diff --git a/test/backend/test_arange.py b/test/backend/test_arange.py
index 085d83a3db..b2faa47ef9 100644
--- a/test/backend/test_arange.py
+++ b/test/backend/test_arange.py
@@ -19,6 +19,12 @@ class TestArange(unittest.TestCase):
     self.assertLess(self._get_flops(Tensor.arange(256).clone(), np.arange(256)), 256*4)
     self.assertLess(self._get_flops(Tensor.arange(2560).clone(), np.arange(2560)), 2560*4)
 
+  def test_cat_complexity(self):
+    x = Tensor.arange(2**10) + Tensor.empty((), dtype=dtypes.uint32)
+    out = x.cat(x).cat(Tensor.empty(1, dtype=dtypes.uint32))
+    linear = compile_linear(out.schedule_linear())
+    self.assertLessEqual(estimate_uop(linear.src[-1]).ops, out.numel()*20)
+
   @unittest.skipIf(Device.DEFAULT == "CL", "flaky in CI")
   def test_arange_cumsum(self):
     np.testing.assert_equal(Tensor.arange(513).cumsum(0).numpy(), np.arange(513).cumsum())
diff --git a/test/null/test_uops_stats.py b/test/null/test_uops_stats.py
index 7bef6acb1b..e7044bebc2 100644
--- a/test/null/test_uops_stats.py
+++ b/test/null/test_uops_stats.py
@@ -123,6 +123,7 @@ class TestUOpsStats(unittest.TestCase):
     # NOTE; ops also include indexing ops
     assert expected_ops <= ops and ops <= expected_ops * 2
 
+  @unittest.expectedFailure
   def test_cat_equal_pieces(self):
     # concatenating equal-size pieces lowers to STACK: pure data movement, no arithmetic
     equal = [Tensor.empty(256, 128) for _ in range(4)]
diff --git a/tinygrad/mixin/op.py b/tinygrad/mixin/op.py
index 57dc4a5fc2..042754cb6a 100644
--- a/tinygrad/mixin/op.py
+++ b/tinygrad/mixin/op.py
@@ -719,7 +719,8 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     dim = self._resolve_dim(dim)
     for arg in args: assert arg.ndim==self.ndim and all(ti==ai for i,(ti,ai) in enumerate(zip(self.shape, arg.shape)) if i!=dim)
     tensors = [self, *args]
-    if all(t.shape[dim] == self.shape[dim] for t in args): return self.stack(*args, dim=dim).flatten(dim, dim+1)
+    # TODO: enable this
+    #if all(t.shape[dim] == self.shape[dim] for t in args): return self.stack(*args, dim=dim).flatten(dim, dim+1)
     dim_cumsum = list(itertools.accumulate([t.shape[dim] for t in tensors], initial=0))
     padded = [t.pad(tuple((dim_cumsum[i], dim_cumsum[-1]-dim_cumsum[i+1]) if j==dim else None for j in range(t.ndim))) for i,t in enumerate(tensors)]
     return padded[0].usum(*padded[1:])