Speelman makes a straightforward case. Humans analyzing chess positions miss things. Computers find them. Together, they're unbeatable.
He uses Svidler versus Lobron from the 1996 Yerevan Olympiad as his opening example, mining a complex middlegame for hidden defensive resources that human analysis had overlooked. The position demanded precise calculation, the kind that engines now execute instantly but strong players once needed hours to verify.
The article pulls recent examples from the Grand Chess Tour in Zagreb to show this principle in action. Zugzwang positions that looked winning for one side revealed themselves differently under computer scrutiny. Tablebase endgames produced findings that contradicted conventional wisdom.
Speelman frames the practical takeaway clearly. Computer-assisted study isn't about trusting the engine blindly. It's about using silicon to catch the moves humans dismiss as irrelevant, the quiet defensive tries that turn lost positions into drawn ones, the tactical resources hidden in plain sight.
He peppers the piece with calculation challenges for readers, forcing you to work before the computer answer arrives. This drives home his real point. Strong analysis demands both human understanding and mechanical precision. The engine sees what we miss. We understand why it matters.
Two legs. Four legs. Together they walk further than either alone.