diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 610a39a05148b..08de9002948ae 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1764,6 +1764,9 @@ def get_values(self, dtype=None):
         return values
 
     def to_dense(self):
+        if self.dtype.kind == "O":
+            # See https://github.com/numpy/numpy/issues/15041
+            return np.asarray(self.values, dtype=object)
         return np.asarray(self.values)
 
     def to_native_types(self, slicer=None, na_rep="nan", quoting=None, **kwargs):
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 109df6584641d..8c1aaf8f7706c 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1485,6 +1485,11 @@ def _format_strings(self) -> List[str]:
         if is_categorical_dtype(values.dtype):
             # Categorical is special for now, so that we can preserve tzinfo
             array = values._internal_get_values()
+        elif values.dtype.kind == "O":
+            # numpy>=1.18 wants object dtype passed explicitly
+            # Note: dtype.kind check works for json extension tests, while
+            #  dtype == object check does not.
+            array = np.asarray(values, dtype=object)
         else:
             array = np.asarray(values)
 
diff --git a/pandas/tests/extension/base/interface.py b/pandas/tests/extension/base/interface.py
index a29f6deeffae6..b20ef96fa42f5 100644
--- a/pandas/tests/extension/base/interface.py
+++ b/pandas/tests/extension/base/interface.py
@@ -32,7 +32,11 @@ def test_memory_usage(self, data):
         assert result == s.nbytes
 
     def test_array_interface(self, data):
-        result = np.array(data)
+        if hasattr(data, "dtype") and data.dtype.kind == "O":
+            # e.g. JSONArray
+            result = np.array(data, dtype=object)
+        else:
+            result = np.array(data)
         assert result[0] == data[0]
 
         result = np.array(data, dtype=object)
diff --git a/pandas/tests/extension/test_numpy.py b/pandas/tests/extension/test_numpy.py
index 221cf0787d839..beb3fc80eccd6 100644
--- a/pandas/tests/extension/test_numpy.py
+++ b/pandas/tests/extension/test_numpy.py
@@ -51,7 +51,7 @@ def data_missing(allow_in_pandas, dtype):
     if dtype.numpy_dtype == "object":
         if _np_version_under1p16:
             raise pytest.skip("Skipping for NumPy <1.16")
-        return PandasArray(np.array([np.nan, (1,)]))
+        return PandasArray(np.array([np.nan, (1,)], dtype=object))
     return PandasArray(np.array([np.nan, 1.0]))
 
 
@@ -78,7 +78,7 @@ def data_for_sorting(allow_in_pandas, dtype):
     if dtype.numpy_dtype == "object":
         # Use an empty tuple for first element, then remove,
         # to disable np.array's shape inference.
-        return PandasArray(np.array([(), (2,), (3,), (1,)])[1:])
+        return PandasArray(np.array([(), (2,), (3,), (1,)], dtype=object)[1:])
     return PandasArray(np.array([1, 2, 0]))
 
 
@@ -90,7 +90,7 @@ def data_missing_for_sorting(allow_in_pandas, dtype):
     A < B and NA missing.
     """
     if dtype.numpy_dtype == "object":
-        return PandasArray(np.array([(1,), np.nan, (0,)]))
+        return PandasArray(np.array([(1,), np.nan, (0,)], dtype=object))
     return PandasArray(np.array([1, np.nan, 0]))
 
 
@@ -106,7 +106,9 @@ def data_for_grouping(allow_in_pandas, dtype):
         a, b, c = (1,), (2,), (3,)
     else:
         a, b, c = np.arange(3)
-    return PandasArray(np.array([b, b, np.nan, np.nan, a, a, b, c]))
+    return PandasArray(
+        np.array([b, b, np.nan, np.nan, a, a, b, c], dtype=dtype.numpy_dtype)
+    )
 
 
 @pytest.fixture