diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index 35df3859324ae..7d48333a50c4b 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -271,7 +271,7 @@ def randomized_svd(
     power_iteration_normalizer="auto",
     transpose="auto",
     flip_sign=True,
-    random_state="warn",
+    random_state=None,
     svd_lapack_driver="gesdd",
 ):
     """Compute a truncated randomized SVD.
@@ -344,10 +344,7 @@ def randomized_svd(
         function calls. See :term:`Glossary <random_state>`.
 
         .. versionchanged:: 1.2
-            The previous behavior (`random_state=0`) is deprecated, and
-            from v1.2 the default value will be `random_state=None`. Set
-            the value of `random_state` explicitly to suppress the deprecation
-            warning.
+            The default value changed from 0 to None.
 
     svd_lapack_driver : {"gesdd", "gesvd"}, default="gesdd"
         Whether to use the more efficient divide-and-conquer approach
@@ -409,19 +406,6 @@ def randomized_svd(
             sparse.SparseEfficiencyWarning,
         )
 
-    if random_state == "warn":
-        warnings.warn(
-            "If 'random_state' is not supplied, the current default "
-            "is to use 0 as a fixed seed. This will change to  "
-            "None in version 1.2 leading to non-deterministic results "
-            "that better reflect nature of the randomized_svd solver. "
-            "If you want to silence this warning, set 'random_state' "
-            "to an integer seed or to None explicitly depending "
-            "if you want your code to be deterministic or not.",
-            FutureWarning,
-        )
-        random_state = 0
-
     random_state = check_random_state(random_state)
     n_random = n_components + n_oversamples
     n_samples, n_features = M.shape