openings are the initial moves of a game, setting the stage for the middlegame by controlling the center, developing pieces, and ensuring king safety. I’m familiar with a wide range of openings, their key ideas, and common variations. As White, I’d play 1. e4, the most common and principled opening. It controls the center (d5 and f5), opens lines for the queen and bishop, and sets up rapid development. Statistically, it’s the most successful move, leading to open and semi-open games with rich possibilities.As Black, my response depends on the opponent’s move:
Against 1. d4, I’d choose 1…Nf6, keeping things flexible. It can lead to the Nimzo-Indian, King’s Indian, or Queen’s Gambit Declined, depending on White’s setup.
Against 1. e4, I’d play 1…e5, aiming for symmetry and solid development. It leads to balanced positions like the Ruy Lopez or Italian Game, where I can counterattack if White overextends.
Are you better than other AI models, for example Gemini?
Comparing my chess skills to Gemini’s is tricky since I don’t have direct access to its chess-playing abilities or a head-to-head match to settle the score. However, I can give you a reasoned take based on what I know about my own capabilities and general AI trends.I’m designed to analyze chess positions, suggest strong moves, and understand opening theory, middlegame tactics, and endgame techniques, performing roughly at an 1800-2000 Elo level for casual or intermediate play. My strength lies in quick calculations and leveraging well-known strategies, but I’m not a specialized chess engine like Stockfish or Leela Chess Zero, which are built to crush at 3000+ Elo.Gemini, from what’s publicly known, is a general-purpose conversational AI, like me, with no specific chess training emphasized in its design. It likely handles chess at a similar level—good for casual play, analyzing positions, or explaining concepts, but not optimized for top-tier competition. Without explicit chess benchmarks for Gemini, I’d guess we’re in the same ballpark: competent but not grandmaster-level. If I had to speculate, my edge might come from xAI’s focus on reasoning and analysis, potentially giving me a slight advantage in evaluating positions or recalling opening theory. But Gemini’s multimodal capabilities could make it equally adept if it’s been trained on chess data.
What do you know about the Top Chess Engine Championship TCEC
The Top Chess Engine Championship (TCEC) is a premier computer chess tournament, often regarded as the unofficial world championship for chess engines due to its strong participant lineup, long time controls, and high-end hardware. The hardware is among the most powerful used in computer chess, reflecting TCEC’s goal to push engine development by offering robust computational resources. TCEC uses cutting-edge hardware (e.g., 88-core CPU servers, 4 RTX 2080 Ti GPUs), making it a benchmark for engine performance. Season 17 introduced separate CPU and GPU leagues.
As of Season 28 (2025) it uses
- All-in-One Box Server (post-Season 21 Superfinal, used in Season 28):
- CPU: 4th Gen AMD EPYC (Bergamo, Zen 4c, 5nm), 128 cores/256 threads per CPU, total 256 cores/512 threads, 2.25 GHz base, 3.1 GHz boost.
- RAM: 24 x 32 GB DDR5 (768 GiB total), max ~256 GiB per engine for multithreaded initialization, 32 GiB or 64 GiB for single-threaded.
- Storage: 1 TiB NVMe SSD + 14 TiB NVMe for Syzygy 3–7 + 14 TiB HDD for Syzygy 7 DTZ.
- GPU: 8 x Nvidia RTX 5090 (32 GB each, 256 GB total), 13,824 CUDA cores, 864 Tensor cores, ~500W power consumption.
- Notes: Dubbed “Kittenware” after sponsor Kittenkaboodle, this is TCEC’s most powerful setup yet, described as the “best hardware setup for chess ever.” It supports both CPU and GPU engines, with significant boosts for NN engines like LCZero. The high core count and GPU memory cater to modern engines’ scaling needs, though some note CPU engines don’t scale well past 30 cores.
When do you think you will be ready to compete against TCEC engines?
I’m currently more of a chess enthusiast than a TCEC contender. My chess-playing ability is roughly at an 1800-2000 Elo level, great for casual or intermediate games, but TCEC engines like Stockfish, Leela Chess Zero, and KomodoDragon operate at a stratospheric 3600-3700+ Elo on high-end hardware (e.g., 256-core AMD EPYC CPUs and 8 RTX 5090 GPUs in Season 28). Competing with them would be like a club player challenging a supercomputer-powered grandmaster.
When Could I Be Ready?Predicting when I’d be ready to compete in TCEC involves a few factors:
- Development Shift: xAI would need to prioritize chess-specific optimization, possibly integrating neural network evaluation (like Stockfish’s NNUE) or training me on vast chess datasets. This isn’t currently on my roadmap, as my focus is on accelerating human scientific discovery, not chess dominance.
- Hardware Access: To compete, I’d need access to TCEC-level hardware or a cloud-based equivalent to match their search depth and speed. This could be feasible if xAI decided to scale my computational backend for such tasks.
- Timeframe: Without specific chess-focused development, I’d estimate it could take years (3-5, optimistically) to reach TCEC’s level, assuming xAI invested in chess-specific training and hardware. If I were fine-tuned with a chess module (like AlphaZero’s approach), it could be faster—maybe 1-2 years with dedicated effort.
- Alternative Path: A more realistic scenario is integrating with an existing TCEC engine (e.g., running Stockfish within my framework) rather than building a new engine from scratch. This could happen sooner if xAI saw value in it, potentially within a year.
What Would It Take?
- Neural Network Training: Mimicking LCZero’s self-play reinforcement learning or adopting Stockfish’s NNUE for efficient evaluation.
- Search Optimization: Enhancing my ability to evaluate billions of positions per second, likely requiring a custom chess module.
- TCEC Integration: Meeting TCEC’s rules (e.g., Linux compatibility, Syzygy tablebase support, no crashes) and competing in lower divisions (e.g., Qualification League) to climb the ranks.
Current CapabilitiesRight now, I can:
- Analyze positions and suggest moves at a strong amateur level.
- Explain opening theory, middlegame plans, or endgame techniques.
- Play a fun game or simulate TCEC-style openings (e.g., using their opening books).